Unverified Commit d0d74e45 authored by papuSpartan's avatar papuSpartan Committed by GitHub

Merge branch 'AUTOMATIC1111:master' into js

parents 401350cd 198a1ffc
......@@ -21,4 +21,15 @@ function extensions_check(){
})
return []
}
\ No newline at end of file
}
function install_extension_from_index(button, url){
button.disabled = "disabled"
button.value = "Installing..."
textarea = gradioApp().querySelector('#extension_to_install textarea')
textarea.value = url
textarea.dispatchEvent(new Event("input", { bubbles: true }))
gradioApp().querySelector('#install_extension_button').click()
}
......@@ -7,6 +7,7 @@ import shlex
import platform
dir_repos = "repositories"
dir_extensions = "extensions"
python = sys.executable
git = os.environ.get('GIT', "git")
index_url = os.environ.get('INDEX_URL', "")
......@@ -16,11 +17,11 @@ def extract_arg(args, name):
return [x for x in args if x != name], name in args
def run(command, desc=None, errdesc=None):
def run(command, desc=None, errdesc=None, custom_env=None):
if desc is not None:
print(desc)
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)
result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ if custom_env is None else custom_env)
if result.returncode != 0:
......@@ -101,9 +102,27 @@ def version_check(commit):
else:
print("Not a git clone, can't perform version check.")
except Exception as e:
print("versipm check failed",e)
print("version check failed", e)
def run_extensions_installers():
if not os.path.isdir(dir_extensions):
return
for dirname_extension in os.listdir(dir_extensions):
path_installer = os.path.join(dir_extensions, dirname_extension, "install.py")
if not os.path.isfile(path_installer):
continue
try:
env = os.environ.copy()
env['PYTHONPATH'] = os.path.abspath(".")
print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {dirname_extension}", custom_env=env))
except Exception as e:
print(e, file=sys.stderr)
def prepare_enviroment():
torch_command = os.environ.get('TORCH_COMMAND', "pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113")
requirements_file = os.environ.get('REQS_FILE', "requirements_versions.txt")
......@@ -189,6 +208,8 @@ def prepare_enviroment():
run_pip(f"install -r {requirements_file}", "requirements for Web UI")
run_extensions_installers()
if update_check:
version_check(commit)
......
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import base64
import io
import time
import uvicorn
from gradio.processing_utils import encode_pil_to_base64, decode_base64_to_file, decode_base64_to_image
from gradio.processing_utils import decode_base64_to_file, decode_base64_to_image
from fastapi import APIRouter, Depends, HTTPException
import modules.shared as shared
from modules import devices
......@@ -29,6 +31,12 @@ def setUpscalers(req: dict):
return reqDict
def encode_pil_to_base64(image):
buffer = io.BytesIO()
image.save(buffer, format="png")
return base64.b64encode(buffer.getvalue())
class Api:
def __init__(self, app, queue_lock):
self.router = APIRouter()
......@@ -40,6 +48,7 @@ class Api:
self.app.add_api_route("/sdapi/v1/extra-batch-images", self.extras_batch_images_api, methods=["POST"], response_model=ExtrasBatchImagesResponse)
self.app.add_api_route("/sdapi/v1/png-info", self.pnginfoapi, methods=["POST"], response_model=PNGInfoResponse)
self.app.add_api_route("/sdapi/v1/progress", self.progressapi, methods=["GET"], response_model=ProgressResponse)
self.app.add_api_route("/sdapi/v1/interrupt", self.interruptapi, methods=["POST"])
def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
sampler_index = sampler_to_index(txt2imgreq.sampler_index)
......@@ -176,6 +185,11 @@ class Api:
return ProgressResponse(progress=progress, eta_relative=eta_relative, state=shared.state.dict(), current_image=current_image)
def interruptapi(self):
shared.state.interrupt()
return {}
def launch(self, server_name, port):
self.app.include_router(self.router)
uvicorn.run(self.app, host=server_name, port=port)
......@@ -141,7 +141,7 @@ def run_extras(extras_mode, resize_mode, image, image_folder, input_dir, output_
upscaling_resize_w, upscaling_resize_h, upscaling_crop)
cache_key = LruCache.Key(image_hash=hash(np.array(image.getdata()).tobytes()),
info_hash=hash(info),
args_hash=hash(upscale_args))
args_hash=hash((upscale_args, upscale_first)))
cached_entry = cached_images.get(cache_key)
if cached_entry is None:
res = upscale(image, *upscale_args)
......
