1955 lines
96 KiB
Python
1955 lines
96 KiB
Python
import html
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import json
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import math
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import mimetypes
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import os
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import platform
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import random
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import subprocess as sp
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import sys
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import tempfile
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import time
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import traceback
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from functools import partial, reduce
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import warnings
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import gradio as gr
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import gradio.routes
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import gradio.utils
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import numpy as np
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from PIL import Image, PngImagePlugin
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from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
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from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae
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from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
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from modules.paths import script_path
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from modules.shared import opts, cmd_opts, restricted_opts
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import modules.codeformer_model
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import modules.generation_parameters_copypaste as parameters_copypaste
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import modules.gfpgan_model
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import modules.hypernetworks.ui
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import modules.scripts
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import modules.shared as shared
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import modules.styles
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import modules.textual_inversion.ui
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from modules import prompt_parser
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from modules.images import save_image
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from modules.sd_hijack import model_hijack
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from modules.sd_samplers import samplers, samplers_for_img2img
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from modules.textual_inversion import textual_inversion
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import modules.hypernetworks.ui
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from modules.generation_parameters_copypaste import image_from_url_text
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warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
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# 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
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mimetypes.init()
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mimetypes.add_type('application/javascript', '.js')
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if not cmd_opts.share and not cmd_opts.listen:
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# fix gradio phoning home
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gradio.utils.version_check = lambda: None
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gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
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if cmd_opts.ngrok is not None:
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import modules.ngrok as ngrok
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print('ngrok authtoken detected, trying to connect...')
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ngrok.connect(
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cmd_opts.ngrok,
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cmd_opts.port if cmd_opts.port is not None else 7860,
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cmd_opts.ngrok_region
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)
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def gr_show(visible=True):
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return {"visible": visible, "__type__": "update"}
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sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
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sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
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css_hide_progressbar = """
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.wrap .m-12 svg { display:none!important; }
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.wrap .m-12::before { content:"Loading..." }
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.wrap .z-20 svg { display:none!important; }
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.wrap .z-20::before { content:"Loading..." }
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.progress-bar { display:none!important; }
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.meta-text { display:none!important; }
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.meta-text-center { display:none!important; }
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"""
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# Using constants for these since the variation selector isn't visible.
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# Important that they exactly match script.js for tooltip to work.
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random_symbol = '\U0001f3b2\ufe0f' # 🎲️
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reuse_symbol = '\u267b\ufe0f' # ♻️
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paste_symbol = '\u2199\ufe0f' # ↙
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folder_symbol = '\U0001f4c2' # 📂
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refresh_symbol = '\U0001f504' # 🔄
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save_style_symbol = '\U0001f4be' # 💾
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apply_style_symbol = '\U0001f4cb' # 📋
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clear_prompt_symbol = '\U0001F5D1' # 🗑️
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def plaintext_to_html(text):
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text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
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return text
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def send_gradio_gallery_to_image(x):
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if len(x) == 0:
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return None
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return image_from_url_text(x[0])
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def save_files(js_data, images, do_make_zip, index):
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import csv
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filenames = []
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fullfns = []
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#quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
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class MyObject:
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def __init__(self, d=None):
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if d is not None:
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for key, value in d.items():
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setattr(self, key, value)
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data = json.loads(js_data)
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p = MyObject(data)
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path = opts.outdir_save
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save_to_dirs = opts.use_save_to_dirs_for_ui
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extension: str = opts.samples_format
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start_index = 0
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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
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images = [images[index]]
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start_index = index
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os.makedirs(opts.outdir_save, exist_ok=True)
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with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
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at_start = file.tell() == 0
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writer = csv.writer(file)
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if at_start:
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writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
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for image_index, filedata in enumerate(images, start_index):
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image = image_from_url_text(filedata)
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is_grid = image_index < p.index_of_first_image
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i = 0 if is_grid else (image_index - p.index_of_first_image)
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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)
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filename = os.path.relpath(fullfn, path)
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filenames.append(filename)
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fullfns.append(fullfn)
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if txt_fullfn:
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filenames.append(os.path.basename(txt_fullfn))
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fullfns.append(txt_fullfn)
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writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler_name"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
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# Make Zip
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if do_make_zip:
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zip_filepath = os.path.join(path, "images.zip")
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from zipfile import ZipFile
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with ZipFile(zip_filepath, "w") as zip_file:
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for i in range(len(fullfns)):
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with open(fullfns[i], mode="rb") as f:
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zip_file.writestr(filenames[i], f.read())
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fullfns.insert(0, zip_filepath)
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return gr.File.update(value=fullfns, visible=True), plaintext_to_html(f"Saved: {filenames[0]}")
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def visit(x, func, path=""):
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if hasattr(x, 'children'):
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for c in x.children:
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visit(c, func, path)
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elif x.label is not None:
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func(path + "/" + str(x.label), x)
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def add_style(name: str, prompt: str, negative_prompt: str):
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if name is None:
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return [gr_show() for x in range(4)]
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style = modules.styles.PromptStyle(name, prompt, negative_prompt)
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shared.prompt_styles.styles[style.name] = style
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# Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
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# reserialize all styles every time we save them
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shared.prompt_styles.save_styles(shared.styles_filename)
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return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(2)]
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def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
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from modules import processing, devices
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if not enable:
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return ""
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p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y)
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with devices.autocast():
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p.init([""], [0], [0])
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return f"resize: from <span class='resolution'>{p.width}x{p.height}</span> to <span class='resolution'>{p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}</span>"
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def apply_styles(prompt, prompt_neg, styles):
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prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles)
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prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles)
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return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value=[])]
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def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles):
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if mode in {0, 1, 3, 4}:
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return [interrogation_function(ii_singles[mode]), None]
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elif mode == 2:
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return [interrogation_function(ii_singles[mode]["image"]), None]
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elif mode == 5:
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assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
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images = shared.listfiles(ii_input_dir)
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print(f"Will process {len(images)} images.")
