mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-14 22:53:25 +03:00
1858 lines
80 KiB
Python
1858 lines
80 KiB
Python
import base64
|
|
import html
|
|
import io
|
|
import json
|
|
import math
|
|
import mimetypes
|
|
import os
|
|
import random
|
|
import sys
|
|
import tempfile
|
|
import time
|
|
import traceback
|
|
import platform
|
|
import subprocess as sp
|
|
from functools import partial, reduce
|
|
|
|
import numpy as np
|
|
import torch
|
|
from PIL import Image, PngImagePlugin
|
|
import piexif
|
|
|
|
import gradio as gr
|
|
import gradio.utils
|
|
import gradio.routes
|
|
|
|
from modules import sd_hijack, sd_models, localization
|
|
from modules.paths import script_path
|
|
from modules.shared import opts, cmd_opts, restricted_opts
|
|
if cmd_opts.deepdanbooru:
|
|
from modules.deepbooru import get_deepbooru_tags
|
|
import modules.shared as shared
|
|
from modules.sd_samplers import samplers, samplers_for_img2img
|
|
from modules.sd_hijack import model_hijack
|
|
import modules.ldsr_model
|
|
import modules.scripts
|
|
import modules.gfpgan_model
|
|
import modules.codeformer_model
|
|
import modules.styles
|
|
import modules.generation_parameters_copypaste
|
|
from modules import prompt_parser
|
|
from modules.images import save_image
|
|
import modules.textual_inversion.ui
|
|
import modules.hypernetworks.ui
|
|
import modules.images_history as img_his
|
|
|
|
# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
|
|
mimetypes.init()
|
|
mimetypes.add_type('application/javascript', '.js')
|
|
|
|
|
|
if not cmd_opts.share and not cmd_opts.listen:
|
|
# fix gradio phoning home
|
|
gradio.utils.version_check = lambda: None
|
|
gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
|
|
|
|
if cmd_opts.ngrok != None:
|
|
import modules.ngrok as ngrok
|
|
print('ngrok authtoken detected, trying to connect...')
|
|
ngrok.connect(cmd_opts.ngrok, cmd_opts.port if cmd_opts.port != None else 7860, cmd_opts.ngrok_region)
|
|
|
|
|
|
def gr_show(visible=True):
|
|
return {"visible": visible, "__type__": "update"}
|
|
|
|
|
|
sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
|
|
sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
|
|
|
|
css_hide_progressbar = """
|
|
.wrap .m-12 svg { display:none!important; }
|
|
.wrap .m-12::before { content:"Loading..." }
|
|
.progress-bar { display:none!important; }
|
|
.meta-text { display:none!important; }
|
|
"""
|
|
|
|
# Using constants for these since the variation selector isn't visible.
|
|
# Important that they exactly match script.js for tooltip to work.
|
|
random_symbol = '\U0001f3b2\ufe0f' # 🎲️
|
|
reuse_symbol = '\u267b\ufe0f' # ♻️
|
|
art_symbol = '\U0001f3a8' # 🎨
|
|
paste_symbol = '\u2199\ufe0f' # ↙
|
|
folder_symbol = '\U0001f4c2' # 📂
|
|
refresh_symbol = '\U0001f504' # 🔄
|
|
save_style_symbol = '\U0001f4be' # 💾
|
|
apply_style_symbol = '\U0001f4cb' # 📋
|
|
|
|
|
|
def plaintext_to_html(text):
|
|
text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
|
|
return text
|
|
|
|
|
|
def image_from_url_text(filedata):
|
|
if type(filedata) == dict and filedata["is_file"]:
|
|
filename = filedata["name"]
|
|
tempdir = os.path.normpath(tempfile.gettempdir())
|
|
normfn = os.path.normpath(filename)
|
|
assert normfn.startswith(tempdir), 'trying to open image file not in temporary directory'
|
|
|
|
return Image.open(filename)
|
|
|
|
if type(filedata) == list:
|
|
if len(filedata) == 0:
|
|
return None
|
|
|
|
filedata = filedata[0]
|
|
|
|
if filedata.startswith("data:image/png;base64,"):
|
|
filedata = filedata[len("data:image/png;base64,"):]
|
|
|
|
filedata = base64.decodebytes(filedata.encode('utf-8'))
|
|
image = Image.open(io.BytesIO(filedata))
|
|
return image
|
|
|
|
|
|
def send_gradio_gallery_to_image(x):
|
|
if len(x) == 0:
|
|
return None
|
|
|
|
return image_from_url_text(x[0])
|
|
|
|
|
|
def save_files(js_data, images, do_make_zip, index):
|
|
import csv
|
|
filenames = []
|
|
fullfns = []
|
|
|
|
#quick dictionary to class object conversion. Its necessary due apply_filename_pattern requiring it
|
|
class MyObject:
|
|
def __init__(self, d=None):
|
|
if d is not None:
|
|
for key, value in d.items():
|
|
setattr(self, key, value)
|
|
|
|
data = json.loads(js_data)
|
|
|
|
p = MyObject(data)
|
|
path = opts.outdir_save
|
|
save_to_dirs = opts.use_save_to_dirs_for_ui
|
|
extension: str = opts.samples_format
|
|
start_index = 0
|
|
|
|
if index > -1 and opts.save_selected_only and (index >= data["index_of_first_image"]): # ensures we are looking at a specific non-grid picture, and we have save_selected_only
|
|
|
|
images = [images[index]]
|
|
start_index = index
|
|
|
|
os.makedirs(opts.outdir_save, exist_ok=True)
|
|
|
|
with open(os.path.join(opts.outdir_save, "log.csv"), "a", encoding="utf8", newline='') as file:
|
|
at_start = file.tell() == 0
|
|
writer = csv.writer(file)
|
|
if at_start:
|
|
writer.writerow(["prompt", "seed", "width", "height", "sampler", "cfgs", "steps", "filename", "negative_prompt"])
|
|
|
|
for image_index, filedata in enumerate(images, start_index):
|
|
image = image_from_url_text(filedata)
|
|
|
|
is_grid = image_index < p.index_of_first_image
|
|
i = 0 if is_grid else (image_index - p.index_of_first_image)
|
|
|
|
fullfn, txt_fullfn = save_image(image, path, "", seed=p.all_seeds[i], prompt=p.all_prompts[i], extension=extension, info=p.infotexts[image_index], grid=is_grid, p=p, save_to_dirs=save_to_dirs)
|
|
|
|
filename = os.path.relpath(fullfn, path)
|
|
filenames.append(filename)
|
|
fullfns.append(fullfn)
|
|
if txt_fullfn:
|
|
filenames.append(os.path.basename(txt_fullfn))
|
|
fullfns.append(txt_fullfn)
|
|
|
|
writer.writerow([data["prompt"], data["seed"], data["width"], data["height"], data["sampler"], data["cfg_scale"], data["steps"], filenames[0], data["negative_prompt"]])
|
|
|
|
# Make Zip
|
|
if do_make_zip:
|
|
zip_filepath = os.path.join(path, "images.zip")
|
|
|
|
from zipfile import ZipFile
|
|
with ZipFile(zip_filepath, "w") as zip_file:
|
|
for i in range(len(fullfns)):
|
|
with open(fullfns[i], mode="rb") as f:
|
|
zip_file.writestr(filenames[i], f.read())
|
|
fullfns.insert(0, zip_filepath)
|
|
|
|
return gr.File.update(value=fullfns, visible=True), '', '', plaintext_to_html(f"Saved: {filenames[0]}")
|
|
|
|
|
|
def save_pil_to_file(pil_image, dir=None):
|
|
use_metadata = False
|
|
metadata = PngImagePlugin.PngInfo()
|
|
for key, value in pil_image.info.items():
|
|
if isinstance(key, str) and isinstance(value, str):
|
|
metadata.add_text(key, value)
|
|
use_metadata = True
|
|
|
|
file_obj = tempfile.NamedTemporaryFile(delete=False, suffix=".png", dir=dir)
|
|
pil_image.save(file_obj, pnginfo=(metadata if use_metadata else None))
|
|
return file_obj
|
|
|
|
|
|
# override save to file function so that it also writes PNG info
|
|
gr.processing_utils.save_pil_to_file = save_pil_to_file
|
|
|
|
|
|
def wrap_gradio_call(func, extra_outputs=None):
|
|
def f(*args, extra_outputs_array=extra_outputs, **kwargs):
|
|
run_memmon = opts.memmon_poll_rate > 0 and not shared.mem_mon.disabled
|
|
if run_memmon:
|
|
shared.mem_mon.monitor()
|
|
t = time.perf_counter()
|
|
|
|
try:
|
|
res = list(func(*args, **kwargs))
|
|
except Exception as e:
|
|
# When printing out our debug argument list, do not print out more than a MB of text
|
|
max_debug_str_len = 131072 # (1024*1024)/8
|
|
|
|
print("Error completing request", file=sys.stderr)
|
|
argStr = f"Arguments: {str(args)} {str(kwargs)}"
|
|
print(argStr[:max_debug_str_len], file=sys.stderr)
|
|
if len(argStr) > max_debug_str_len:
|
|
print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
|
|
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
|
|
shared.state.job = ""
|
|
shared.state.job_count = 0
|
|
|
|
if extra_outputs_array is None:
|
|
extra_outputs_array = [None, '']
|
|
|
|
