Merge branch 'AUTOMATIC1111:master' into master

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不会画画的中医不是好程序员 2022-10-14 07:35:07 +08:00 committed by GitHub
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17 changed files with 210 additions and 60 deletions

1
.gitignore vendored
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@ -17,6 +17,7 @@ __pycache__
/webui.settings.bat
/embeddings
/styles.csv
/params.txt
/styles.csv.bak
/webui-user.bat
/webui-user.sh

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@ -94,7 +94,7 @@ contextMenuInit = function(){
}
gradioApp().addEventListener("click", function(e) {
let source = e.composedPath()[0]
if(source.id && source.indexOf('check_progress')>-1){
if(source.id && source.id.indexOf('check_progress')>-1){
return
}

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@ -14,7 +14,7 @@ titles = {
"\u{1f3b2}\ufe0f": "Set seed to -1, which will cause a new random number to be used every time",
"\u267b\ufe0f": "Reuse seed from last generation, mostly useful if it was randomed",
"\u{1f3a8}": "Add a random artist to the prompt.",
"\u2199\ufe0f": "Read generation parameters from prompt into user interface.",
"\u2199\ufe0f": "Read generation parameters from prompt or last generation if prompt is empty into user interface.",
"\u{1f4c2}": "Open images output directory",
"Inpaint a part of image": "Draw a mask over an image, and the script will regenerate the masked area with content according to prompt",
@ -84,6 +84,8 @@ titles = {
"Filename word regex": "This regular expression will be used extract words from filename, and they will be joined using the option below into label text used for training. Leave empty to keep filename text as it is.",
"Filename join string": "This string will be used to hoin split words into a single line if the option above is enabled.",
"Quicksettings list": "List of setting names, separated by commas, for settings that should go to the quick access bar at the top, rather than the usual setting tab. See modules/shared.py for setting names. Requires restarting to apply."
}

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@ -36,7 +36,7 @@ onUiUpdate(function(){
const notification = new Notification(
'Stable Diffusion',
{
body: `Generated ${imgs.size > 1 ? imgs.size - 1 : 1} image${imgs.size > 1 ? 's' : ''}`,
body: `Generated ${imgs.size > 1 ? imgs.size - opts.return_grid : 1} image${imgs.size > 1 ? 's' : ''}`,
icon: headImg,
image: headImg,
}

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@ -33,27 +33,27 @@ function args_to_array(args){
}
function switch_to_txt2img(){
gradioApp().querySelectorAll('button')[0].click();
gradioApp().querySelector('#tabs').querySelectorAll('button')[0].click();
return args_to_array(arguments);
}
function switch_to_img2img_img2img(){
gradioApp().querySelectorAll('button')[1].click();
gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
gradioApp().getElementById('mode_img2img').querySelectorAll('button')[0].click();
return args_to_array(arguments);
}
function switch_to_img2img_inpaint(){
gradioApp().querySelectorAll('button')[1].click();
gradioApp().querySelector('#tabs').querySelectorAll('button')[1].click();
gradioApp().getElementById('mode_img2img').querySelectorAll('button')[1].click();
return args_to_array(arguments);
}
function switch_to_extras(){
gradioApp().querySelectorAll('button')[2].click();
gradioApp().querySelector('#tabs').querySelectorAll('button')[2].click();
return args_to_array(arguments);
}