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if ii_output_dir != "":
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os.makedirs(ii_output_dir, exist_ok=True)
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else:
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ii_output_dir = ii_input_dir
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for image in images:
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img = Image.open(image)
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filename = os.path.basename(image)
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left, _ = os.path.splitext(filename)
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print(interrogation_function(img), file=open(os.path.join(ii_output_dir, left + ".txt"), 'a'))
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return [gr_show(True), None]
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def interrogate(image):
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prompt = shared.interrogator.interrogate(image.convert("RGB"))
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return gr_show(True) if prompt is None else prompt
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def interrogate_deepbooru(image):
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prompt = deepbooru.model.tag(image)
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return gr_show(True) if prompt is None else prompt
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def create_seed_inputs(target_interface):
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with FormRow(elem_id=target_interface + '_seed_row'):
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seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1, elem_id=target_interface + '_seed')
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seed.style(container=False)
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random_seed = gr.Button(random_symbol, elem_id=target_interface + '_random_seed')
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reuse_seed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_seed')
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with gr.Group(elem_id=target_interface + '_subseed_show_box'):
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seed_checkbox = gr.Checkbox(label='Extra', elem_id=target_interface + '_subseed_show', value=False)
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# Components to show/hide based on the 'Extra' checkbox
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seed_extras = []
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with FormRow(visible=False, elem_id=target_interface + '_subseed_row') as seed_extra_row_1:
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seed_extras.append(seed_extra_row_1)
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subseed = gr.Number(label='Variation seed', value=-1, elem_id=target_interface + '_subseed')
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subseed.style(container=False)
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random_subseed = gr.Button(random_symbol, elem_id=target_interface + '_random_subseed')
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reuse_subseed = gr.Button(reuse_symbol, elem_id=target_interface + '_reuse_subseed')
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subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01, elem_id=target_interface + '_subseed_strength')
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with FormRow(visible=False) as seed_extra_row_2:
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seed_extras.append(seed_extra_row_2)
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seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from width", value=0, elem_id=target_interface + '_seed_resize_from_w')
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seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize seed from height", value=0, elem_id=target_interface + '_seed_resize_from_h')
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random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
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random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
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def change_visibility(show):
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return {comp: gr_show(show) for comp in seed_extras}
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seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
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return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
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def connect_clear_prompt(button):
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"""Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
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button.click(
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_js="clear_prompt",
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fn=None,
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inputs=[],
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outputs=[],
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)
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def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
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""" Connects a 'reuse (sub)seed' button's click event so that it copies last used
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(sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
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was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
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def copy_seed(gen_info_string: str, index):
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res = -1
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try:
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gen_info = json.loads(gen_info_string)
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index -= gen_info.get('index_of_first_image', 0)
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if is_subseed and gen_info.get('subseed_strength', 0) > 0:
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all_subseeds = gen_info.get('all_subseeds', [-1])
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res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
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else:
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all_seeds = gen_info.get('all_seeds', [-1])
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res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
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except json.decoder.JSONDecodeError as e:
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if gen_info_string != '':
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print("Error parsing JSON generation info:", file=sys.stderr)
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print(gen_info_string, file=sys.stderr)
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return [res, gr_show(False)]
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reuse_seed.click(
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fn=copy_seed,
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_js="(x, y) => [x, selected_gallery_index()]",
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show_progress=False,
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inputs=[generation_info, dummy_component],
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outputs=[seed, dummy_component]
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)
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def update_token_counter(text, steps):
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try:
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_, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
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prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
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except Exception:
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# a parsing error can happen here during typing, and we don't want to bother the user with