res = extra_outputs_array + [f"<div class='error'>{plaintext_to_html(type(e).__name__+': '+str(e))}</div>"]
|
|
|
|
elapsed = time.perf_counter() - t
|
|
elapsed_m = int(elapsed // 60)
|
|
elapsed_s = elapsed % 60
|
|
elapsed_text = f"{elapsed_s:.2f}s"
|
|
if (elapsed_m > 0):
|
|
elapsed_text = f"{elapsed_m}m "+elapsed_text
|
|
|
|
if run_memmon:
|
|
mem_stats = {k: -(v//-(1024*1024)) for k, v in shared.mem_mon.stop().items()}
|
|
active_peak = mem_stats['active_peak']
|
|
reserved_peak = mem_stats['reserved_peak']
|
|
sys_peak = mem_stats['system_peak']
|
|
sys_total = mem_stats['total']
|
|
sys_pct = round(sys_peak/max(sys_total, 1) * 100, 2)
|
|
|
|
vram_html = f"<p class='vram'>Torch active/reserved: {active_peak}/{reserved_peak} MiB, <wbr>Sys VRAM: {sys_peak}/{sys_total} MiB ({sys_pct}%)</p>"
|
|
else:
|
|
vram_html = ''
|
|
|
|
# last item is always HTML
|
|
res[-1] += f"<div class='performance'><p class='time'>Time taken: <wbr>{elapsed_text}</p>{vram_html}</div>"
|
|
|
|
shared.state.skipped = False
|
|
shared.state.interrupted = False
|
|
shared.state.job_count = 0
|
|
|
|
return tuple(res)
|
|
|
|
return f
|
|
|
|
|
|
def calc_time_left(progress, threshold, label, force_display):
|
|
if progress == 0:
|
|
return ""
|
|
else:
|
|
time_since_start = time.time() - shared.state.time_start
|
|
eta = (time_since_start/progress)
|
|
eta_relative = eta-time_since_start
|
|
if (eta_relative > threshold and progress > 0.02) or force_display:
|
|
return label + time.strftime('%H:%M:%S', time.gmtime(eta_relative))
|
|
else:
|
|
return ""
|
|
|
|
|
|
def check_progress_call(id_part):
|
|
if shared.state.job_count == 0:
|
|
return "", gr_show(False), gr_show(False), gr_show(False)
|
|
|
|
progress = 0
|
|
|
|
if shared.state.job_count > 0:
|
|
progress += shared.state.job_no / shared.state.job_count
|
|
if shared.state.sampling_steps > 0:
|
|
progress += 1 / shared.state.job_count * shared.state.sampling_step / shared.state.sampling_steps
|
|
|
|
time_left = calc_time_left( progress, 60, " ETA:", shared.state.time_left_force_display )
|
|
if time_left != "":
|
|
shared.state.time_left_force_display = True
|
|
|
|
progress = min(progress, 1)
|
|
|
|
progressbar = ""
|
|
if opts.show_progressbar:
|
|
progressbar = f"""<div class='progressDiv'><div class='progress' style="overflow:hidden;width:{progress * 100}%">{str(int(progress*100))+"%"+time_left if progress > 0.01 else ""}</div></div>"""
|
|
|
|
image = gr_show(False)
|
|
preview_visibility = gr_show(False)
|
|
|
|
if opts.show_progress_every_n_steps > 0:
|
|
if shared.parallel_processing_allowed:
|
|
|
|
if shared.state.sampling_step - shared.state.current_image_sampling_step >= opts.show_progress_every_n_steps and shared.state.current_latent is not None:
|
|
shared.state.current_image = modules.sd_samplers.sample_to_image(shared.state.current_latent)
|
|
shared.state.current_image_sampling_step = shared.state.sampling_step
|
|
|
|
image = shared.state.current_image
|
|
|
|
if image is None:
|
|
image = gr.update(value=None)
|
|
else:
|
|
preview_visibility = gr_show(True)
|
|
|
|
if shared.state.textinfo is not None:
|
|
textinfo_result = gr.HTML.update(value=shared.state.textinfo, visible=True)
|
|
else:
|
|
textinfo_result = gr_show(False)
|
|
|
|
return f"<span id='{id_part}_progress_span' style='display: none'>{time.time()}</span><p>{progressbar}</p>", preview_visibility, image, textinfo_result
|
|
|
|
|
|
def check_progress_call_initial(id_part):
|
|
shared.state.job_count = -1
|
|
shared.state.current_latent = None
|
|
shared.state.current_image = None
|
|
shared.state.textinfo = None
|
|
shared.state.time_start = time.time()
|
|
shared.state.time_left_force_display = False
|
|
|
|
return check_progress_call(id_part)
|
|
|
|
|
|
def roll_artist(prompt):
|
|
allowed_cats = set([x for x in shared.artist_db.categories() if len(opts.random_artist_categories)==0 or x in opts.random_artist_categories])
|
|
artist = random.choice([x for x in shared.artist_db.artists if x.category in allowed_cats])
|
|
|
|
return prompt + ", " + artist.name if prompt != '' else artist.name
|
|
|
|
|
|
def visit(x, func, path=""):
|
|
if hasattr(x, 'children'):
|
|
for c in x.children:
|
|
visit(c, func, path)
|
|
elif x.label is not None:
|
|
func(path + "/" + str(x.label), x)
|
|
|
|
|
|
def add_style(name: str, prompt: str, negative_prompt: str):
|
|
if name is None:
|
|
return [gr_show() for x in range(4)]
|
|
|
|
style = modules.styles.PromptStyle(name, prompt, negative_prompt)
|
|
shared.prompt_styles.styles[style.name] = style
|
|
# Save all loaded prompt styles: this allows us to update the storage format in the future more easily, because we
|
|
# reserialize all styles every time we save them
|
|
shared.prompt_styles.save_styles(shared.styles_filename)
|
|
|
|
return [gr.Dropdown.update(visible=True, choices=list(shared.prompt_styles.styles)) for _ in range(4)]
|
|
|
|
|
|
def apply_styles(prompt, prompt_neg, style1_name, style2_name):
|
|
prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, [style1_name, style2_name])
|
|
prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, [style1_name, style2_name])
|
|
|
|
return [gr.Textbox.update(value=prompt), gr.Textbox.update(value=prompt_neg), gr.Dropdown.update(value="None"), gr.Dropdown.update(value="None")]
|
|
|
|
|
|
def interrogate(image):
|
|
prompt = shared.interrogator.interrogate(image)
|
|
|
|
return gr_show(True) if prompt is None else prompt
|
|
|
|
|
|
def interrogate_deepbooru(image):
|
|
prompt = get_deepbooru_tags(image)
|
|
return gr_show(True) if prompt is None else prompt
|
|
|
|
|
|
def create_seed_inputs():
|
|
with gr.Row():
|
|
with gr.Box():
|
|
with gr.Row(elem_id='seed_row'):
|
|
seed = (gr.Textbox if cmd_opts.use_textbox_seed else gr.Number)(label='Seed', value=-1)
|
|
seed.style(container=False)
|
|
random_seed = gr.Button(random_symbol, elem_id='random_seed')
|
|
reuse_seed = gr.Button(reuse_symbol, elem_id='reuse_seed')
|
|
|
|
with gr.Box(elem_id='subseed_show_box'):
|
|
seed_checkbox = gr.Checkbox(label='Extra', elem_id='subseed_show', value=False)
|
|
|
|
# Components to show/hide based on the 'Extra' checkbox
|
|
seed_extras = []
|
|
|
|
with gr.Row(visible=False) as seed_extra_row_1:
|
|
seed_extras.append(seed_extra_row_1)
|
|
with gr.Box():
|
|
with gr.Row(elem_id='subseed_row'):
|
|
subseed = gr.Number(label='Variation seed', value=-1)
|
|
subseed.style(container=False)
|
|
random_subseed = gr.Button(random_symbol, elem_id='random_subseed')
|
|
reuse_subseed = gr.Button(reuse_symbol, elem_id='reuse_subseed')
|
|
subseed_strength = gr.Slider(label='Variation strength', value=0.0, minimum=0, maximum=1, step=0.01)
|
|
|
|
with gr.Row(visible=False) as seed_extra_row_2:
|
|
seed_extras.append(seed_extra_row_2)
|
|
seed_resize_from_w = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from width", value=0)
|
|
seed_resize_from_h = gr.Slider(minimum=0, maximum=2048, step=64, label="Resize seed from height", value=0)
|
|
|
|
random_seed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[seed])
|
|
random_subseed.click(fn=lambda: -1, show_progress=False, inputs=[], outputs=[subseed])
|
|
|
|
def change_visibility(show):
|
|
return {comp: gr_show(show) for comp in seed_extras}
|
|
|
|
seed_checkbox.change(change_visibility, show_progress=False, inputs=[seed_checkbox], outputs=seed_extras)
|
|
|
|
return seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox
|
|
|
|
|
|
def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info: gr.Textbox, dummy_component, is_subseed):
|
|
""" Connects a 'reuse (sub)seed' button's click event so that it copies last used
|
|
(sub)seed value from generation info the to the seed field. If copying subseed and subseed strength
|
|
was 0, i.e. no variation seed was used, it copies the normal seed value instead."""