......@@ -510,8 +510,9 @@ def save_image(image, path, basename, seed=None, prompt=None, extension='png', i
if extension.lower() == '.png':
pnginfo_data = PngImagePlugin.PngInfo()
for k, v in params.pnginfo.items():
pnginfo_data.add_text(k, str(v))
if opts.enable_pnginfo:
for k, v in params.pnginfo.items():
pnginfo_data.add_text(k, str(v))
image.save(fullfn, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
......
......@@ -55,6 +55,7 @@ def process_batch(p, input_dir, output_dir, args):
filename = f"{left}-{n}{right}"
if not save_normally:
os.makedirs(output_dir, exist_ok=True)
processed_image.save(os.path.join(output_dir, filename))
......
......@@ -56,9 +56,9 @@ class InterrogateModels:
import clip
if self.running_on_cpu:
model, preprocess = clip.load(clip_model_name, device="cpu")
model, preprocess = clip.load(clip_model_name, device="cpu", download_root=shared.cmd_opts.clip_models_path)
else:
model, preprocess = clip.load(clip_model_name)
model, preprocess = clip.load(clip_model_name, download_root=shared.cmd_opts.clip_models_path)
model.eval()
model = model.to(devices.device_interrogate)
......
......@@ -38,13 +38,18 @@ def setup_for_low_vram(sd_model, use_medvram):
# see below for register_forward_pre_hook;
# first_stage_model does not use forward(), it uses encode/decode, so register_forward_pre_hook is
# useless here, and we just replace those methods
def first_stage_model_encode_wrap(self, encoder, x):
send_me_to_gpu(self, None)
return encoder(x)
def first_stage_model_decode_wrap(self, decoder, z):
send_me_to_gpu(self, None)
return decoder(z)
first_stage_model = sd_model.first_stage_model
first_stage_model_encode = sd_model.first_stage_model.encode
first_stage_model_decode = sd_model.first_stage_model.decode
def first_stage_model_encode_wrap(x):
send_me_to_gpu(first_stage_model, None)
return first_stage_model_encode(x)
def first_stage_model_decode_wrap(z):
send_me_to_gpu(first_stage_model, None)
return first_stage_model_decode(z)
# remove three big modules, cond, first_stage, and unet from the model and then
# send the model to GPU. Then put modules back. the modules will be in CPU.
......@@ -56,8 +61,8 @@ def setup_for_low_vram(sd_model, use_medvram):
# register hooks for those the first two models
sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu)
sd_model.first_stage_model.register_forward_pre_hook(send_me_to_gpu)
sd_model.first_stage_model.encode = lambda x, en=sd_model.first_stage_model.encode: first_stage_model_encode_wrap(sd_model.first_stage_model, en, x)
sd_model.first_stage_model.decode = lambda z, de=sd_model.first_stage_model.decode: first_stage_model_decode_wrap(sd_model.first_stage_model, de, z)
sd_model.first_stage_model.encode = first_stage_model_encode_wrap
sd_model.first_stage_model.decode = first_stage_model_decode_wrap
parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model
if use_medvram:
......
......@@ -597,6 +597,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if p.scripts is not None:
p.scripts.postprocess(p, res)
p.sd_model = None
p.sampler = None
return res
......