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# messages related to it in console
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prompt_schedules = [[[steps, text]]]
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flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
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prompts = [prompt_text for step, prompt_text in flat_prompts]
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token_count, max_length = max([model_hijack.get_prompt_lengths(prompt) for prompt in prompts], key=lambda args: args[0])
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style_class = ' class="red"' if (token_count > max_length) else ""
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return f"<span {style_class}>{token_count}/{max_length}</span>"
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def create_toprow(is_img2img):
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id_part = "img2img" if is_img2img else "txt2img"
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with gr.Row(elem_id="toprow"):
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with gr.Column(scale=6):
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with gr.Row():
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with gr.Column(scale=80):
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with gr.Row():
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prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2,
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placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
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)
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with gr.Row():
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with gr.Column(scale=80):
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with gr.Row():
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negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2,
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placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
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)
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with gr.Column(scale=1, elem_id="roll_col"):
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paste = gr.Button(value=paste_symbol, elem_id="paste")
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save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
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prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
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clear_prompt_button = gr.Button(value=clear_prompt_symbol, elem_id=f"{id_part}_clear_prompt")
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token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
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token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
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clear_prompt_button.click(
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fn=lambda *x: x,
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_js="confirm_clear_prompt",
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inputs=[prompt, negative_prompt],
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outputs=[prompt, negative_prompt],
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)
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button_interrogate = None
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button_deepbooru = None
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if is_img2img:
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with gr.Column(scale=1, elem_id="interrogate_col"):
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button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
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button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
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with gr.Column(scale=1):
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with gr.Row(elem_id=f"{id_part}_generate_box"):
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interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
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skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
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submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
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skip.click(
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fn=lambda: shared.state.skip(),
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inputs=[],
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outputs=[],
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)
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interrupt.click(
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fn=lambda: shared.state.interrupt(),
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inputs=[],
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outputs=[],
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)
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with gr.Row():
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prompt_styles = gr.Dropdown(label="Styles", elem_id=f"{id_part}_styles", choices=[k for k, v in shared.prompt_styles.styles.items()], value=[], multiselect=True)
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create_refresh_button(prompt_styles, shared.prompt_styles.reload, lambda: {"choices": [k for k, v in shared.prompt_styles.styles.items()]}, f"refresh_{id_part}_styles")
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return prompt, prompt_styles, negative_prompt, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
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def setup_progressbar(*args, **kwargs):
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pass
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def apply_setting(key, value):
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if value is None:
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return gr.update()
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if shared.cmd_opts.freeze_settings:
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return gr.update()
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# dont allow model to be swapped when model hash exists in prompt
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if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
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return gr.update()
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if key == "sd_model_checkpoint":
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ckpt_info = sd_models.get_closet_checkpoint_match(value)
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if ckpt_info is not None:
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value = ckpt_info.title
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else:
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return gr.update()
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comp_args = opts.data_labels[key].component_args
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|
if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
|
|
return
|
|
|
|
valtype = type(opts.data_labels[key].default)
|
|
oldval = opts.data.get(key, None)
|
|
opts.data[key] = valtype(value) if valtype != type(None) else value
|
|
if oldval != value and opts.data_labels[key].onchange is not None:
|
|
opts.data_labels[key].onchange()
|
|
|
|
opts.save(shared.config_filename)
|
|
return value
|
|
|
|
|
|
def update_generation_info(generation_info, html_info, img_index):
|
|
try:
|
|
generation_info = json.loads(generation_info)
|
|
if img_index < 0 or img_index >= len(generation_info["infotexts"]):
|
|
return html_info, gr.update()
|
|
return plaintext_to_html(generation_info["infotexts"][img_index]), gr.update()
|
|
except Exception:
|
|
pass
|
|
# if the json parse or anything else fails, just return the old html_info
|
|
return html_info, gr.update()
|
|
|
|
|
|
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 = ToolButton(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])
|
|
elif "microsoft-standard-WSL2" in platform.uname().release:
|
|
sp.Popen(["wsl-open", path])
|
|
else:
|
|
sp.Popen(["xdg-open", path])