|
|
def copy_seed(gen_info_string: str, index):
|
|
res = -1
|
|
|
|
try:
|
|
gen_info = json.loads(gen_info_string)
|
|
index -= gen_info.get('index_of_first_image', 0)
|
|
|
|
if is_subseed and gen_info.get('subseed_strength', 0) > 0:
|
|
all_subseeds = gen_info.get('all_subseeds', [-1])
|
|
res = all_subseeds[index if 0 <= index < len(all_subseeds) else 0]
|
|
else:
|
|
all_seeds = gen_info.get('all_seeds', [-1])
|
|
res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
|
|
|
|
except json.decoder.JSONDecodeError as e:
|
|
if gen_info_string != '':
|
|
print("Error parsing JSON generation info:", file=sys.stderr)
|
|
print(gen_info_string, file=sys.stderr)
|
|
|
|
return [res, gr_show(False)]
|
|
|
|
reuse_seed.click(
|
|
fn=copy_seed,
|
|
_js="(x, y) => [x, selected_gallery_index()]",
|
|
show_progress=False,
|
|
inputs=[generation_info, dummy_component],
|
|
outputs=[seed, dummy_component]
|
|
)
|
|
|
|
|
|
def update_token_counter(text, steps):
|
|
try:
|
|
_, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
|
|
prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
|
|
|
|
except Exception:
|
|
# a parsing error can happen here during typing, and we don't want to bother the user with
|
|
# messages related to it in console
|
|
prompt_schedules = [[[steps, text]]]
|
|
|
|
flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
|
|
prompts = [prompt_text for step, prompt_text in flat_prompts]
|
|
tokens, token_count, max_length = max([model_hijack.tokenize(prompt) for prompt in prompts], key=lambda args: args[1])
|
|
style_class = ' class="red"' if (token_count > max_length) else ""
|
|
return f"<span {style_class}>{token_count}/{max_length}</span>"
|
|
|
|
|
|
def create_toprow(is_img2img):
|
|
id_part = "img2img" if is_img2img else "txt2img"
|
|
|
|
with gr.Row(elem_id="toprow"):
|
|
with gr.Column(scale=6):
|
|
with gr.Row():
|
|
with gr.Column(scale=80):
|
|
with gr.Row():
|
|
prompt = gr.Textbox(label="Prompt", elem_id=f"{id_part}_prompt", show_label=False, lines=2,
|
|
placeholder="Prompt (press Ctrl+Enter or Alt+Enter to generate)"
|
|
)
|
|
|
|
with gr.Row():
|
|
with gr.Column(scale=80):
|
|
with gr.Row():
|
|
negative_prompt = gr.Textbox(label="Negative prompt", elem_id=f"{id_part}_neg_prompt", show_label=False, lines=2,
|
|
placeholder="Negative prompt (press Ctrl+Enter or Alt+Enter to generate)"
|
|
)
|
|
|
|
with gr.Column(scale=1, elem_id="roll_col"):
|
|
roll = gr.Button(value=art_symbol, elem_id="roll", visible=len(shared.artist_db.artists) > 0)
|
|
paste = gr.Button(value=paste_symbol, elem_id="paste")
|
|
save_style = gr.Button(value=save_style_symbol, elem_id="style_create")
|
|
prompt_style_apply = gr.Button(value=apply_style_symbol, elem_id="style_apply")
|
|
|
|
token_counter = gr.HTML(value="<span></span>", elem_id=f"{id_part}_token_counter")
|
|
token_button = gr.Button(visible=False, elem_id=f"{id_part}_token_button")
|
|
|
|
button_interrogate = None
|
|
button_deepbooru = None
|
|
if is_img2img:
|
|
with gr.Column(scale=1, elem_id="interrogate_col"):
|
|
button_interrogate = gr.Button('Interrogate\nCLIP', elem_id="interrogate")
|
|
|
|
if cmd_opts.deepdanbooru:
|
|
button_deepbooru = gr.Button('Interrogate\nDeepBooru', elem_id="deepbooru")
|
|
|
|
with gr.Column(scale=1):
|
|
with gr.Row():
|
|
skip = gr.Button('Skip', elem_id=f"{id_part}_skip")
|
|
interrupt = gr.Button('Interrupt', elem_id=f"{id_part}_interrupt")
|
|
submit = gr.Button('Generate', elem_id=f"{id_part}_generate", variant='primary')
|
|
|
|
skip.click(
|
|
fn=lambda: shared.state.skip(),
|
|
inputs=[],
|
|
outputs=[],
|
|
)
|
|
|
|
interrupt.click(
|
|
fn=lambda: shared.state.interrupt(),
|
|
inputs=[],
|
|
outputs=[],
|
|
)
|
|
|
|
with gr.Row():
|
|
with gr.Column(scale=1, elem_id="style_pos_col"):
|
|
prompt_style = gr.Dropdown(label="Style 1", elem_id=f"{id_part}_style_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
|
|
prompt_style.save_to_config = True
|
|
|
|
with gr.Column(scale=1, elem_id="style_neg_col"):
|
|
prompt_style2 = gr.Dropdown(label="Style 2", elem_id=f"{id_part}_style2_index", choices=[k for k, v in shared.prompt_styles.styles.items()], value=next(iter(shared.prompt_styles.styles.keys())))
|
|
prompt_style2.save_to_config = True
|
|
|
|
return prompt, roll, prompt_style, negative_prompt, prompt_style2, submit, button_interrogate, button_deepbooru, prompt_style_apply, save_style, paste, token_counter, token_button
|
|
|
|
|
|
def setup_progressbar(progressbar, preview, id_part, textinfo=None):
|
|
if textinfo is None:
|
|
textinfo = gr.HTML(visible=False)
|
|
|
|
check_progress = gr.Button('Check progress', elem_id=f"{id_part}_check_progress", visible=False)
|
|
check_progress.click(
|
|
fn=lambda: check_progress_call(id_part),
|
|
show_progress=False,
|
|
inputs=[],
|
|
outputs=[progressbar, preview, preview, textinfo],
|
|
)
|
|
|
|
check_progress_initial = gr.Button('Check progress (first)', elem_id=f"{id_part}_check_progress_initial", visible=False)
|
|
check_progress_initial.click(
|
|
fn=lambda: check_progress_call_initial(id_part),
|
|
show_progress=False,
|
|
inputs=[],
|
|
outputs=[progressbar, preview, preview, textinfo],
|
|
)
|
|
|
|
|
|
def apply_setting(key, value):
|
|
if value is None:
|
|
return gr.update()
|
|
|
|
# dont allow model to be swapped when model hash exists in prompt
|
|
if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
|
|
return gr.update()
|
|
|
|
if key == "sd_model_checkpoint":
|
|
ckpt_info = sd_models.get_closet_checkpoint_match(value)
|
|
|
|
if ckpt_info is not None:
|
|
value = ckpt_info.title
|
|
else:
|
|
return gr.update()
|
|
|
|
comp_args = opts.data_labels[key].component_args
|
|
if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
|
|
return
|
|
|
|
valtype = type(opts.data_labels[key].default)
|
|
oldval = opts.data[key]
|
|
opts.data[key] = valtype(value) if valtype != type(None) else value
|
|
if oldval != value and opts.data_labels[key].onchange is not None:
|
|
opts.data_labels[key].onchange()
|
|
|
|
opts.save(shared.config_filename)
|
|
return value
|
|
|
|
|
|
def create_ui(wrap_gradio_gpu_call):
|
|
import modules.img2img
|
|
import modules.txt2img
|
|
|
|
def create_refresh_button(refresh_component, refresh_method, refreshed_args, elem_id):
|
|
def refresh():
|
|
refresh_method()
|
|
args = refreshed_args() if callable(refreshed_args) else refreshed_args
|
|
|
|
for k, v in args.items():
|
|
setattr(refresh_component, k, v)
|
|
|
|
return gr.update(**(args or {}))
|
|
|
|
refresh_button = gr.Button(value=refresh_symbol, elem_id=elem_id)
|
|
refresh_button.click(
|
|
fn = refresh,
|
|
inputs = [],
|
|
outputs = [refresh_component]
|
|
)
|
|
return refresh_button
|
|
|
|
with gr.Blocks(analytics_enabled=False) as txt2img_interface:
|
|
txt2img_prompt, roll, txt2img_prompt_style, txt2img_negative_prompt, txt2img_prompt_style2, submit, _, _, txt2img_prompt_style_apply, txt2img_save_style, txt2img_paste, token_counter, token_button = create_toprow(is_img2img=False)
|
|
dummy_component = gr.Label(visible=False)
|
|
txt_prompt_img = gr.File(label="", elem_id="txt2img_prompt_image", file_count="single", type="bytes", visible=False)
|
|
|
|
with gr.Row(elem_id='txt2img_progress_row'):
|
|
with gr.Column(scale=1):
|
|
pass
|
|
|
|
with gr.Column(scale=1):
|
|
progressbar = gr.HTML(elem_id="txt2img_progressbar")