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@ -19,6 +19,7 @@ def get_deepbooru_tags(pil_image):
release_process()
OPT_INCLUDE_RANKS = "include_ranks"
def create_deepbooru_opts():
from modules import shared
@ -26,6 +27,7 @@ def create_deepbooru_opts():
"use_spaces": shared.opts.deepbooru_use_spaces,
"use_escape": shared.opts.deepbooru_escape,
"alpha_sort": shared.opts.deepbooru_sort_alpha,
OPT_INCLUDE_RANKS: shared.opts.interrogate_return_ranks,
}
@ -113,6 +115,7 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
alpha_sort = deepbooru_opts['alpha_sort']
use_spaces = deepbooru_opts['use_spaces']
use_escape = deepbooru_opts['use_escape']
include_ranks = deepbooru_opts['include_ranks']
width = model.input_shape[2]
height = model.input_shape[1]
@ -151,19 +154,20 @@ def get_deepbooru_tags_from_model(model, tags, pil_image, threshold, deepbooru_o
if alpha_sort:
sort_ndx = 1
# sort by reverse by likelihood and normal for alpha
# sort by reverse by likelihood and normal for alpha, and format tag text as requested
unsorted_tags_in_theshold.sort(key=lambda y: y[sort_ndx], reverse=(not alpha_sort))
for weight, tag in unsorted_tags_in_theshold:
result_tags_out.append(tag)
# note: tag_outformat will still have a colon if include_ranks is True
tag_outformat = tag.replace(':', ' ')
if use_spaces:
tag_outformat = tag_outformat.replace('_', ' ')
if use_escape:
tag_outformat = re.sub(re_special, r'\\\1', tag_outformat)
if include_ranks:
tag_outformat = f"({tag_outformat}:{weight:.3f})"
result_tags_out.append(tag_outformat)
print('\n'.join(sorted(result_tags_print, reverse=True)))
tags_text = ', '.join(result_tags_out)
if use_spaces:
tags_text = tags_text.replace('_', ' ')
if use_escape:
tags_text = re.sub(re_special, r'\\\1', tags_text)
return tags_text.replace(':', ' ')
return ', '.join(result_tags_out)

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@ -1,5 +1,8 @@
import os
import re
import gradio as gr
from modules.shared import script_path
from modules import shared
re_param_code = r"\s*([\w ]+):\s*([^,]+)(?:,|$)"
re_param = re.compile(re_param_code)
@ -61,6 +64,12 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
def connect_paste(button, paste_fields, input_comp, js=None):
def paste_func(prompt):
if not prompt and not shared.cmd_opts.hide_ui_dir_config:
filename = os.path.join(script_path, "params.txt")
if os.path.exists(filename):
with open(filename, "r", encoding="utf8") as file:
prompt = file.read()
params = parse_generation_parameters(prompt)
res = []

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@ -18,6 +18,8 @@ from modules.textual_inversion.learn_schedule import LearnRateScheduler
class HypernetworkModule(torch.nn.Module):
multiplier = 1.0
def __init__(self, dim, state_dict=None):
super().__init__()
@ -36,7 +38,11 @@ class HypernetworkModule(torch.nn.Module):
self.to(devices.device)
def forward(self, x):
return x + (self.linear2(self.linear1(x)))
return x + (self.linear2(self.linear1(x))) * self.multiplier
def apply_strength(value=None):
HypernetworkModule.multiplier = value if value is not None else shared.opts.sd_hypernetwork_strength
class Hypernetwork:

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@ -123,7 +123,7 @@ class InterrogateModels:
return caption[0]
def interrogate(self, pil_image):
def interrogate(self, pil_image, include_ranks=False):
res = None
try:
@ -156,7 +156,10 @@ class InterrogateModels:
for name, topn, items in self.categories:
matches = self.rank(image_features, items, top_count=topn)
for match, score in matches:
res += ", " + match
if include_ranks:
res += ", " + match
else:
res += f", ({match}:{score})"
except Exception:
print(f"Error interrogating", file=sys.stderr)

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@ -324,6 +324,10 @@ def process_images(p: StableDiffusionProcessing) -> Processed:
else:
assert p.prompt is not None
with open(os.path.join(shared.script_path, "params.txt"), "w", encoding="utf8") as file:
processed = Processed(p, [], p.seed, "")
file.write(processed.infotext(p, 0))
devices.torch_gc()
seed = get_fixed_seed(p.seed)