......@@ -32,7 +32,7 @@ class RestrictedUnpickler(pickle.Unpickler):
return getattr(collections, name)
if module == 'torch._utils' and name in ['_rebuild_tensor_v2', '_rebuild_parameter']:
return getattr(torch._utils, name)
if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage']:
if module == 'torch' and name in ['FloatStorage', 'HalfStorage', 'IntStorage', 'LongStorage', 'DoubleStorage', 'ByteStorage']:
return getattr(torch, name)
if module == 'torch.nn.modules.container' and name in ['ParameterDict']:
return getattr(torch.nn.modules.container, name)
......
......@@ -3,6 +3,8 @@ import traceback
from collections import namedtuple
import inspect
from fastapi import FastAPI
from gradio import Blocks
def report_exception(c, job):
print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
......@@ -25,6 +27,7 @@ class ImageSaveParams:
ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"])
callbacks_app_started = []
callbacks_model_loaded = []
callbacks_ui_tabs = []
callbacks_ui_settings = []
......@@ -40,6 +43,14 @@ def clear_callbacks():
callbacks_image_saved.clear()
def app_started_callback(demo: Blocks, app: FastAPI):
for c in callbacks_app_started:
try:
c.callback(demo, app)
except Exception:
report_exception(c, 'app_started_callback')
def model_loaded_callback(sd_model):
for c in callbacks_model_loaded:
try:
......@@ -69,7 +80,7 @@ def ui_settings_callback():
def before_image_saved_callback(params: ImageSaveParams):
for c in callbacks_image_saved:
for c in callbacks_before_image_saved:
try:
c.callback(params)
except Exception:
......@@ -91,6 +102,12 @@ def add_callback(callbacks, fun):
callbacks.append(ScriptCallback(filename, fun))
def on_app_started(callback):
"""register a function to be called when the webui started, the gradio `Block` component and
fastapi `FastAPI` object are passed as the arguments"""
add_callback(callbacks_app_started, callback)
def on_model_loaded(callback):
"""register a function to be called when the stable diffusion model is created; the model is
passed as an argument"""
......
......@@ -94,6 +94,10 @@ class StableDiffusionModelHijack:
if type(model_embeddings.token_embedding) == EmbeddingsWithFixes:
model_embeddings.token_embedding = model_embeddings.token_embedding.wrapped
self.layers = None
self.circular_enabled = False
self.clip = None
def apply_circular(self, enable):
if self.circular_enabled == enable:
return
......
import collections
import os.path
import sys
import gc
from collections import namedtuple
import torch
import re
......@@ -220,6 +221,12 @@ def load_model(checkpoint_info=None):
if checkpoint_info.config != shared.cmd_opts.config:
print(f"Loading config from: {checkpoint_info.config}")
if shared.sd_model:
sd_hijack.model_hijack.undo_hijack(shared.sd_model)
shared.sd_model = None
gc.collect()
devices.torch_gc()
sd_config = OmegaConf.load(checkpoint_info.config)
if should_hijack_inpainting(checkpoint_info):
......@@ -233,6 +240,7 @@ def load_model(checkpoint_info=None):
checkpoint_info = checkpoint_info._replace(config=checkpoint_info.config.replace(".yaml", "-inpainting.yaml"))
do_inpainting_hijack()
sd_model = instantiate_from_config(sd_config.model)
load_model_weights(sd_model, checkpoint_info)
......@@ -252,14 +260,18 @@ def load_model(checkpoint_info=None):
return sd_model
def reload_model_weights(sd_model, info=None):
def reload_model_weights(sd_model=None, info=None):
from modules import lowvram, devices, sd_hijack
checkpoint_info = info or select_checkpoint()
if not sd_model:
sd_model = shared.sd_model
if sd_model.sd_model_checkpoint == checkpoint_info.filename:
return
if sd_model.sd_checkpoint_info.config != checkpoint_info.config or should_hijack_inpainting(checkpoint_info) != should_hijack_inpainting(sd_model.sd_checkpoint_info):
del sd_model
checkpoints_loaded.clear()
load_model(checkpoint_info)
return shared.sd_model
......