|
|
txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
|
|
setup_progressbar(progressbar, txt2img_preview, 'txt2img')
|
|
|
|
with gr.Row().style(equal_height=False):
|
|
with gr.Column(variant='panel'):
|
|
steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
|
|
sampler_index = gr.Radio(label='Sampling method', elem_id="txt2img_sampling", choices=[x.name for x in samplers], value=samplers[0].name, type="index")
|
|
|
|
with gr.Group():
|
|
width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
|
|
height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
|
|
|
|
with gr.Row():
|
|
restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
|
|
tiling = gr.Checkbox(label='Tiling', value=False)
|
|
enable_hr = gr.Checkbox(label='Highres. fix', value=False)
|
|
|
|
with gr.Row(visible=False) as hr_options:
|
|
firstphase_width = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass width", value=0)
|
|
firstphase_height = gr.Slider(minimum=0, maximum=1024, step=64, label="Firstpass height", value=0)
|
|
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7)
|
|
|
|
with gr.Row(equal_height=True):
|
|
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
|
|
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
|
|
|
|
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
|
|
|
|
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
|
|
|
|
with gr.Group():
|
|
custom_inputs = modules.scripts.scripts_txt2img.setup_ui(is_img2img=False)
|
|
|
|
with gr.Column(variant='panel'):
|
|
|
|
with gr.Group():
|
|
txt2img_preview = gr.Image(elem_id='txt2img_preview', visible=False)
|
|
txt2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='txt2img_gallery').style(grid=4)
|
|
|
|
with gr.Column():
|
|
with gr.Row():
|
|
save = gr.Button('Save')
|
|
send_to_img2img = gr.Button('Send to img2img')
|
|
send_to_inpaint = gr.Button('Send to inpaint')
|
|
send_to_extras = gr.Button('Send to extras')
|
|
button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
|
|
open_txt2img_folder = gr.Button(folder_symbol, elem_id=button_id)
|
|
|
|
with gr.Row():
|
|
do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
|
|
|
|
with gr.Row():
|
|
download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
|
|
|
|
with gr.Group():
|
|
html_info = gr.HTML()
|
|
generation_info = gr.Textbox(visible=False)
|
|
|
|
connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
|
|
connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
|
|
|
|
txt2img_args = dict(
|
|
fn=wrap_gradio_gpu_call(modules.txt2img.txt2img),
|
|
_js="submit",
|
|
inputs=[
|
|
txt2img_prompt,
|
|
txt2img_negative_prompt,
|
|
txt2img_prompt_style,
|
|
txt2img_prompt_style2,
|
|
steps,
|
|
sampler_index,
|
|
restore_faces,
|
|
tiling,
|
|
batch_count,
|
|
batch_size,
|
|
cfg_scale,
|
|
seed,
|
|
subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
|
|
height,
|
|
width,
|
|
enable_hr,
|
|
denoising_strength,
|
|
firstphase_width,
|
|
firstphase_height,
|
|
] + custom_inputs,
|
|
outputs=[
|
|
txt2img_gallery,
|
|
generation_info,
|
|
html_info
|
|
],
|
|
show_progress=False,
|
|
)
|
|
|
|
txt2img_prompt.submit(**txt2img_args)
|
|
submit.click(**txt2img_args)
|
|
|
|
txt_prompt_img.change(
|
|
fn=modules.images.image_data,
|
|
inputs=[
|
|
txt_prompt_img
|
|
],
|
|
outputs=[
|
|
txt2img_prompt,
|
|
txt_prompt_img
|
|
]
|
|
)
|
|
|
|
enable_hr.change(
|
|
fn=lambda x: gr_show(x),
|
|
inputs=[enable_hr],
|
|
outputs=[hr_options],
|
|
)
|
|
|
|
save.click(
|
|
fn=wrap_gradio_call(save_files),
|
|
_js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
|
|
inputs=[
|
|
generation_info,
|
|
txt2img_gallery,
|
|
do_make_zip,
|
|
html_info,
|
|
],
|
|
outputs=[
|
|
download_files,
|
|
html_info,
|
|
html_info,
|
|
html_info,
|
|
]
|
|
)
|
|
|
|
roll.click(
|
|
fn=roll_artist,
|
|
_js="update_txt2img_tokens",
|
|
inputs=[
|
|
txt2img_prompt,
|
|
],
|
|
outputs=[
|
|
txt2img_prompt,
|
|
]
|
|
)
|
|
|
|
txt2img_paste_fields = [
|
|
(txt2img_prompt, "Prompt"),
|
|
(txt2img_negative_prompt, "Negative prompt"),
|
|
(steps, "Steps"),
|
|
(sampler_index, "Sampler"),
|
|
(restore_faces, "Face restoration"),
|
|
(cfg_scale, "CFG scale"),
|
|
(seed, "Seed"),
|
|
(width, "Size-1"),
|
|
(height, "Size-2"),
|
|
(batch_size, "Batch size"),
|
|
(subseed, "Variation seed"),
|
|
(subseed_strength, "Variation seed strength"),
|
|
(seed_resize_from_w, "Seed resize from-1"),
|
|
(seed_resize_from_h, "Seed resize from-2"),
|
|
(denoising_strength, "Denoising strength"),
|
|
(enable_hr, lambda d: "Denoising strength" in d),
|
|
(hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
|
|
(firstphase_width, "First pass size-1"),
|
|
(firstphase_height, "First pass size-2"),
|
|
]
|
|
|
|
txt2img_preview_params = [
|
|
txt2img_prompt,
|
|
txt2img_negative_prompt,
|
|
steps,
|
|
sampler_index,
|
|
cfg_scale,
|
|
seed,
|
|
width,
|
|
height,
|
|
]
|
|
|
|
token_button.click(fn=update_token_counter, inputs=[txt2img_prompt, steps], outputs=[token_counter])
|
|
|
|
with gr.Blocks(analytics_enabled=False) as img2img_interface:
|
|
img2img_prompt, roll, img2img_prompt_style, img2img_negative_prompt, img2img_prompt_style2, submit, img2img_interrogate, img2img_deepbooru, img2img_prompt_style_apply, img2img_save_style, img2img_paste, token_counter, token_button = create_toprow(is_img2img=True)
|
|
|
|
with gr.Row(elem_id='img2img_progress_row'):
|
|
img2img_prompt_img = gr.File(label="", elem_id="img2img_prompt_image", file_count="single", type="bytes", visible=False)
|
|
|
|
with gr.Column(scale=1):
|
|
pass
|
|
|
|
with gr.Column(scale=1):
|
|
progressbar = gr.HTML(elem_id="img2img_progressbar")
|
|
img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
|
|
setup_progressbar(progressbar, img2img_preview, 'img2img')
|
|
|
|
with gr.Row().style(equal_height=False):
|
|
with gr.Column(variant='panel'):
|
|
|
|
with gr.Tabs(elem_id="mode_img2img") as tabs_img2img_mode:
|
|
with gr.TabItem('img2img', id='img2img'):
|
|
init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="pil", tool=cmd_opts.gradio_img2img_tool).style(height=480)
|
|
|
|
with gr.TabItem('Inpaint', id='inpaint'):
|
|
init_img_with_mask = gr.Image(label="Image for inpainting with mask", show_label=False, elem_id="img2maskimg", source="upload", interactive=True, type="pil", tool="sketch", image_mode="RGBA").style(height=480)
|
|
|
|
init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_base")
|
|
init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", visible=False, elem_id="img_inpaint_mask")
|
|
|
|
mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4)
|
|
|
|
with gr.Row():
|
|
mask_mode = gr.Radio(label="Mask mode", show_label=False, choices=["Draw mask", "Upload mask"], type="index", value="Draw mask", elem_id="mask_mode")
|
|
inpainting_mask_invert = gr.Radio(label='Masking mode', show_label=False, choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index")
|
|
|
|
inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index")
|
|
|
|
with gr.Row():
|
|
inpaint_full_res = gr.Checkbox(label='Inpaint at full resolution', value=False)
|
|
inpaint_full_res_padding = gr.Slider(label='Inpaint at full resolution padding, pixels', minimum=0, maximum=256, step=4, value=32)
|
|
|
|
with gr.TabItem('Batch img2img', id='batch'):
|
|
hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