|
|
|
|
with gr.Column(variant='panel', elem_id=f"{tabname}_results"):
|
|
with gr.Group(elem_id=f"{tabname}_gallery_container"):
|
|
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(elem_id=f"image_buttons_{tabname}"):
|
|
open_folder_button = gr.Button(folder_symbol, elem_id="hidden_element" if shared.cmd_opts.hide_ui_dir_config else f'open_folder_{tabname}')
|
|
|
|
if tabname != "extras":
|
|
save = gr.Button('Save', elem_id=f'save_{tabname}')
|
|
save_zip = gr.Button('Zip', elem_id=f'save_zip_{tabname}')
|
|
|
|
buttons = parameters_copypaste.create_buttons(["img2img", "inpaint", "extras"])
|
|
|
|
open_folder_button.click(
|
|
fn=lambda: open_folder(opts.outdir_samples or outdir),
|
|
inputs=[],
|
|
outputs=[],
|
|
)
|
|
|
|
if tabname != "extras":
|
|
with gr.Row():
|
|
download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False, elem_id=f'download_files_{tabname}')
|
|
|
|
with gr.Group():
|
|
html_info = gr.HTML(elem_id=f'html_info_{tabname}')
|
|
html_log = gr.HTML(elem_id=f'html_log_{tabname}')
|
|
|
|
generation_info = gr.Textbox(visible=False, elem_id=f'generation_info_{tabname}')
|
|
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="function(x, y, z){ console.log(x, y, z); return [x, y, selected_gallery_index()] }",
|
|
inputs=[generation_info, html_info, html_info],
|
|
outputs=[html_info, html_info],
|
|
)
|
|
|
|
save.click(
|
|
fn=wrap_gradio_call(save_files),
|
|
_js="(x, y, z, w) => [x, y, false, selected_gallery_index()]",
|
|
inputs=[
|
|
generation_info,
|
|
result_gallery,
|
|
html_info,
|
|
html_info,
|
|
],
|
|
outputs=[
|
|
download_files,
|
|
html_log,
|
|
],
|
|
show_progress=False,
|
|
)
|
|
|
|
save_zip.click(
|
|
fn=wrap_gradio_call(save_files),
|
|
_js="(x, y, z, w) => [x, y, true, selected_gallery_index()]",
|
|
inputs=[
|
|
generation_info,
|
|
result_gallery,
|
|
html_info,
|
|
html_info,
|
|
],
|
|
outputs=[
|
|
download_files,
|
|
html_log,
|
|
]
|
|
)
|
|
|
|
else:
|
|
html_info_x = gr.HTML(elem_id=f'html_info_x_{tabname}')
|
|
html_info = gr.HTML(elem_id=f'html_info_{tabname}')
|
|
html_log = gr.HTML(elem_id=f'html_log_{tabname}')
|
|
|
|
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, html_log
|
|
|
|
|
|
def create_sampler_and_steps_selection(choices, tabname):
|
|
if opts.samplers_in_dropdown:
|
|
with FormRow(elem_id=f"sampler_selection_{tabname}"):
|
|
sampler_index = gr.Dropdown(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
|
|
steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
|
|
else:
|
|
with FormGroup(elem_id=f"sampler_selection_{tabname}"):
|
|
steps = gr.Slider(minimum=1, maximum=150, step=1, elem_id=f"{tabname}_steps", label="Sampling steps", value=20)
|
|
sampler_index = gr.Radio(label='Sampling method', elem_id=f"{tabname}_sampling", choices=[x.name for x in choices], value=choices[0].name, type="index")
|
|
|
|
return steps, sampler_index
|
|
|
|
|
|
def ordered_ui_categories():
|
|
user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder.split(","))}
|
|
|
|
for i, category in sorted(enumerate(shared.ui_reorder_categories), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
|
|
yield category
|
|
|
|
|
|
def create_ui():
|
|
import modules.img2img
|
|
import modules.txt2img
|
|
|
|
reload_javascript()
|
|
|
|
parameters_copypaste.reset()
|
|
|
|
modules.scripts.scripts_current = modules.scripts.scripts_txt2img
|
|
modules.scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
|
|
|
|
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
|
|
txt2img_prompt, txt2img_prompt_styles, txt2img_negative_prompt, submit, _, _,txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, 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="binary", visible=False)
|
|
|
|
with gr.Row().style(equal_height=False):
|
|
with gr.Column(variant='compact', elem_id="txt2img_settings"):
|
|
for category in ordered_ui_categories():
|
|
if category == "sampler":
|
|
steps, sampler_index = create_sampler_and_steps_selection(samplers, "txt2img")
|
|
|
|
elif category == "dimensions":
|
|
with FormRow():
|
|
with gr.Column(elem_id="txt2img_column_size", scale=4):
|
|
width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
|
|
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
|
|
|
|
if opts.dimensions_and_batch_together:
|
|
with gr.Column(elem_id="txt2img_column_batch"):
|
|
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
|
|
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
|
|
|
|
elif category == "cfg":
|
|
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
|
|
|
|
elif category == "seed":
|
|
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('txt2img')
|
|
|
|
elif category == "checkboxes":
|
|
with FormRow(elem_id="txt2img_checkboxes", variant="compact"):
|
|
restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
|
|
tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
|
|
enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
|
|
hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution", interactive=False)
|
|
|
|
elif category == "hires_fix":
|
|
with FormGroup(visible=False, elem_id="txt2img_hires_fix") as hr_options:
|
|
with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
|
|
hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
|
|
hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
|
|
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
|
|
|
|
with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"):
|
|
hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
|
|
hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
|
|
hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
|
|
|
|
elif category == "batch":
|
|
if not opts.dimensions_and_batch_together:
|
|
with FormRow(elem_id="txt2img_column_batch"):
|
|
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
|
|
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
|
|
|
|
elif category == "scripts":
|
|
with FormGroup(elem_id="txt2img_script_container"):
|
|
custom_inputs = modules.scripts.scripts_txt2img.setup_ui()
|
|
|
|
hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
|
|
for input in hr_resolution_preview_inputs:
|
|
input.change(
|
|
fn=calc_resolution_hires,
|
|
inputs=hr_resolution_preview_inputs,
|
|
outputs=[hr_final_resolution],
|
|
show_progress=False,
|
|
)
|
|
input.change(
|
|
None,
|
|
_js="onCalcResolutionHires",
|
|
inputs=hr_resolution_preview_inputs,
|
|
outputs=[],
|
|
show_progress=False,
|
|
)
|
|
|
|
txt2img_gallery, generation_info, html_info, html_log = create_output_panel("txt2img", opts.outdir_txt2img_samples)
|
|
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, extra_outputs=[None, '', '']),
|
|
_js="submit",
|
|
inputs=[
|
|
dummy_component,
|
|
txt2img_prompt,
|
|
txt2img_negative_prompt,
|
|
txt2img_prompt_styles,
|
|
steps,
|
|
sampler_index,
|
|
restore_faces,
|
|
tiling,
|
|
batch_count,
|
|
batch_size,
|
|
cfg_scale,
|
|
seed,
|
|
subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
|
|
height,
|
|
width,
|
|
enable_hr,
|
|
denoising_strength,
|
|
hr_scale,
|
|
hr_upscaler,
|
|
hr_second_pass_steps,
|
|
hr_resize_x,
|
|
hr_resize_y,
|
|
] + custom_inputs,
|
|
|
|
outputs=[
|
|
txt2img_gallery,
|
|
generation_info,
|
|
html_info,
|
|
html_log,
|
|
],
|
|
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],
|
|
show_progress = False,
|
|
)
|
|
|
|
txt2img_paste_fields = [
|
|
(txt2img_prompt, "Prompt"),
|
|
(txt2img_negative_prompt, "Negative prompt"),
|
|
(steps, "Steps"),
|
|
(sampler_index, "Sampler"),
|
|
(restore_faces, "Face restoration"),
|
|
(cfg_scale, "CFG scale"),
|
|
(seed, "Seed"),
|
|
(width, "Size-1"),
|
|
(height, "Size-2"),
|
|
(batch_size, "Batch size"),
|
|
(subseed, "Variation seed"),
|
|
(subseed_strength, "Variation seed strength"),
|
|
(seed_resize_from_w, "Seed resize from-1"),
|
|
(seed_resize_from_h, "Seed resize from-2"),
|
|
(denoising_strength, "Denoising strength"),
|
|
(enable_hr, lambda d: "Denoising strength" in d),
|
|
(hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
|
|
(hr_scale, "Hires upscale"),
|
|
(hr_upscaler, "Hires upscaler"),
|
|
(hr_second_pass_steps, "Hires steps"),
|
|
(hr_resize_x, "Hires resize-1"),
|
|
(hr_resize_y, "Hires resize-2"),
|
|
*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=wrap_queued_call(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, img2img_prompt_styles, img2img_negative_prompt, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste,token_counter, token_button = create_toprow(is_img2img=True)