from collections import namedtuple
import numpy as np
from math import floor
import torch
import tqdm
from PIL import Image
......@@ -205,17 +206,22 @@ class VanillaStableDiffusionSampler:
self.mask = p.mask if hasattr(p, 'mask') else None
self.nmask = p.nmask if hasattr(p, 'nmask') else None
def adjust_steps_if_invalid(self, p, num_steps):
if (self.config.name == 'DDIM' and p.ddim_discretize == 'uniform') or (self.config.name == 'PLMS'):
valid_step = 999 / (1000 // num_steps)
if valid_step == floor(valid_step):
return int(valid_step) + 1
return num_steps
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
steps, t_enc = setup_img2img_steps(p, steps)
steps = self.adjust_steps_if_invalid(p, steps)
self.initialize(p)
# existing code fails with certain step counts, like 9
try:
self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
except Exception:
self.sampler.make_schedule(ddim_num_steps=steps+1, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
self.sampler.make_schedule(ddim_num_steps=steps, ddim_eta=self.eta, ddim_discretize=p.ddim_discretize, verbose=False)
x1 = self.sampler.stochastic_encode(x, torch.tensor([t_enc] * int(x.shape[0])).to(shared.device), noise=noise)
self.init_latent = x
......@@ -239,18 +245,14 @@ class VanillaStableDiffusionSampler:
self.last_latent = x
self.step = 0
steps = steps or p.steps
steps = self.adjust_steps_if_invalid(p, steps or p.steps)
# Wrap the conditioning models with additional image conditioning for inpainting model
if image_conditioning is not None:
conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]}
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}
# existing code fails with certain step counts, like 9
try:
samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
except Exception:
samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps+1, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0])
return samples_ddim
......
......@@ -51,6 +51,7 @@ parser.add_argument("--realesrgan-models-path", type=str, help="Path to director
parser.add_argument("--scunet-models-path", type=str, help="Path to directory with ScuNET model file(s).", default=os.path.join(models_path, 'ScuNET'))
parser.add_argument("--swinir-models-path", type=str, help="Path to directory with SwinIR model file(s).", default=os.path.join(models_path, 'SwinIR'))
parser.add_argument("--ldsr-models-path", type=str, help="Path to directory with LDSR model file(s).", default=os.path.join(models_path, 'LDSR'))
parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None)
parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers")
parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work")
parser.add_argument("--deepdanbooru", action='store_true', help="enable deepdanbooru interrogator")
......@@ -288,11 +289,12 @@ options_templates.update(options_section(('system', "System"), {
}))
options_templates.update(options_section(('training', "Training"), {
"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training hypernetwork. Saves VRAM."),
"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
"dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
"dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
"training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
"training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
"training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),
}))
options_templates.update(options_section(('sd', "Stable Diffusion"), {
......
......@@ -235,6 +235,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), embedding_name)
unload = shared.opts.unload_models_when_training
if save_embedding_every > 0:
embedding_dir = os.path.join(log_directory, "embeddings")
......@@ -272,6 +273,8 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
with torch.autocast("cuda"):
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)
if unload:
shared.sd_model.first_stage_model.to(devices.cpu)
embedding.vec.requires_grad = True
optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
......@@ -328,6 +331,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
if images_dir is not None and steps_done % create_image_every == 0:
forced_filename = f'{embedding_name}-{steps_done}'
last_saved_image = os.path.join(images_dir, forced_filename)
shared.sd_model.first_stage_model.to(devices.device)
p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
do_not_save_grid=True,
......@@ -355,6 +361,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
processed = processing.process_images(p)
image = processed.images[0]
if unload:
shared.sd_model.first_stage_model.to(devices.cpu)
shared.state.current_image = image
if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
......@@ -400,6 +409,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
shared.sd_model.first_stage_model.to(devices.device)
return embedding, filename
......