|
|
gr.HTML(f"<p 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)
|
|
img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs)
|
|
|
|
with gr.Row():
|
|
resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", show_label=False, choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize")
|
|
|
|
steps = gr.Slider(minimum=1, maximum=150, step=1, label="Sampling Steps", value=20)
|
|
sampler_index = gr.Radio(label='Sampling method', choices=[x.name for x in samplers_for_img2img], value=samplers_for_img2img[0].name, type="index")
|
|
|
|
with gr.Group():
|
|
width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
|
|
height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
|
|
|
|
with gr.Row():
|
|
restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1)
|
|
tiling = gr.Checkbox(label='Tiling', value=False)
|
|
|
|
with gr.Row():
|
|
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1)
|
|
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1)
|
|
|
|
with gr.Group():
|
|
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0)
|
|
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75)
|
|
|
|
seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs()
|
|
|
|
with gr.Group():
|
|
custom_inputs = modules.scripts.scripts_img2img.setup_ui(is_img2img=True)
|
|
|
|
with gr.Column(variant='panel'):
|
|
|
|
with gr.Group():
|
|
img2img_preview = gr.Image(elem_id='img2img_preview', visible=False)
|
|
img2img_gallery = gr.Gallery(label='Output', show_label=False, elem_id='img2img_gallery').style(grid=4)
|
|
|
|
with gr.Column():
|
|
with gr.Row():
|
|
save = gr.Button('Save')
|
|
img2img_send_to_img2img = gr.Button('Send to img2img')
|
|
img2img_send_to_inpaint = gr.Button('Send to inpaint')
|
|
img2img_send_to_extras = gr.Button('Send to extras')
|
|
button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else 'open_folder'
|
|
open_img2img_folder = gr.Button(folder_symbol, elem_id=button_id)
|
|
|
|
with gr.Row():
|
|
do_make_zip = gr.Checkbox(label="Make Zip when Save?", value=False)
|
|
|
|
with gr.Row():
|
|
download_files = gr.File(None, file_count="multiple", interactive=False, show_label=False, visible=False)
|
|
|
|
with gr.Group():
|
|
html_info = gr.HTML()
|
|
generation_info = gr.Textbox(visible=False)
|
|
|
|
connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False)
|
|
connect_reuse_seed(subseed, reuse_subseed, generation_info, dummy_component, is_subseed=True)
|
|
|
|
img2img_prompt_img.change(
|
|
fn=modules.images.image_data,
|
|
inputs=[
|
|
img2img_prompt_img
|
|
],
|
|
outputs=[
|
|
img2img_prompt,
|
|
img2img_prompt_img
|
|
]
|
|
)
|
|
|
|
mask_mode.change(
|
|
lambda mode, img: {
|
|
init_img_with_mask: gr_show(mode == 0),
|
|
init_img_inpaint: gr_show(mode == 1),
|
|
init_mask_inpaint: gr_show(mode == 1),
|
|
},
|
|
inputs=[mask_mode, init_img_with_mask],
|
|
outputs=[
|
|
init_img_with_mask,
|
|
init_img_inpaint,
|
|
init_mask_inpaint,
|
|
],
|
|
)
|
|
|
|
img2img_args = dict(
|
|
fn=wrap_gradio_gpu_call(modules.img2img.img2img),
|
|
_js="submit_img2img",
|
|
inputs=[
|
|
dummy_component,
|
|
img2img_prompt,
|
|
img2img_negative_prompt,
|
|
img2img_prompt_style,
|
|
img2img_prompt_style2,
|
|
init_img,
|
|
init_img_with_mask,
|
|
init_img_inpaint,
|
|
init_mask_inpaint,
|
|
mask_mode,
|
|
steps,
|
|
sampler_index,
|
|
mask_blur,
|
|
inpainting_fill,
|
|
restore_faces,
|
|
tiling,
|
|
batch_count,
|
|
batch_size,
|
|
cfg_scale,
|
|
denoising_strength,
|
|
seed,
|
|
subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox,
|
|
height,
|
|
width,
|
|
resize_mode,
|
|
inpaint_full_res,
|
|
inpaint_full_res_padding,
|
|
inpainting_mask_invert,
|
|
img2img_batch_input_dir,
|
|
img2img_batch_output_dir,
|
|
] + custom_inputs,
|
|
outputs=[
|
|
img2img_gallery,
|
|
generation_info,
|
|
html_info
|
|
],
|
|
show_progress=False,
|
|
)
|
|
|
|
img2img_prompt.submit(**img2img_args)
|
|
submit.click(**img2img_args)
|
|
|
|
img2img_interrogate.click(
|
|
fn=interrogate,
|
|
inputs=[init_img],
|
|
outputs=[img2img_prompt],
|
|
)
|
|
|
|
if cmd_opts.deepdanbooru:
|
|
img2img_deepbooru.click(
|
|
fn=interrogate_deepbooru,
|
|
inputs=[init_img],
|
|
outputs=[img2img_prompt],
|
|
)
|
|
|
|
save.click(
|
|
fn=wrap_gradio_call(save_files),
|
|
_js="(x, y, z, w) => [x, y, z, selected_gallery_index()]",
|
|
inputs=[
|
|
generation_info,
|
|
img2img_gallery,
|
|
do_make_zip,
|
|
html_info,
|
|
],
|
|
outputs=[
|
|
download_files,
|
|
html_info,
|
|
html_info,
|
|
html_info,
|
|
]
|
|
)
|
|
|
|
roll.click(
|
|
fn=roll_artist,
|
|
_js="update_img2img_tokens",
|
|
inputs=[
|
|
img2img_prompt,
|
|
],
|
|
outputs=[
|
|
img2img_prompt,
|
|
]
|
|
)
|
|
|
|
prompts = [(txt2img_prompt, txt2img_negative_prompt), (img2img_prompt, img2img_negative_prompt)]
|
|
style_dropdowns = [(txt2img_prompt_style, txt2img_prompt_style2), (img2img_prompt_style, img2img_prompt_style2)]
|
|
style_js_funcs = ["update_txt2img_tokens", "update_img2img_tokens"]
|
|
|
|
for button, (prompt, negative_prompt) in zip([txt2img_save_style, img2img_save_style], prompts):
|
|
button.click(
|
|
fn=add_style,
|
|
_js="ask_for_style_name",
|
|
# Have to pass empty dummy component here, because the JavaScript and Python function have to accept
|
|
# the same number of parameters, but we only know the style-name after the JavaScript prompt
|
|
inputs=[dummy_component, prompt, negative_prompt],
|
|
outputs=[txt2img_prompt_style, img2img_prompt_style, txt2img_prompt_style2, img2img_prompt_style2],
|
|
)
|
|
|
|
for button, (prompt, negative_prompt), (style1, style2), js_func in zip([txt2img_prompt_style_apply, img2img_prompt_style_apply], prompts, style_dropdowns, style_js_funcs):
|
|
button.click(
|
|
fn=apply_styles,
|
|
_js=js_func,
|
|
inputs=[prompt, negative_prompt, style1, style2],
|
|
outputs=[prompt, negative_prompt, style1, style2],
|
|
)
|
|
|
|
img2img_paste_fields = [
|
|
(img2img_prompt, "Prompt"),
|
|
(img2img_negative_prompt, "Negative prompt"),
|
|
(steps, "Steps"),
|
|
(sampler_index, "Sampler"),
|
|
(restore_faces, "Face restoration"),
|
|
(cfg_scale, "CFG scale"),
|
|
(seed, "Seed"),
|
|
(width, "Size-1"),
|
|
(height, "Size-2"),
|
|
(batch_size, "Batch size"),
|
|
(subseed, "Variation seed"),
|
|
(subseed_strength, "Variation seed strength"),
|
|
(seed_resize_from_w, "Seed resize from-1"),
|
|
(seed_resize_from_h, "Seed resize from-2"),
|
|
(denoising_strength, "Denoising strength"),
|
|
]
|
|
token_button.click(fn=update_token_counter, inputs=[img2img_prompt, steps], outputs=[token_counter])
|
|
|
|
with gr.Blocks(analytics_enabled=False) as extras_interface:
|
|
with gr.Row().style(equal_height=False):
|
|
with gr.Column(variant='panel'):
|
|
with gr.Tabs(elem_id="mode_extras"):
|
|
with gr.TabItem('Single Image'):
|
|
extras_image = gr.Image(label="Source", source="upload", interactive=True, type="pil")
|
|
|
|
with gr.TabItem('Batch Process'):
|
|
image_batch = gr.File(label="Batch Process", file_count="multiple", interactive=True, type="file")
|
|
|
|
with gr.TabItem('Batch from Directory'):
|
|
extras_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs,
|
|
placeholder="A directory on the same machine where the server is running."
|
|
)
|
|
extras_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs,
|
|
placeholder="Leave blank to save images to the default path."