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@ -13,7 +13,7 @@ import modules.memmon
import modules.sd_models
import modules.styles
import modules.devices as devices
from modules import sd_samplers
from modules import sd_samplers, sd_models
from modules.hypernetworks import hypernetwork
from modules.paths import models_path, script_path, sd_path
@ -145,14 +145,14 @@ def realesrgan_models_names():
class OptionInfo:
def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False):
def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, show_on_main_page=False, refresh=None):
self.default = default
self.label = label
self.component = component
self.component_args = component_args
self.onchange = onchange
self.section = None
self.show_on_main_page = show_on_main_page
self.refresh = refresh
def options_section(section_identifier, options_dict):
@ -237,8 +237,9 @@ options_templates.update(options_section(('training', "Training"), {
}))
options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, show_on_main_page=True),
"sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, refresh=sd_models.list_models),
"sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
"sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}),
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies (normally you'd do less with less denoising)."),
@ -250,14 +251,17 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"filter_nsfw": OptionInfo(False, "Filter NSFW content"),
'CLIP_stop_at_last_layers': OptionInfo(1, "Stop At last layers of CLIP model", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}),
"random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}),
'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"),
}))
options_templates.update(options_section(('interrogate', "Interrogate Options"), {
"interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
"interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"),
"interrogate_return_ranks": OptionInfo(False, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."),
"interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
"interrogate_clip_min_length": OptionInfo(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
"interrogate_clip_max_length": OptionInfo(48, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
"interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file (0 = No limit)"),
"interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
"deepbooru_sort_alpha": OptionInfo(True, "Interrogate: deepbooru sort alphabetically"),
"deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
@ -345,6 +349,8 @@ class Options:
item = self.data_labels.get(key)
item.onchange = func
func()
def dumpjson(self):
d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
return json.dumps(d)

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@ -17,7 +17,9 @@ def preprocess(process_src, process_dst, process_width, process_height, process_
shared.interrogator.load()
if process_caption_deepbooru:
deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, deepbooru.create_deepbooru_opts())
db_opts = deepbooru.create_deepbooru_opts()
db_opts[deepbooru.OPT_INCLUDE_RANKS] = False
deepbooru.create_deepbooru_process(opts.interrogate_deepbooru_score_threshold, db_opts)
preprocess_work(process_src, process_dst, process_width, process_height, process_flip, process_split, process_caption, process_caption_deepbooru)

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@ -79,6 +79,8 @@ reuse_symbol = '\u267b\ufe0f' # ♻️
art_symbol = '\U0001f3a8' # 🎨
paste_symbol = '\u2199\ufe0f' # ↙
folder_symbol = '\U0001f4c2' # 📂
refresh_symbol = '\U0001f504' # 🔄
def plaintext_to_html(text):
text = "<p>" + "<br>\n".join([f"{html.escape(x)}" for x in text.split('\n')]) + "</p>"
@ -1218,8 +1220,7 @@ def create_ui(wrap_gradio_gpu_call):
outputs=[],
)
def create_setting_component(key):
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
@ -1239,7 +1240,34 @@ def create_ui(wrap_gradio_gpu_call):
else:
raise Exception(f'bad options item type: {str(t)} for key {key}')
return comp(label=info.label, value=fun, **(args or {}))
if info.refresh is not None:
if is_quicksettings:
res = comp(label=info.label, value=fun, **(args or {}))
refresh_button = gr.Button(value=refresh_symbol, elem_id="refresh_"+key)
else:
with gr.Row(variant="compact"):
res = comp(label=info.label, value=fun, **(args or {}))
refresh_button = gr.Button(value=refresh_symbol, elem_id="refresh_" + key)
def refresh():
info.refresh()
refreshed_args = info.component_args() if callable(info.component_args) else info.component_args
for k, v in refreshed_args.items():
setattr(res, k, v)
return gr.update(**(refreshed_args or {}))
refresh_button.click(
fn=refresh,
inputs=[],
outputs=[res],
)
else:
res = comp(label=info.label, value=fun, **(args or {}))
return res
components = []
component_dict = {}
@ -1313,6 +1341,9 @@ Requested path was: {f}
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
@ -1337,7 +1368,7 @@ Requested path was: {f}
gr.HTML(elem_id="settings_header_text_{}".format(item.section[0]), value='<h1 class="gr-button-lg">{}</h1>'.format(item.section[1]))
if item.show_on_main_page:
if k in quicksettings_names:
quicksettings_list.append((i, k, item))
components.append(dummy_component)
else:
@ -1346,7 +1377,11 @@ Requested path was: {f}
components.append(component)
items_displayed += 1
request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
with gr.Row():
request_notifications = gr.Button(value='Request browser notifications', elem_id="request_notifications")
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=[],
@ -1354,10 +1389,6 @@ Requested path was: {f}
_js='function(){}'
)
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')
def reload_scripts():
modules.scripts.reload_script_body_only()
@ -1372,7 +1403,6 @@ Requested path was: {f}
shared.state.interrupt()
settings_interface.gradio_ref.do_restart = True
restart_gradio.click(
fn=request_restart,
inputs=[],
@ -1408,12 +1438,12 @@ Requested path was: {f}
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)
component = create_setting_component(k, is_quicksettings=True)
component_dict[k] = component
settings_interface.gradio_ref = demo
with gr.Tabs() as tabs:
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()