......@@ -25,8 +25,10 @@ def train_embedding(*args):
assert not shared.cmd_opts.lowvram, 'Training models with lowvram not possible'
apply_optimizations = shared.opts.training_xattention_optimizations
try:
sd_hijack.undo_optimizations()
if not apply_optimizations:
sd_hijack.undo_optimizations()
embedding, filename = modules.textual_inversion.textual_inversion.train_embedding(*args)
......@@ -38,5 +40,6 @@ Embedding saved to {html.escape(filename)}
except Exception:
raise
finally:
sd_hijack.apply_optimizations()
if not apply_optimizations:
sd_hijack.apply_optimizations()
......@@ -13,6 +13,9 @@ import html
from modules import extensions, shared, paths
available_extensions = {"extensions": []}
def check_access():
assert not shared.cmd_opts.disable_extension_access, "extension access disabed because of commandline flags"
......@@ -96,6 +99,14 @@ def extension_table():
return code
def normalize_git_url(url):
if url is None:
return ""
url = url.replace(".git", "")
return url
def install_extension_from_url(dirname, url):
check_access()
......@@ -103,14 +114,15 @@ def install_extension_from_url(dirname, url):
if dirname is None or dirname == "":
*parts, last_part = url.split('/')
last_part = last_part.replace(".git", "")
last_part = normalize_git_url(last_part)
dirname = last_part
target_dir = os.path.join(extensions.extensions_dir, dirname)
assert not os.path.exists(target_dir), f'Extension directory already exists: {target_dir}'
assert len([x for x in extensions.extensions if x.remote == url]) == 0, 'Extension with this URL is already installed'
normalized_url = normalize_git_url(url)
assert len([x for x in extensions.extensions if normalize_git_url(x.remote) == normalized_url]) == 0, 'Extension with this URL is already installed'
tmpdir = os.path.join(paths.script_path, "tmp", dirname)
......@@ -128,18 +140,80 @@ def install_extension_from_url(dirname, url):
shutil.rmtree(tmpdir, True)
def install_extension_from_index(url):
ext_table, message = install_extension_from_url(None, url)
return refresh_available_extensions_from_data(), ext_table, message
def refresh_available_extensions(url):
global available_extensions
import urllib.request
with urllib.request.urlopen(url) as response:
text = response.read()
available_extensions = json.loads(text)
return url, refresh_available_extensions_from_data(), ''
def refresh_available_extensions_from_data():
extlist = available_extensions["extensions"]
installed_extension_urls = {normalize_git_url(extension.remote): extension.name for extension in extensions.extensions}
code = f"""<!-- {time.time()} -->
<table id="available_extensions">
<thead>
<tr>
<th>Extension</th>
<th>Description</th>
<th>Action</th>
</tr>
</thead>
<tbody>
"""
for ext in extlist:
name = ext.get("name", "noname")
url = ext.get("url", None)
description = ext.get("description", "")
if url is None:
continue
existing = installed_extension_urls.get(normalize_git_url(url), None)
install_code = f"""<input onclick="install_extension_from_index(this, '{html.escape(url)}')" type="button" value="{"Install" if not existing else "Installed"}" {"disabled=disabled" if existing else ""} class="gr-button gr-button-lg gr-button-secondary">"""
code += f"""
<tr>
<td><a href="{html.escape(url)}">{html.escape(name)}</a></td>
<td>{html.escape(description)}</td>
<td>{install_code}</td>
</tr>
"""
code += """
</tbody>
</table>
"""
return code
def create_ui():
import modules.ui
with gr.Blocks(analytics_enabled=False) as ui:
with gr.Tabs(elem_id="tabs_extensions") as tabs:
with gr.TabItem("Installed"):
extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False)
extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False)
with gr.Row():
apply = gr.Button(value="Apply and restart UI", variant="primary")
check = gr.Button(value="Check for updates")