|
|
)
|
|
show_extras_results = gr.Checkbox(label='Show result images', value=True)
|
|
|
|
with gr.Tabs(elem_id="extras_resize_mode"):
|
|
with gr.TabItem('Scale by'):
|
|
upscaling_resize = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Resize", value=2)
|
|
with gr.TabItem('Scale to'):
|
|
with gr.Group():
|
|
with gr.Row():
|
|
upscaling_resize_w = gr.Number(label="Width", value=512, precision=0)
|
|
upscaling_resize_h = gr.Number(label="Height", value=512, precision=0)
|
|
upscaling_crop = gr.Checkbox(label='Crop to fit', value=True)
|
|
|
|
with gr.Group():
|
|
extras_upscaler_1 = gr.Radio(label='Upscaler 1', elem_id="extras_upscaler_1", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
|
|
|
|
with gr.Group():
|
|
extras_upscaler_2 = gr.Radio(label='Upscaler 2', elem_id="extras_upscaler_2", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index")
|
|
extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="Upscaler 2 visibility", value=1)
|
|
|
|
with gr.Group():
|
|
gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="GFPGAN visibility", value=0, interactive=modules.gfpgan_model.have_gfpgan)
|
|
|
|
with gr.Group():
|
|
codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer visibility", value=0, interactive=modules.codeformer_model.have_codeformer)
|
|
codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label="CodeFormer weight (0 = maximum effect, 1 = minimum effect)", value=0, interactive=modules.codeformer_model.have_codeformer)
|
|
|
|
submit = gr.Button('Generate', elem_id="extras_generate", variant='primary')
|
|
|
|
with gr.Column(variant='panel'):
|
|
result_images = gr.Gallery(label="Result", show_label=False)
|
|
html_info_x = gr.HTML()
|
|
html_info = gr.HTML()
|
|
extras_send_to_img2img = gr.Button('Send to img2img')
|
|
extras_send_to_inpaint = gr.Button('Send to inpaint')
|
|
button_id = "hidden_element" if shared.cmd_opts.hide_ui_dir_config else ''
|
|
open_extras_folder = gr.Button('Open output directory', elem_id=button_id)
|
|
|
|
|
|
submit.click(
|
|
fn=wrap_gradio_gpu_call(modules.extras.run_extras),
|
|
_js="get_extras_tab_index",
|
|
inputs=[
|
|
dummy_component,
|
|
dummy_component,
|
|
extras_image,
|
|
image_batch,
|
|
extras_batch_input_dir,
|
|
extras_batch_output_dir,
|
|
show_extras_results,
|
|
gfpgan_visibility,
|
|
codeformer_visibility,
|
|
codeformer_weight,
|
|
upscaling_resize,
|
|
upscaling_resize_w,
|
|
upscaling_resize_h,
|
|
upscaling_crop,
|
|
extras_upscaler_1,
|
|
extras_upscaler_2,
|
|
extras_upscaler_2_visibility,
|
|
],
|
|
outputs=[
|
|
result_images,
|
|
html_info_x,
|
|
html_info,
|
|
]
|
|
)
|
|
|
|
extras_send_to_img2img.click(
|
|
fn=lambda x: image_from_url_text(x),
|
|
_js="extract_image_from_gallery_img2img",
|
|
inputs=[result_images],
|
|
outputs=[init_img],
|
|
)
|
|
|
|
extras_send_to_inpaint.click(
|
|
fn=lambda x: image_from_url_text(x),
|
|
_js="extract_image_from_gallery_inpaint",
|
|
inputs=[result_images],
|
|
outputs=[init_img_with_mask],
|
|
)
|
|
|
|
with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
|
|
with gr.Row().style(equal_height=False):
|
|
with gr.Column(variant='panel'):
|
|
image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil")
|
|
|
|
with gr.Column(variant='panel'):
|
|
html = gr.HTML()
|
|
generation_info = gr.Textbox(visible=False)
|
|
html2 = gr.HTML()
|
|
|
|
with gr.Row():
|
|
pnginfo_send_to_txt2img = gr.Button('Send to txt2img')
|
|
pnginfo_send_to_img2img = gr.Button('Send to img2img')
|
|
|
|
image.change(
|
|
fn=wrap_gradio_call(modules.extras.run_pnginfo),
|
|
inputs=[image],
|
|
outputs=[html, generation_info, html2],
|
|
)
|
|
#images history
|
|
images_history_switch_dict = {
|
|
"fn":modules.generation_parameters_copypaste.connect_paste,
|
|
"t2i":txt2img_paste_fields,
|
|
"i2i":img2img_paste_fields
|
|
}
|
|
|
|
images_history = img_his.create_history_tabs(gr, opts, wrap_gradio_call(modules.extras.run_pnginfo), images_history_switch_dict)
|
|
|
|
with gr.Blocks() as modelmerger_interface:
|
|
with gr.Row().style(equal_height=False):
|
|
with gr.Column(variant='panel'):
|
|
gr.HTML(value="<p>A merger of the two checkpoints will be generated in your <b>checkpoint</b> directory.</p>")
|
|
|
|
with gr.Row():
|
|
primary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_primary_model_name", label="Primary model (A)")
|
|
secondary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_secondary_model_name", label="Secondary model (B)")
|
|
tertiary_model_name = gr.Dropdown(modules.sd_models.checkpoint_tiles(), elem_id="modelmerger_tertiary_model_name", label="Tertiary model (C)")
|
|
custom_name = gr.Textbox(label="Custom Name (Optional)")
|
|
interp_amount = gr.Slider(minimum=0.0, maximum=1.0, step=0.05, label='Multiplier (M) - set to 0 to get model A', value=0.3)
|
|
interp_method = gr.Radio(choices=["Weighted sum", "Add difference"], value="Weighted sum", label="Interpolation Method")
|
|
save_as_half = gr.Checkbox(value=False, label="Save as float16")
|
|
modelmerger_merge = gr.Button(elem_id="modelmerger_merge", label="Merge", variant='primary')
|
|
|
|
with gr.Column(variant='panel'):
|
|
submit_result = gr.Textbox(elem_id="modelmerger_result", show_label=False)
|
|
|
|
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
|
|
|
|
with gr.Blocks() as train_interface:
|
|
with gr.Row().style(equal_height=False):
|
|
gr.HTML(value="<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")
|
|
initialization_text = gr.Textbox(label="Initialization text", value="*")
|
|
nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
|
|
overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding")
|
|
|
|
with gr.Row():
|
|
with gr.Column(scale=3):
|
|
gr.HTML(value="")
|
|
|
|
with gr.Column():
|
|
create_embedding = gr.Button(value="Create embedding", variant='primary')
|
|
|
|
with gr.Tab(label="Create hypernetwork"):
|
|
new_hypernetwork_name = gr.Textbox(label="Name")
|
|
new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
|
|
new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'")
|
|
new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization")
|
|
overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork")
|
|
|
|
with gr.Row():
|
|
with gr.Column(scale=3):
|
|
gr.HTML(value="")
|
|
|
|
with gr.Column():
|
|
create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
|
|
|
|
with gr.Tab(label="Preprocess images"):
|
|
process_src = gr.Textbox(label='Source directory')
|
|
process_dst = gr.Textbox(label='Destination directory')
|
|
process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
|
|
process_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
|
|
|
|
with gr.Row():
|
|
process_flip = gr.Checkbox(label='Create flipped copies')
|
|
process_split = gr.Checkbox(label='Split oversized images into two')
|
|
process_caption = gr.Checkbox(label='Use BLIP for caption')
|
|
process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True if cmd_opts.deepdanbooru else False)
|
|
|
|
with gr.Row():
|
|
with gr.Column(scale=3):
|
|
gr.HTML(value="")
|
|
|
|
with gr.Column():
|
|
run_preprocess = gr.Button(value="Preprocess", variant='primary')
|
|
|
|
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 gr.Row():
|
|
train_embedding_name = gr.Dropdown(label='Embedding', elem_id="train_embedding", choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
|
|
create_refresh_button(train_embedding_name, sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings, lambda: {"choices": sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys())}, "refresh_train_embedding_name")
|
|
with gr.Row():
|
|
train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()])
|
|
create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name")
|
|
with gr.Row():
|
|
embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005")
|
|
hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001")
|
|
|
|
batch_size = gr.Number(label='Batch size', value=1, precision=0)
|
|
dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
|
|
log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
|
|
template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
|
|
training_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
|
|
training_height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height", value=512)
|
|
steps = gr.Number(label='Max steps', value=100000, precision=0)
|
|
create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
|
|
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
|
|
save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True)
|
|
preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False)
|
|
|
|
with gr.Row():
|
|
interrupt_training = gr.Button(value="Interrupt")
|
|
train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary')
|
|
train_embedding = gr.Button(value="Train Embedding", variant='primary')
|
|
|
|
with gr.Column():
|
|
progressbar = gr.HTML(elem_id="ti_progressbar")
|
|
ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
|
|
|
|
ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(grid=4)
|
|
ti_preview = gr.Image(elem_id='ti_preview', visible=False)
|
|
ti_progress = gr.HTML(elem_id="ti_progress", value="")
|
|
ti_outcome = gr.HTML(elem_id="ti_error", value="")
|
|
setup_progressbar(progressbar, ti_preview, 'ti', textinfo=ti_progress)
|
|
|
|
create_embedding.click(
|
|
fn=modules.textual_inversion.ui.create_embedding,
|
|
inputs=[
|
|
new_embedding_name,
|
|
initialization_text,
|
|
nvpt,
|
|
overwrite_old_embedding,
|
|
],
|
|
outputs=[
|
|
train_embedding_name,
|
|
ti_output,
|
|
ti_outcome,
|
|
]
|
|
)
|
|
|
|
create_hypernetwork.click(
|
|
fn=modules.hypernetworks.ui.create_hypernetwork,
|
|
inputs=[
|
|
new_hypernetwork_name,
|
|
new_hypernetwork_sizes,
|
|
new_hypernetwork_layer_structure,
|
|
new_hypernetwork_add_layer_norm,
|
|
overwrite_old_hypernetwork,
|
|
],
|
|
outputs=[
|
|
train_hypernetwork_name,
|
|
ti_output,
|
|
ti_outcome,
|
|
]
|
|
)
|
|
|
|
run_preprocess.click(
|
|
fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.preprocess, extra_outputs=[gr.update()]),
|
|
_js="start_training_textual_inversion",
|
|
inputs=[
|
|
process_src,
|
|
process_dst,
|
|
process_width,
|
|
process_height,
|
|
process_flip,
|
|
process_split,
|
|
process_caption,
|
|
process_caption_deepbooru
|
|
],
|
|
outputs=[
|
|
ti_output,
|
|
ti_outcome,
|
|
],
|
|
)
|
|
|
|
train_embedding.click(
|
|
fn=wrap_gradio_gpu_call(modules.textual_inversion.ui.train_embedding, extra_outputs=[gr.update()]),
|
|
_js="start_training_textual_inversion",
|
|
inputs=[
|
|
train_embedding_name,
|
|
embedding_learn_rate,
|
|
batch_size,
|
|
dataset_directory,
|
|
log_directory,
|
|
training_width,
|
|
training_height,
|
|
steps,
|
|
create_image_every,
|
|
save_embedding_every,
|
|
template_file,
|
|
save_image_with_stored_embedding,
|
|
preview_from_txt2img,
|
|
*txt2img_preview_params,
|
|
],
|
|
outputs=[
|
|
ti_output,
|
|
ti_outcome,
|
|
]
|
|
)
|
|
|
|
train_hypernetwork.click(
|
|
fn=wrap_gradio_gpu_call(modules.hypernetworks.ui.train_hypernetwork, extra_outputs=[gr.update()]),
|
|
_js="start_training_textual_inversion",
|
|
inputs=[
|
|
train_hypernetwork_name,
|
|
hypernetwork_learn_rate,
|
|
batch_size,
|
|
dataset_directory,
|
|
log_directory,
|
|
training_width,
|
|
training_height,
|
|
steps,
|
|
create_image_every,
|
|
save_embedding_every,
|
|
template_file,
|
|
preview_from_txt2img,
|
|
*txt2img_preview_params,
|
|
],
|
|
outputs=[
|
|
ti_output,
|
|
ti_outcome,
|
|
]
|
|
)
|
|
|
|
interrupt_training.click(
|
|
fn=lambda: shared.state.interrupt(),
|
|
inputs=[],
|
|
outputs=[],
|
|
)
|
|
|
|
def create_setting_component(key, is_quicksettings=False):
|
|
def fun():
|
|
return opts.data[key] if key in opts.data else opts.data_labels[key].default
|
|
|
|
info = opts.data_labels[key]
|
|
t = type(info.default)
|
|
|
|
args = info.component_args() if callable(info.component_args) else info.component_args
|
|
|
|
if info.component is not None:
|
|
comp = info.component
|
|
elif t == str:
|
|
comp = gr.Textbox
|
|
elif t == int:
|
|
comp = gr.Number
|
|
elif t == bool:
|
|
comp = gr.Checkbox
|
|
else:
|
|
raise Exception(f'bad options item type: {str(t)} for key {key}')
|
|
|
|
elem_id = "setting_"+key
|
|
|
|
if info.refresh is not None:
|
|
if is_quicksettings:
|
|
res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))
|
|
create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
|
|
else:
|
|
with gr.Row(variant="compact"):
|
|
res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))
|
|
create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key)
|
|
else:
|
|
res = comp(label=info.label, value=fun, elem_id=elem_id, **(args or {}))
|
|
|
|
|
|
return res
|
|
|
|
components = []
|
|
component_dict = {}
|
|
|
|
def open_folder(f):
|
|
if not os.path.exists(f):
|
|
print(f'Folder "{f}" does not exist. After you create an image, the folder will be created.')