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@ -120,15 +120,45 @@ class Script(scripts.Script):
return is_img2img
def ui(self, is_img2img):
info = gr.Markdown('''
* `CFG Scale` should be 2 or lower.
''')
override_sampler = gr.Checkbox(label="Override `Sampling method` to Euler?(this method is built for it)", value=True)
override_prompt = gr.Checkbox(label="Override `prompt` to the same value as `original prompt`?(and `negative prompt`)", value=True)
original_prompt = gr.Textbox(label="Original prompt", lines=1)
original_negative_prompt = gr.Textbox(label="Original negative prompt", lines=1)
cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
override_steps = gr.Checkbox(label="Override `Sampling Steps` to the same value as `Decode steps`?", value=True)
st = gr.Slider(label="Decode steps", minimum=1, maximum=150, step=1, value=50)
override_strength = gr.Checkbox(label="Override `Denoising strength` to 1?", value=True)
cfg = gr.Slider(label="Decode CFG scale", minimum=0.0, maximum=15.0, step=0.1, value=1.0)
randomness = gr.Slider(label="Randomness", minimum=0.0, maximum=1.0, step=0.01, value=0.0)
sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False)
return [original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment]
def run(self, p, original_prompt, original_negative_prompt, cfg, st, randomness, sigma_adjustment):
return [
info,
override_sampler,
override_prompt, original_prompt, original_negative_prompt,
override_steps, st,
override_strength,
cfg, randomness, sigma_adjustment,
]
def run(self, p, _, override_sampler, override_prompt, original_prompt, original_negative_prompt, override_steps, st, override_strength, cfg, randomness, sigma_adjustment):
# Override
if override_sampler:
p.sampler_index = [sampler.name for sampler in sd_samplers.samplers].index("Euler")
if override_prompt:
p.prompt = original_prompt
p.negative_prompt = original_negative_prompt
if override_steps:
p.steps = st
if override_strength:
p.denoising_strength = 1.0
def sample_extra(conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength):