|
|
|
|
img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="binary", visible=False)
|
|
|
|
with FormRow().style(equal_height=False):
|
|
with gr.Column(variant='compact', elem_id="img2img_settings"):
|
|
copy_image_buttons = []
|
|
copy_image_destinations = {}
|
|
|
|
def add_copy_image_controls(tab_name, elem):
|
|
with gr.Row(variant="compact", elem_id=f"img2img_copy_to_{tab_name}"):
|
|
gr.HTML("Copy image to: ", elem_id=f"img2img_label_copy_to_{tab_name}")
|
|
|
|
for title, name in zip(['img2img', 'sketch', 'inpaint', 'inpaint sketch'], ['img2img', 'sketch', 'inpaint', 'inpaint_sketch']):
|
|
if name == tab_name:
|
|
gr.Button(title, interactive=False)
|
|
copy_image_destinations[name] = elem
|
|
continue
|
|
|
|
button = gr.Button(title)
|
|
copy_image_buttons.append((button, name, elem))
|
|
|
|
with gr.Tabs(elem_id="mode_img2img"):
|
|
with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
|
|
init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool="editor", image_mode="RGBA").style(height=480)
|
|
add_copy_image_controls('img2img', init_img)
|
|
|
|
with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
|
|
sketch = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=480)
|
|
add_copy_image_controls('sketch', sketch)
|
|
|
|
with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
|
|
init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
|
|
add_copy_image_controls('inpaint', init_img_with_mask)
|
|
|
|
with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
|
|
inpaint_color_sketch = gr.Image(label="Color sketch inpainting", show_label=False, elem_id="inpaint_sketch", source="upload", interactive=True, type="pil", tool="color-sketch", image_mode="RGBA").style(height=480)
|
|
inpaint_color_sketch_orig = gr.State(None)
|
|
add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
|
|
|
|
def update_orig(image, state):
|
|
if image is not None:
|
|
same_size = state is not None and state.size == image.size
|
|
has_exact_match = np.any(np.all(np.array(image) == np.array(state), axis=-1))
|
|
edited = same_size and has_exact_match
|
|
return image if not edited or state is None else state
|
|
|
|
inpaint_color_sketch.change(update_orig, [inpaint_color_sketch, inpaint_color_sketch_orig], inpaint_color_sketch_orig)
|
|
|
|
with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
|
|
init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
|
|
init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", elem_id="img_inpaint_mask")
|
|
|
|
with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
|
|
hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
|
|
gr.HTML(f"<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running.<br>Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}</p>")
|
|
img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
|
|
img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
|
|
|
|
def copy_image(img):
|
|
if isinstance(img, dict) and 'image' in img:
|
|
return img['image']
|
|
|
|
return img
|
|
|
|
for button, name, elem in copy_image_buttons:
|
|
button.click(
|
|
fn=copy_image,
|
|
inputs=[elem],
|
|
outputs=[copy_image_destinations[name]],
|
|
)
|
|
button.click(
|
|
fn=lambda: None,
|
|
_js="switch_to_"+name.replace(" ", "_"),
|
|
inputs=[],
|
|
outputs=[],
|
|
)
|
|
|
|
with FormRow():
|
|
resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
|
|
|
|
for category in ordered_ui_categories():
|
|
if category == "sampler":
|
|
steps, sampler_index = create_sampler_and_steps_selection(samplers_for_img2img, "img2img")
|
|
|
|
elif category == "dimensions":
|
|
with FormRow():
|
|
with gr.Column(elem_id="img2img_column_size", scale=4):
|
|
width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
|
|
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
|
|
|
|
if opts.dimensions_and_batch_together:
|
|
with gr.Column(elem_id="img2img_column_batch"):
|
|
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
|
|
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
|
|
|
|
elif category == "cfg":
|
|
with FormGroup():
|
|
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
|
|
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
|
|
|
|
elif category == "seed":
|
|
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img')
|
|
|
|
elif category == "checkboxes":
|
|
with FormRow(elem_id="img2img_checkboxes"):
|
|
restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="img2img_restore_faces")
|
|
tiling = gr.Checkbox(label='Tiling', value=False, elem_id="img2img_tiling")
|
|
|
|
elif category == "batch":
|
|
if not opts.dimensions_and_batch_together:
|
|
with FormRow(elem_id="img2img_column_batch"):
|
|
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
|
|
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
|
|
|
|
elif category == "scripts":
|
|
with FormGroup(elem_id="img2img_script_container"):
|
|
custom_inputs = modules.scripts.scripts_img2img.setup_ui()
|
|
|
|
elif category == "inpaint":
|
|
with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
|
|
with FormRow():
|
|
mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
|
|
mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha")
|
|
|
|
with FormRow():
|
|
inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
|
|
|
|
with FormRow():
|
|
inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
|
|
|
|
with FormRow():
|
|
with gr.Column():
|
|
inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
|
|
|
|
with gr.Column(scale=4):
|
|
inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
|
|
|
|
def select_img2img_tab(tab):
|
|
return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
|
|
|
|
for i, elem in enumerate([tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]):
|
|
elem.select(
|
|
fn=lambda tab=i: select_img2img_tab(tab),
|
|
inputs=[],
|
|
outputs=[inpaint_controls, mask_alpha],
|
|
)
|
|
|
|
img2img_gallery, generation_info, html_info, html_log = 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
|
|
]
|
|
)
|
|
|
|
img2img_args = dict(
|
|
fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
|
|
_js="submit_img2img",
|
|
inputs=[
|
|
dummy_component,
|
|
dummy_component,
|
|
img2img_prompt,
|
|
img2img_negative_prompt,
|
|
img2img_prompt_styles,
|
|
init_img,
|
|
sketch,
|
|
init_img_with_mask,
|
|
inpaint_color_sketch,
|
|
inpaint_color_sketch_orig,
|
|
init_img_inpaint,
|
|
init_mask_inpaint,
|
|
steps,
|
|
sampler_index,
|
|
mask_blur,
|
|
mask_alpha,
|
|
inpainting_fill,
|
|
restore_faces,
|
|
tiling,
|
|
batch_count,
|
|
batch_size,
|
|
cfg_scale,
|
|
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,
|
|
html_log,
|
|
],
|
|
show_progress=False,
|
|
)
|
|
|
|
interrogate_args = dict(
|
|
_js="get_img2img_tab_index",
|
|
inputs=[
|
|
dummy_component,
|
|
img2img_batch_input_dir,
|
|
img2img_batch_output_dir,
|
|
init_img,
|
|
sketch,
|
|
init_img_with_mask,
|
|
inpaint_color_sketch,
|
|
init_img_inpaint,
|
|
],
|
|
outputs=[img2img_prompt, dummy_component],
|
|
show_progress=False,
|
|
)
|
|
|
|
img2img_prompt.submit(**img2img_args)
|
|
submit.click(**img2img_args)
|
|
|
|
img2img_interrogate.click(
|
|
fn=lambda *args : process_interrogate(interrogate, *args),
|
|
**interrogate_args,
|
|
)
|
|
|
|
img2img_deepbooru.click(
|
|
fn=lambda *args : process_interrogate(interrogate_deepbooru, *args),
|
|
**interrogate_args,
|
|
)
|
|
|
|
prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
|
|
style_dropdowns = [txt2img_prompt_styles, img2img_prompt_styles]
|
|
style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
|
|
|
|
for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
|
|
button.click(
|
|
fn=add_style,
|
|
_js="ask_for_style_name",
|
|
# Have to pass empty dummy component here, because the JavaScript and Python function have to accept
|
|
# the same number of parameters, but we only know the style-name after the JavaScript prompt
|
|
inputs=[dummy_component, prompt, negative_prompt],
|
|
outputs=[txt2img_prompt_styles, img2img_prompt_styles],
|
|
)
|
|
|
|
for button, (prompt, negative_prompt), styles, js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
|
|
button.click(
|
|
fn=apply_styles,
|
|
_js=js_func,
|
|
inputs=[prompt, negative_prompt, styles],
|
|
outputs=[prompt, negative_prompt, styles],
|
|
)
|
|
|