|
|
return
|
|
elif not os.path.isdir(f):
|
|
print(f"""
|
|
WARNING
|
|
An open_folder request was made with an argument that is not a folder.
|
|
This could be an error or a malicious attempt to run code on your computer.
|
|
Requested path was: {f}
|
|
""", file=sys.stderr)
|
|
return
|
|
|
|
if not shared.cmd_opts.hide_ui_dir_config:
|
|
path = os.path.normpath(f)
|
|
if platform.system() == "Windows":
|
|
os.startfile(path)
|
|
elif platform.system() == "Darwin":
|
|
sp.Popen(["open", path])
|
|
else:
|
|
sp.Popen(["xdg-open", path])
|
|
|
|
def run_settings(*args):
|
|
changed = 0
|
|
|
|
for key, value, comp in zip(opts.data_labels.keys(), args, components):
|
|
if comp != dummy_component and not opts.same_type(value, opts.data_labels[key].default):
|
|
return f"Bad value for setting {key}: {value}; expecting {type(opts.data_labels[key].default).__name__}", opts.dumpjson()
|
|
|
|
for key, value, comp in zip(opts.data_labels.keys(), args, components):
|
|
if comp == dummy_component:
|
|
continue
|
|
|
|
comp_args = opts.data_labels[key].component_args
|
|
if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
|
|
continue
|
|
|
|
if cmd_opts.hide_ui_dir_config and key in restricted_opts:
|
|
continue
|
|
|
|
oldval = opts.data.get(key, None)
|
|
opts.data[key] = value
|
|
|
|
if oldval != value:
|
|
if opts.data_labels[key].onchange is not None:
|
|
opts.data_labels[key].onchange()
|
|
|
|
changed += 1
|
|
|
|
opts.save(shared.config_filename)
|
|
|
|
return f'{changed} settings changed.', opts.dumpjson()
|
|
|
|
def run_settings_single(value, key):
|
|
if not opts.same_type(value, opts.data_labels[key].default):
|
|
return gr.update(visible=True), opts.dumpjson()
|
|
|
|
if cmd_opts.hide_ui_dir_config and key in restricted_opts:
|
|
return gr.update(value=oldval), opts.dumpjson()
|
|
|
|
oldval = opts.data.get(key, None)
|
|
opts.data[key] = value
|
|
|
|
if oldval != value:
|
|
if opts.data_labels[key].onchange is not None:
|
|
opts.data_labels[key].onchange()
|
|
|
|
opts.save(shared.config_filename)
|
|
|
|
return gr.update(value=value), opts.dumpjson()
|
|
|
|
with gr.Blocks(analytics_enabled=False) as settings_interface:
|
|
settings_submit = gr.Button(value="Apply settings", variant='primary')
|
|
result = gr.HTML()
|
|
|
|
settings_cols = 3
|
|
items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols)
|
|
|
|
quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")]
|
|
quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings')
|
|
|
|
quicksettings_list = []
|
|
|
|
cols_displayed = 0
|
|
items_displayed = 0
|
|
previous_section = None
|
|
column = None
|
|
with gr.Row(elem_id="settings").style(equal_height=False):
|
|
for i, (k, item) in enumerate(opts.data_labels.items()):
|
|
|
|
if previous_section != item.section:
|
|
if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None):
|
|
if column is not None:
|
|
column.__exit__()
|
|
|
|
column = gr.Column(variant='panel')
|
|
column.__enter__()
|
|
|
|
items_displayed = 0
|
|
cols_displayed += 1
|
|
|
|
previous_section = item.section
|
|
|
|
gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))
|
|
|
|
if k in quicksettings_names:
|
|
quicksettings_list.append((i, k, item))
|
|
components.append(dummy_component)
|
|
else:
|
|
component = create_setting_component(k)
|
|
component_dict[k] = component
|
|
components.append(component)
|
|
items_displayed += 1
|
|
|
|
with gr.Row():
|
|
request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
|
|
download_localization = gr.Button(value='Download localization template', elem_id="download_localization")
|
|
|
|
with gr.Row():
|
|
reload_script_bodies = gr.Button(value='Reload custom script bodies (No ui updates, No restart)', variant='secondary')
|
|
restart_gradio = gr.Button(value='Restart Gradio and Refresh components (Custom Scripts, ui.py, js and css only)', variant='primary')
|
|
|
|
request_notifications.click(
|
|
fn=lambda: None,
|
|
inputs=[],
|
|
outputs=[],
|
|
_js='function(){}'
|
|
)
|
|
|
|
download_localization.click(
|
|
fn=lambda: None,
|
|
inputs=[],
|
|
outputs=[],
|
|
_js='download_localization'
|
|
)
|
|
|
|
def reload_scripts():
|
|
modules.scripts.reload_script_body_only()
|
|
reload_javascript() # need to refresh the html page
|
|
|
|
reload_script_bodies.click(
|
|
fn=reload_scripts,
|
|
inputs=[],
|
|
outputs=[],
|
|
_js='function(){}'
|
|
)
|
|
|
|
def request_restart():
|
|
shared.state.interrupt()
|
|
settings_interface.gradio_ref.do_restart = True
|
|
|
|
restart_gradio.click(
|
|
fn=request_restart,
|
|
inputs=[],
|
|
outputs=[],
|
|
_js='function(){restart_reload()}'
|
|
)
|
|
|
|
if column is not None:
|
|
column.__exit__()
|
|
|
|
interfaces = [
|
|
(txt2img_interface, "txt2img", "txt2img"),
|
|
(img2img_interface, "img2img", "img2img"),
|
|
(extras_interface, "Extras", "extras"),
|
|
(pnginfo_interface, "PNG Info", "pnginfo"),
|
|
(images_history, "History", "images_history"),
|
|
(modelmerger_interface, "Checkpoint Merger", "modelmerger"),
|
|
(train_interface, "Train", "ti"),
|
|
(settings_interface, "Settings", "settings"),
|
|
]
|
|
|
|
with open(os.path.join(script_path, "style.css"), "r", encoding="utf8") as file:
|
|
css = file.read()
|
|
|
|
if os.path.exists(os.path.join(script_path, "user.css")):
|
|
with open(os.path.join(script_path, "user.css"), "r", encoding="utf8") as file:
|
|
usercss = file.read()
|
|
css += usercss
|
|
|
|
if not cmd_opts.no_progressbar_hiding:
|
|
css += css_hide_progressbar
|
|
|
|
with gr.Blocks(css=css, analytics_enabled=False, title="Stable Diffusion") as demo:
|
|
with gr.Row(elem_id="quicksettings"):
|
|
for i, k, item in quicksettings_list:
|
|
component = create_setting_component(k, is_quicksettings=True)
|
|
component_dict[k] = component
|
|
|
|
settings_interface.gradio_ref = demo
|
|
|
|
with gr.Tabs(elem_id="tabs") as tabs:
|
|
for interface, label, ifid in interfaces:
|
|
with gr.TabItem(label, id=ifid, elem_id='tab_' + ifid):
|
|
interface.render()
|
|
|
|
if os.path.exists(os.path.join(script_path, "notification.mp3")):
|
|
audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
|
|
|
|
text_settings = gr.Textbox(elem_id="settings_json", value=lambda: opts.dumpjson(), visible=False)
|
|
settings_submit.click(
|
|
fn=run_settings,
|
|
inputs=components,
|
|
outputs=[result, text_settings],
|
|
)
|
|
|
|
for i, k, item in quicksettings_list:
|
|
component = component_dict[k]
|
|
|
|
component.change(
|
|
fn=lambda value, k=k: run_settings_single(value, key=k),
|
|
inputs=[component],
|
|
outputs=[component, text_settings],
|
|
)
|
|
|
|
def modelmerger(*args):
|
|
try:
|
|
results = modules.extras.run_modelmerger(*args)
|
|
except Exception as e:
|
|
print("Error loading/saving model file:", file=sys.stderr)