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@ -107,6 +107,10 @@ def apply_hypernetwork(p, x, xs):
hypernetwork.load_hypernetwork(name)
def apply_hypernetwork_strength(p, x, xs):
hypernetwork.apply_strength(x)
def confirm_hypernetworks(p, xs):
for x in xs:
if x.lower() in ["", "none"]:
@ -165,6 +169,7 @@ axis_options = [
AxisOption("Sampler", str, apply_sampler, format_value, confirm_samplers),
AxisOption("Checkpoint name", str, apply_checkpoint, format_value, confirm_checkpoints),
AxisOption("Hypernetwork", str, apply_hypernetwork, format_value, confirm_hypernetworks),
AxisOption("Hypernet str.", float, apply_hypernetwork_strength, format_value_add_label, None),
AxisOption("Sigma Churn", float, apply_field("s_churn"), format_value_add_label, None),
AxisOption("Sigma min", float, apply_field("s_tmin"), format_value_add_label, None),
AxisOption("Sigma max", float, apply_field("s_tmax"), format_value_add_label, None),
@ -175,13 +180,17 @@ axis_options = [
]
def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
res = []
def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend, include_lone_images):
ver_texts = [[images.GridAnnotation(y)] for y in y_labels]
hor_texts = [[images.GridAnnotation(x)] for x in x_labels]
first_processed = None
# Temporary list of all the images that are generated to be populated into the grid.
# Will be filled with empty images for any individual step that fails to process properly
image_cache = []
processed_result = None
cell_mode = "P"
cell_size = (1,1)
state.job_count = len(xs) * len(ys) * p.n_iter
@ -189,22 +198,39 @@ def draw_xy_grid(p, xs, ys, x_labels, y_labels, cell, draw_legend):
for ix, x in enumerate(xs):
state.job = f"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}"
processed = cell(x, y)
if first_processed is None:
first_processed = processed
processed:Processed = cell(x, y)
try:
res.append(processed.images[0])
# this dereference will throw an exception if the image was not processed
# (this happens in cases such as if the user stops the process from the UI)
processed_image = processed.images[0]
if processed_result is None:
# Use our first valid processed result as a template container to hold our full results
processed_result = copy(processed)
cell_mode = processed_image.mode
cell_size = processed_image.size
processed_result.images = [Image.new(cell_mode, cell_size)]
image_cache.append(processed_image)
if include_lone_images:
processed_result.images.append(processed_image)
processed_result.all_prompts.append(processed.prompt)
processed_result.all_seeds.append(processed.seed)
processed_result.infotexts.append(processed.infotexts[0])
except:
res.append(Image.new(res[0].mode, res[0].size))
image_cache.append(Image.new(cell_mode, cell_size))
grid = images.image_grid(res, rows=len(ys))
if not processed_result:
print("Unexpected error: draw_xy_grid failed to return even a single processed image")
return Processed()
grid = images.image_grid(image_cache, rows=len(ys))
if draw_legend:
grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)
grid = images.draw_grid_annotations(grid, cell_size[0], cell_size[1], hor_texts, ver_texts)
first_processed.images = [grid]
processed_result.images[0] = grid
return first_processed
return processed_result
re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
@ -229,11 +255,12 @@ class Script(scripts.Script):
y_values = gr.Textbox(label="Y values", visible=False, lines=1)
draw_legend = gr.Checkbox(label='Draw legend', value=True)
include_lone_images = gr.Checkbox(label='Include Separate Images', value=False)
no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False)
return [x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds]
return [x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds]
def run(self, p, x_type, x_values, y_type, y_values, draw_legend, no_fixed_seeds):
def run(self, p, x_type, x_values, y_type, y_values, draw_legend, include_lone_images, no_fixed_seeds):
if not no_fixed_seeds:
modules.processing.fix_seed(p)
@ -344,7 +371,8 @@ class Script(scripts.Script):
x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],
y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],
cell=cell,
draw_legend=draw_legend
draw_legend=draw_legend,
include_lone_images=include_lone_images
)
if opts.grid_save:
@ -354,6 +382,8 @@ class Script(scripts.Script):
modules.sd_models.reload_model_weights(shared.sd_model)
hypernetwork.load_hypernetwork(opts.sd_hypernetwork)
hypernetwork.apply_strength()
opts.data["CLIP_stop_at_last_layers"] = CLIP_stop_at_last_layers

View file

@ -228,6 +228,8 @@ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block s
border-top: 1px solid #eee;
border-left: 1px solid #eee;
border-right: 1px solid #eee;
z-index: 300;
}
.dark fieldset span.text-gray-500, .dark .gr-block.gr-box span.text-gray-500, .dark label.block span{
@ -480,16 +482,30 @@ input[type="range"]{
background: #a55000;
}
#quicksettings {
gap: 0.4em;
}
#quicksettings > div{
border: none;
background: none;
flex: unset;
gap: 0.5em;
}
#quicksettings > div > div{
max-width: 32em;
min-width: 24em;
padding: 0;
}
#refresh_sd_model_checkpoint, #refresh_sd_hypernetwork{
max-width: 2.5em;
min-width: 2.5em;
height: 2.4em;
}
canvas[key="mask"] {
z-index: 12 !important;
filter: invert();
@ -506,3 +522,10 @@ canvas[key="mask"] {
z-index: 200;
width: 8em;
}
#quicksettings .gr-box > div > div > input.gr-text-input {
top: -1.12em;
}
.row.gr-compact{
overflow: visible;
}

View file

@ -72,7 +72,6 @@ def wrap_gradio_gpu_call(func, extra_outputs=None):
return modules.ui.wrap_gradio_call(f, extra_outputs=extra_outputs)
def initialize():
modelloader.cleanup_models()
modules.sd_models.setup_model()
@ -86,6 +85,7 @@ def initialize():
shared.sd_model = 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_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)
def webui():