|
token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
|
|
|
|
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"),
|
|
(mask_blur, "Mask blur"),
|
|
*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='compact'):
|
|
with gr.Tabs(elem_id="mode_extras"):
|
|
with gr.TabItem('Single Image', elem_id="extras_single_tab"):
|
|
extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil", elem_id="extras_image")
|
|
|
|
with gr.TabItem('Batch Process', elem_id="extras_batch_process_tab"):
|
|
image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file", elem_id="extras_image_batch")
|
|
|
|
with gr.TabItem('Batch from Directory', elem_id="extras_batch_directory_tab"):
|
|
extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, placeholder="A directory on the same machine where the server is running.", elem_id="extras_batch_input_dir")
|
|
extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, placeholder="Leave blank to save images to the default path.", elem_id="extras_batch_output_dir")
|
|
show_extras_results = gr.Checkbox(label='Show result images', value=True, elem_id="extras_show_extras_results")
|
|
|
|
submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
|
|
|
|
with gr.Tabs(elem_id="extras_resize_mode"):
|
|
with gr.TabItem('Scale by', elem_id="extras_scale_by_tab"):
|
|
upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label="Resize", value=4, elem_id="extras_upscaling_resize")
|
|
with gr.TabItem('Scale to', elem_id="extras_scale_to_tab"):
|
|
with gr.Group():
|
|
with gr.Row():
|
|
upscaling_resize_w = gr.Number(label="Width", value=512, precision=0, elem_id="extras_upscaling_resize_w")
|
|
upscaling_resize_h = gr.Number(label="Height", value=512, precision=0, elem_id="extras_upscaling_resize_h")
|
|
upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id="extras_upscaling_crop")
|
|
|
|
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, elem_id="extras_upscaler_2_visibility")
|
|
|
|
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, elem_id="extras_gfpgan_visibility")
|
|
|
|
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, elem_id="extras_codeformer_visibility")
|
|
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, elem_id="extras_codeformer_weight")
|
|
|
|
with gr.Group():
|
|
upscale_before_face_fix = gr.Checkbox(label='Upscale Before Restoring Faces', value=False, elem_id="extras_upscale_before_face_fix")
|
|
|
|
result_images, html_info_x, html_info, html_log = create_output_panel("extras", opts.outdir_extras_samples)
|
|
|
|
submit.click(
|
|
fn=wrap_gradio_gpu_call(modules.extras.run_extras, extra_outputs=[None, '']),
|
|
_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, elem_id="pnginfo_generation_info")
|
|
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='compact'):
|
|
gr.HTML(value="<p style='margin-bottom: 2.5em'>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
|
|
|
|
with FormRow(elem_id="modelmerger_models"):
|
|
primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
|
|
create_refresh_button(primary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_A")
|
|
|
|
secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
|
|
create_refresh_button(secondary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_B")
|
|
|
|
tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
|
|
create_refresh_button(tertiary_model_name, modules.sd_models.list_models, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, "refresh_checkpoint_C")
|
|
|
|
custom_name = gr.Textbox(label="Custom Name (Optional)", elem_id="modelmerger_custom_name")
|
|
interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3, elem_id="modelmerger_interp_amount")
|
|
interp_method = gr.Radio(choices=["No interpolation", "Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method", elem_id="modelmerger_interp_method")
|
|
|
|
with FormRow():
|
|
checkpoint_format = gr.Radio(choices=["ckpt", "safetensors"], value="ckpt", label="Checkpoint format", elem_id="modelmerger_checkpoint_format")
|
|
save_as_half = gr.Checkbox(value=False, label="Save as float16", elem_id="modelmerger_save_as_half")
|
|
|
|
with FormRow():
|
|
with gr.Column():
|
|
config_source = gr.Radio(choices=["A, B or C", "B", "C", "Don't"], value="A, B or C", label="Copy config from", type="index", elem_id="modelmerger_config_method")
|
|
|
|
with gr.Column():
|
|
with FormRow():
|
|
bake_in_vae = gr.Dropdown(choices=["None"] + list(sd_vae.vae_dict), value="None", label="Bake in VAE", elem_id="modelmerger_bake_in_vae")
|
|
create_refresh_button(bake_in_vae, sd_vae.refresh_vae_list, lambda: {"choices": ["None"] + list(sd_vae.vae_dict)}, "modelmerger_refresh_bake_in_vae")
|
|
|
|
with gr.Row():
|
|
modelmerger_merge = gr.Button(elem_id="modelmerger_merge", value="Merge", variant='primary')
|
|
|
|
with gr.Column(variant='compact', elem_id="modelmerger_results_container"):
|
|
with gr.Group(elem_id="modelmerger_results_panel"):
|
|
modelmerger_result = gr.HTML(elem_id="modelmerger_result", show_label=False)
|
|
|
|
with gr.Blocks(analytics_enabled=False) as train_interface:
|
|
with gr.Row().style(equal_height=False):
|
|
gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
|
|
|
|
with gr.Row().style(equal_height=False):
|
|
with gr.Tabs(elem_id="train_tabs"):
|
|
|
|
with gr.Tab(label="Create embedding"):
|
|
new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name")
|
|
initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text")
|
|
nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt")
|
|
overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding")
|
|
|
|
with gr.Row():
|
|
with gr.Column(scale=3):
|
|
gr.HTML(value="")
|
|
|
|
with gr.Column():
|
|
create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding")
|
|
|
|
with gr.Tab(label="Create hypernetwork"):
|
|
new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name")
|
|
new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes")
|
|
new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure")
|
|
new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func")
|
|
new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option")
|
|
new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm")
|
|
new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout")
|
|
new_hypernetwork_dropout_structure = gr.Textbox("0, 0, 0", label="Enter hypernetwork Dropout structure (or empty). Recommended : 0~0.35 incrementing sequence: 0, 0.05, 0.15", placeholder="1st and last digit must be 0 and values should be between 0 and 1. ex:'0, 0.01, 0'")
|
|
overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork")
|
|
|
|
with gr.Row():
|
|
with gr.Column(scale=3):
|
|
gr.HTML(value="")
|
|
|
|
with gr.Column():
|
|
create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork")
|
|
|
|
with gr.Tab(label="Preprocess images"):
|
|
process_src = gr.Textbox(label='Source directory', elem_id="train_process_src")
|
|
process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst")
|
|
process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width")
|
|
process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height")
|
|
preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action")
|
|
|
|
with gr.Row():
|
|
process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip")
|
|
process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split")
|
|
process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop")
|
|
process_multicrop = gr.Checkbox(label='Auto-sized crop', elem_id="train_process_multicrop")
|
|
process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption")
|
|
process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru")
|
|
|
|
with gr.Row(visible=False) as process_split_extra_row:
|
|
process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold")
|
|
process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio")
|
|
|
|
with gr.Row(visible=False) as process_focal_crop_row:
|
|
process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight")
|
|
process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight")
|
|
process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight")
|
|
process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug")
|
|
|
|
with gr.Column(visible=False) as process_multicrop_col:
|
|
gr.Markdown('Each image is center-cropped with an automatically chosen width and height.')