extensions_disabled_list = gr.Text(elem_id="extensions_disabled_list", visible=False).style(container=False)
extensions_update_list = gr.Text(elem_id="extensions_update_list", visible=False).style(container=False)
extensions_table = gr.HTML(lambda: extension_table())
......@@ -157,16 +231,38 @@ def create_ui():
outputs=[extensions_table],
)
with gr.TabItem("Available"):
with gr.Row():
refresh_available_extensions_button = gr.Button(value="Load from:", variant="primary")
available_extensions_index = gr.Text(value="https://raw.githubusercontent.com/wiki/AUTOMATIC1111/stable-diffusion-webui/Extensions-index.md", label="Extension index URL").style(container=False)
extension_to_install = gr.Text(elem_id="extension_to_install", visible=False)
install_extension_button = gr.Button(elem_id="install_extension_button", visible=False)
install_result = gr.HTML()
available_extensions_table = gr.HTML()
refresh_available_extensions_button.click(
fn=modules.ui.wrap_gradio_call(refresh_available_extensions, extra_outputs=[gr.update(), gr.update()]),
inputs=[available_extensions_index],
outputs=[available_extensions_index, available_extensions_table, install_result],
)
install_extension_button.click(
fn=modules.ui.wrap_gradio_call(install_extension_from_index, extra_outputs=[gr.update(), gr.update()]),
inputs=[extension_to_install],
outputs=[available_extensions_table, extensions_table, install_result],
)
with gr.TabItem("Install from URL"):
install_url = gr.Text(label="URL for extension's git repository")
install_dirname = gr.Text(label="Local directory name", placeholder="Leave empty for auto")
intall_button = gr.Button(value="Install", variant="primary")
intall_result = gr.HTML(elem_id="extension_install_result")
install_button = gr.Button(value="Install", variant="primary")
install_result = gr.HTML(elem_id="extension_install_result")
intall_button.click(
install_button.click(
fn=modules.ui.wrap_gradio_call(install_extension_from_url, extra_outputs=[gr.update()]),
inputs=[install_dirname, install_url],
outputs=[extensions_table, intall_result],
outputs=[extensions_table, install_result],
)
return ui
......@@ -532,16 +532,16 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
/* Extensions */
#extensions{
#tab_extensions table{
border-collapse: collapse;
}
#extensions td, #extensions th{
#tab_extensions table td, #tab_extensions table th{
border: 1px solid #ccc;
padding: 0.25em 0.5em;
}
#extensions input[type="checkbox"]{
#tab_extensions table input[type="checkbox"]{
margin-right: 0.5em;
}
......@@ -549,6 +549,9 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h
max-width: 16em;
}
#tab_extensions input[disabled="disabled"]{
opacity: 0.5;
}
/* The following handles localization for right-to-left (RTL) languages like Arabic.
The rtl media type will only be activated by the logic in javascript/localization.js.
......
......@@ -23,6 +23,7 @@ import modules.sd_hijack
import modules.sd_models
import modules.shared as shared
import modules.txt2img
import modules.script_callbacks
import modules.ui
from modules import devices
......@@ -77,7 +78,7 @@ def initialize():
modules.scripts.load_scripts()
modules.sd_models.load_model()
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights(shared.sd_model)))
shared.opts.onchange("sd_model_checkpoint", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))
shared.opts.onchange("sd_hypernetwork", wrap_queued_call(lambda: modules.hypernetworks.hypernetwork.load_hypernetwork(shared.opts.sd_hypernetwork)))
shared.opts.onchange("sd_hypernetwork_strength", modules.hypernetworks.hypernetwork.apply_strength)
......@@ -140,6 +141,8 @@ def webui():
if launch_api:
create_api(app)
modules.script_callbacks.app_started_callback(demo, app)
wait_on_server(demo)
sd_samplers.set_samplers()
......
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