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
modules.sd_models.list_models() # to remove the potentially missing models from the list
|
|
return ["Error loading/saving model file. It doesn't exist or the name contains illegal characters"] + [gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(3)]
|
|
return results
|
|
|
|
modelmerger_merge.click(
|
|
fn=modelmerger,
|
|
inputs=[
|
|
primary_model_name,
|
|
secondary_model_name,
|
|
tertiary_model_name,
|
|
interp_method,
|
|
interp_amount,
|
|
save_as_half,
|
|
custom_name,
|
|
],
|
|
outputs=[
|
|
submit_result,
|
|
primary_model_name,
|
|
secondary_model_name,
|
|
tertiary_model_name,
|
|
component_dict['sd_model_checkpoint'],
|
|
]
|
|
)
|
|
paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration', 'Seed', 'Size-1', 'Size-2']
|
|
txt2img_fields = [field for field,name in txt2img_paste_fields if name in paste_field_names]
|
|
img2img_fields = [field for field,name in img2img_paste_fields if name in paste_field_names]
|
|
send_to_img2img.click(
|
|
fn=lambda img, *args: (image_from_url_text(img),*args),
|
|
_js="(gallery, ...args) => [extract_image_from_gallery_img2img(gallery), ...args]",
|
|
inputs=[txt2img_gallery] + txt2img_fields,
|
|
outputs=[init_img] + img2img_fields,
|
|
)
|
|
|
|
send_to_inpaint.click(
|
|
fn=lambda x, *args: (image_from_url_text(x), *args),
|
|
_js="(gallery, ...args) => [extract_image_from_gallery_inpaint(gallery), ...args]",
|
|
inputs=[txt2img_gallery] + txt2img_fields,
|
|
outputs=[init_img_with_mask] + img2img_fields,
|
|
)
|
|
|
|
img2img_send_to_img2img.click(
|
|
fn=lambda x: image_from_url_text(x),
|
|
_js="extract_image_from_gallery_img2img",
|
|
inputs=[img2img_gallery],
|
|
outputs=[init_img],
|
|
)
|
|
|
|
img2img_send_to_inpaint.click(
|
|
fn=lambda x: image_from_url_text(x),
|
|
_js="extract_image_from_gallery_inpaint",
|
|
inputs=[img2img_gallery],
|
|
outputs=[init_img_with_mask],
|
|
)
|
|
|
|
send_to_extras.click(
|
|
fn=lambda x: image_from_url_text(x),
|
|
_js="extract_image_from_gallery_extras",
|
|
inputs=[txt2img_gallery],
|
|
outputs=[extras_image],
|
|
)
|
|
|
|
open_txt2img_folder.click(
|
|
fn=lambda: open_folder(opts.outdir_samples or opts.outdir_txt2img_samples),
|
|
inputs=[],
|
|
outputs=[],
|
|
)
|
|
|
|
open_img2img_folder.click(
|
|
fn=lambda: open_folder(opts.outdir_samples or opts.outdir_img2img_samples),
|
|
inputs=[],
|
|
outputs=[],
|
|
)
|
|
|
|
open_extras_folder.click(
|
|
fn=lambda: open_folder(opts.outdir_samples or opts.outdir_extras_samples),
|
|
inputs=[],
|
|
outputs=[],
|
|
)
|
|
|
|
img2img_send_to_extras.click(
|
|
fn=lambda x: image_from_url_text(x),
|
|
_js="extract_image_from_gallery_extras",
|
|
inputs=[img2img_gallery],
|
|
outputs=[extras_image],
|
|
)
|
|
|
|
settings_map = {
|
|
'sd_hypernetwork': 'Hypernet',
|
|
'CLIP_stop_at_last_layers': 'Clip skip',
|
|
'sd_model_checkpoint': 'Model hash',
|
|
}
|
|
|
|
settings_paste_fields = [
|
|
(component_dict[k], lambda d, k=k, v=v: apply_setting(k, d.get(v, None)))
|
|
for k, v in settings_map.items()
|
|
]
|
|
|
|
modules.generation_parameters_copypaste.connect_paste(txt2img_paste, txt2img_paste_fields + settings_paste_fields, txt2img_prompt)
|
|
modules.generation_parameters_copypaste.connect_paste(img2img_paste, img2img_paste_fields + settings_paste_fields, img2img_prompt)
|
|
|
|
modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_txt2img, txt2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_txt2img')
|
|
modules.generation_parameters_copypaste.connect_paste(pnginfo_send_to_img2img, img2img_paste_fields + settings_paste_fields, generation_info, 'switch_to_img2img_img2img')
|
|
|
|
ui_config_file = cmd_opts.ui_config_file
|
|
ui_settings = {}
|
|
settings_count = len(ui_settings)
|
|
error_loading = False
|
|
|
|
try:
|
|
if os.path.exists(ui_config_file):
|
|
with open(ui_config_file, "r", encoding="utf8") as file:
|
|
ui_settings = json.load(file)
|
|
except Exception:
|
|
error_loading = True
|
|
print("Error loading settings:", file=sys.stderr)
|
|
print(traceback.format_exc(), file=sys.stderr)
|
|
|
|
def loadsave(path, x):
|
|
def apply_field(obj, field, condition=None, init_field=None):
|
|
key = path + "/" + field
|
|
|
|
if getattr(obj,'custom_script_source',None) is not None:
|
|
key = 'customscript/' + obj.custom_script_source + '/' + key
|
|
|
|
if getattr(obj, 'do_not_save_to_config', False):
|
|
return
|
|
|
|
saved_value = ui_settings.get(key, None)
|
|
if saved_value is None:
|
|
ui_settings[key] = getattr(obj, field)
|
|
elif condition and not condition(saved_value):
|
|
print(f'Warning: Bad ui setting value: {key}: {saved_value}; Default value "{getattr(obj, field)}" will be used instead.')
|
|
else:
|
|
setattr(obj, field, saved_value)
|
|
if init_field is not None:
|
|
init_field(saved_value)
|
|
|
|
if type(x) in [gr.Slider, gr.Radio, gr.Checkbox, gr.Textbox, gr.Number] and x.visible:
|
|
apply_field(x, 'visible')
|
|
|
|
if type(x) == gr.Slider:
|
|
apply_field(x, 'value')
|
|
apply_field(x, 'minimum')
|
|
apply_field(x, 'maximum')
|
|
apply_field(x, 'step')
|
|
|
|
if type(x) == gr.Radio:
|
|
apply_field(x, 'value', lambda val: val in x.choices)
|
|
|
|
if type(x) == gr.Checkbox:
|
|
apply_field(x, 'value')
|
|
|
|
if type(x) == gr.Textbox:
|
|
apply_field(x, 'value')
|
|
|
|
if type(x) == gr.Number:
|
|
apply_field(x, 'value')
|
|
|
|
# Since there are many dropdowns that shouldn't be saved,
|
|
# we only mark dropdowns that should be saved.
|
|
if type(x) == gr.Dropdown and getattr(x, 'save_to_config', False):
|
|
apply_field(x, 'value', lambda val: val in x.choices, getattr(x, 'init_field', None))
|
|
apply_field(x, 'visible')
|
|
|
|
visit(txt2img_interface, loadsave, "txt2img")
|
|
visit(img2img_interface, loadsave, "img2img")
|
|
visit(extras_interface, loadsave, "extras")
|
|
visit(modelmerger_interface, loadsave, "modelmerger")
|
|
|
|
if not error_loading and (not os.path.exists(ui_config_file) or settings_count != len(ui_settings)):
|
|
with open(ui_config_file, "w", encoding="utf8") as file:
|
|
json.dump(ui_settings, file, indent=4)
|
|
|
|
return demo
|
|
|
|
|
|
def load_javascript(raw_response):
|
|
with open(os.path.join(script_path, "script.js"), "r", encoding="utf8") as jsfile:
|
|
javascript = f'<script>{jsfile.read()}</script>'
|
|
|
|
jsdir = os.path.join(script_path, "javascript")
|
|
for filename in sorted(os.listdir(jsdir)):
|
|
with open(os.path.join(jsdir, filename), "r", encoding="utf8") as jsfile:
|
|
javascript += f"\n<!-- {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 = raw_response(*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
|
|
|
|
|
|
reload_javascript = partial(load_javascript,
|
|
gradio.routes.templates.TemplateResponse)
|
|
reload_javascript()
|