|
|
with gr.Row():
|
|
process_multicrop_mindim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension lower bound", value=384, elem_id="train_process_multicrop_mindim")
|
|
process_multicrop_maxdim = gr.Slider(minimum=64, maximum=2048, step=8, label="Dimension upper bound", value=768, elem_id="train_process_multicrop_maxdim")
|
|
with gr.Row():
|
|
process_multicrop_minarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area lower bound", value=64*64, elem_id="train_process_multicrop_minarea")
|
|
process_multicrop_maxarea = gr.Slider(minimum=64*64, maximum=2048*2048, step=1, label="Area upper bound", value=640*640, elem_id="train_process_multicrop_maxarea")
|
|
with gr.Row():
|
|
process_multicrop_objective = gr.Radio(["Maximize area", "Minimize error"], value="Maximize area", label="Resizing objective", elem_id="train_process_multicrop_objective")
|
|
process_multicrop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, label="Error threshold", value=0.1, elem_id="train_process_multicrop_threshold")
|
|
|
|
with gr.Row():
|
|
with gr.Column(scale=3):
|
|
gr.HTML(value="")
|
|
|
|
with gr.Column():
|
|
with gr.Row():
|
|
interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing")
|
|
run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess")
|
|
|
|
process_split.change(
|
|
fn=lambda show: gr_show(show),
|
|
inputs=[process_split],
|
|
outputs=[process_split_extra_row],
|
|
)
|
|
|
|
process_focal_crop.change(
|
|
fn=lambda show: gr_show(show),
|
|
inputs=[process_focal_crop],
|
|
outputs=[process_focal_crop_row],
|
|
)
|
|
|
|
process_multicrop.change(
|
|
fn=lambda show: gr_show(show),
|
|
inputs=[process_multicrop],
|
|
outputs=[process_multicrop_col],
|
|
)
|
|
|
|
def get_textual_inversion_template_names():
|
|
return sorted([x for x in textual_inversion.textual_inversion_templates])
|
|
|
|
with gr.Tab(label="Train"):
|
|
gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding or Hypernetwork; you must specify a directory with a set of 1:1 ratio images <a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\" style=\"font-weight:bold;\">[wiki]</a></p>")
|
|
with FormRow():
|
|
train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
|
|
create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
|
|
|
|
train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[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 FormRow():
|
|
embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate")
|
|
hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate")
|
|
|
|
with FormRow():
|
|
clip_grad_mode = gr.Dropdown(value="disabled", label="Gradient Clipping", choices=["disabled", "value", "norm"])
|
|
clip_grad_value = gr.Textbox(placeholder="Gradient clip value", value="0.1", show_label=False)
|
|
|
|
with FormRow():
|
|
batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size")
|
|
gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step")
|
|
|
|
dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory")
|
|
log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory")
|
|
|
|
with FormRow():
|
|
template_file = gr.Dropdown(label='Prompt template', value="style_filewords.txt", elem_id="train_template_file", choices=get_textual_inversion_template_names())
|
|
create_refresh_button(template_file, textual_inversion.list_textual_inversion_templates, lambda: {"choices": get_textual_inversion_template_names()}, "refrsh_train_template_file")
|
|
|
|
training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width")
|
|
training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height")
|
|
varsize = gr.Checkbox(label="Do not resize images", value=False, elem_id="train_varsize")
|
|
steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps")
|
|
|
|
with FormRow():
|
|
create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every")
|
|
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every")
|
|
|
|
save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding")
|
|
preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img")
|
|
|
|
shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags")
|
|
tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out")
|
|
|
|
latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method")
|
|
|
|
with gr.Row():
|
|
train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding")
|
|
interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training")
|
|
train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork")
|
|
|
|
params = script_callbacks.UiTrainTabParams(txt2img_preview_params)
|
|
|
|
script_callbacks.ui_train_tabs_callback(params)
|
|
|
|
with gr.Column(elem_id='ti_gallery_container'):
|
|
ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
|
|
ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
|
|
ti_progress = gr.HTML(elem_id="ti_progress", value="")
|
|
ti_outcome = gr.HTML(elem_id="ti_error", value="")
|
|
|
|
create_embedding.click(
|
|
fn=modules.textual_inversion.ui.create_embedding,
|
|
inputs=[
|
|
new_embedding_name,
|
|
initialization_text,
|
|
nvpt,
|
|
overwrite_old_embedding,
|
|
],
|
|
outputs=[
|
|
train_embedding_name,
|
|
ti_output,
|
|
ti_outcome,
|
|
]
|
|
)
|
|
|
|
create_hypernetwork.click(
|
|
fn=modules.hypernetworks.ui.create_hypernetwork,
|
|
inputs=[
|
|
new_hypernetwork_name,
|
|
new_hypernetwork_sizes,
|
|
overwrite_old_hypernetwork,
|
|
new_hypernetwork_layer_structure,
|
|
new_hypernetwork_activation_func,
|
|
new_hypernetwork_initialization_option,
|
|
new_hypernetwork_add_layer_norm,
|
|
new_hypernetwork_use_dropout,
|
|
new_hypernetwork_dropout_structure
|
|
],
|
|
outputs=[
|
|
train_hypernetwork_name,
|
|
ti_output,
|
|
ti_outcome,
|
|
]
|
|
)
|
|
|
|
run_preprocess.click(
|
|
fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
|
|
_js="start_training_textual_inversion",
|
|
inputs=[
|
|
dummy_component,
|
|
process_src,
|
|
process_dst,
|
|
process_width,
|
|
process_height,
|
|
preprocess_txt_action,
|
|
process_flip,
|
|
process_split,
|
|
process_caption,
|
|
process_caption_deepbooru,
|
|
process_split_threshold,
|
|
process_overlap_ratio,
|
|
process_focal_crop,
|
|
process_focal_crop_face_weight,
|
|
process_focal_crop_entropy_weight,
|
|
process_focal_crop_edges_weight,
|
|
process_focal_crop_debug,
|
|
process_multicrop,
|
|
process_multicrop_mindim,
|
|
process_multicrop_maxdim,
|
|
process_multicrop_minarea,
|
|
process_multicrop_maxarea,
|
|
process_multicrop_objective,
|
|
process_multicrop_threshold,
|
|
],
|
|
outputs=[
|
|
ti_output,
|
|
ti_outcome,
|
|
],
|
|
)
|
|
|
|
train_embedding.click(
|
|
fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
|
|
_js="start_training_textual_inversion",
|
|
inputs=[
|
|
dummy_component,
|
|
train_embedding_name,
|
|
embedding_learn_rate,
|
|
batch_size,
|
|
gradient_step,
|
|
dataset_directory,
|
|
log_directory,
|
|
training_width,
|
|
training_height,
|
|
varsize,
|
|
steps,
|
|
clip_grad_mode,
|
|
clip_grad_value,
|
|
shuffle_tags,
|
|
tag_drop_out,
|
|
latent_sampling_method,
|
|
create_image_every,
|
|
save_embedding_every,
|
|
template_file,
|
|
save_image_with_stored_embedding,
|
|
preview_from_txt2img,
|
|
*txt2img_preview_params,
|
|
],
|
|
outputs=[
|
|
ti_output,
|
|
ti_outcome,
|
|
]
|
|
)
|
|
|
|
train_hypernetwork.click(
|
|
fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
|
|
_js="start_training_textual_inversion",
|
|
inputs=[
|
|
dummy_component,
|
|
train_hypernetwork_name,
|
|
hypernetwork_learn_rate,
|
|
batch_size,
|
|
gradient_step,
|
|
dataset_directory,
|
|
log_directory,
|
|
training_width,
|
|
training_height,
|
|
varsize,
|
|
steps,
|
|
clip_grad_mode,
|
|
clip_grad_value,
|
|
shuffle_tags,
|
|
tag_drop_out,
|
|
latent_sampling_method,
|
|
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 FormRow():
|
|
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{": " if len(changed) > 0 else ""}{", ".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:
|
|
with gr.Row():
|
|
with gr.Column(scale=6):
|
|
settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit")
|
|
with gr.Column():
|
|
restart_gradio = gr.Button(value='Reload UI', variant='primary', elem_id="settings_restart_gradio")
|
|
|
|
result = gr.HTML(elem_id="settings_result")
|
|
|
|
quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
|
|
quicksettings_names = {x: i for i, x in enumerate(quicksettings_names) if x != 'quicksettings'}
|
|
|
|
quicksettings_list = []
|
|
|
|
previous_section = None
|
|
current_tab = None
|
|
current_row = None
|
|
with gr.Tabs(elem_id="settings"):
|
|
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:
|
|
elem_id, text = item.section
|
|
|
|
if current_tab is not None:
|
|
current_row.__exit__()
|
|
current_tab.__exit__()
|
|
|
|
gr.Group()
|
|
current_tab = gr.TabItem(elem_id="settings_{}".format(elem_id), label=text)
|
|
current_tab.__enter__()
|
|
current_row = gr.Column(variant='compact')
|
|
current_row.__enter__()
|
|
|
|
previous_section = item.section
|
|
|
|
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)
|
|
|
|
if current_tab is not None:
|
|
current_row.__exit__()
|
|
current_tab.__exit__()
|
|
|
|
with gr.TabItem("Actions"):
|
|
request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
|
|
download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
|
|
reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary', elem_id="settings_reload_script_bodies")
|
|
|
|
if os.path.exists("html/licenses.html"):
|
|
with open("html/licenses.html", encoding="utf8") as file:
|
|
with gr.TabItem("Licenses"):
|
|
gr.HTML(file.read(), elem_id="licenses")
|
|
|
|
gr.Button(value="Show all pages", elem_id="settings_show_all_pages")
|
|
|
|
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=[],
|
|
)
|
|
|
|
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", variant="compact"):
|
|
for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
|
|
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)
|
|
|
|
if os.path.exists("html/footer.html"):
|
|
with open("html/footer.html", encoding="utf8") as file:
|
|
footer = file.read()
|
|
footer = footer.format(versions=versions_html())
|
|
gr.HTML(footer, elem_id="footer")
|
|
|
|
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 [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
|
|
return results
|
|
|
|
modelmerger_merge.click(fn=lambda: '', inputs=[], outputs=[modelmerger_result])
|
|
modelmerger_merge.click(
|
|
fn=wrap_gradio_gpu_call(modelmerger, extra_outputs=lambda: [gr.update() for _ in range(4)]),
|
|
_js='modelmerger',
|
|
inputs=[
|
|
dummy_component,
|
|
primary_model_name,
|
|
secondary_model_name,
|
|
tertiary_model_name,
|
|
interp_method,
|
|
interp_amount,
|
|
save_as_half,
|
|
custom_name,
|
|
checkpoint_format,
|
|
config_source,
|
|
bake_in_vae,
|
|
],
|
|
outputs=[
|
|
primary_model_name,
|
|
secondary_model_name,
|
|
tertiary_model_name,
|
|
component_dict['sd_model_checkpoint'],
|
|
modelmerger_result,
|
|
]
|
|
)
|
|
|
|
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):
|
|
pass
|
|
|
|
# this warning is generally not useful;
|
|
# 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, gr.Dropdown] 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')
|
|
|
|
if type(x) == gr.Dropdown:
|
|
def check_dropdown(val):
|
|
if x.multiselect:
|
|
return all([value in x.choices for value in val])
|
|
else:
|
|
return val in x.choices
|
|
|
|
apply_field(x, 'value', check_dropdown, getattr(x, 'init_field', None))
|
|
|
|
visit(txt2img_interface, loadsave, "txt2img")
|
|
visit(img2img_interface, loadsave, "img2img")
|
|
visit(extras_interface, loadsave, "extras")
|
|
visit(modelmerger_interface, loadsave, "modelmerger")
|
|
visit(train_interface, loadsave, "train")
|
|
|
|
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'<script>{jsfile.read()}</script>'
|
|
|
|
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<!-- {filename} --><script>{jsfile.read()}</script>"
|
|
|
|
if cmd_opts.theme is not None:
|
|
javascript += f"\n<script>set_theme('{cmd_opts.theme}');</script>\n"
|
|
|
|
javascript += f"\n<script>{localization.localization_js(shared.opts.localization)}</script>"
|
|
|
|
def template_response(*args, **kwargs):
|
|
res = shared.GradioTemplateResponseOriginal(*args, **kwargs)
|
|
res.body = res.body.replace(
|
|
b'</head>', f'{javascript}</head>'.encode("utf8"))
|
|
res.init_headers()
|
|
return res
|
|
|
|
gradio.routes.templates.TemplateResponse = template_response
|
|
|
|
|
|
if not hasattr(shared, 'GradioTemplateResponseOriginal'):
|
|
shared.GradioTemplateResponseOriginal = gradio.routes.templates.TemplateResponse
|
|
|
|
|
|
def versions_html():
|
|
import torch
|
|
import launch
|
|
|
|
python_version = ".".join([str(x) for x in sys.version_info[0:3]])
|
|
commit = launch.commit_hash()
|
|
short_commit = commit[0:8]
|
|
|
|
if shared.xformers_available:
|
|
import xformers
|
|
xformers_version = xformers.__version__
|
|
else:
|
|
xformers_version = "N/A"
|
|
|
|
return f"""
|
|
python: <span title="{sys.version}">{python_version}</span>
|
|
•
|
|
torch: {torch.__version__}
|
|
•
|
|
xformers: {xformers_version}
|
|
•
|
|
gradio: {gr.__version__}
|
|
•
|
|
commit: <a href="https://github.com/AUTOMATIC1111/stable-diffusion-webui/commit/{commit}">{short_commit}</a>
|
|
•
|
|
checkpoint: <a id="sd_checkpoint_hash">N/A</a>
|
|
"""
|