Merge pull request #12426 from AUTOMATIC1111/split_shared

Split shared.py into multiple files
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AUTOMATIC1111 2023-08-09 14:40:06 +03:00 committed by GitHub
commit c8c48640e6
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24 changed files with 1065 additions and 992 deletions

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@ -3,7 +3,7 @@ import contextlib
from functools import lru_cache
import torch
from modules import errors
from modules import errors, shared
if sys.platform == "darwin":
from modules import mac_specific
@ -17,8 +17,6 @@ def has_mps() -> bool:
def get_cuda_device_string():
from modules import shared
if shared.cmd_opts.device_id is not None:
return f"cuda:{shared.cmd_opts.device_id}"
@ -40,8 +38,6 @@ def get_optimal_device():
def get_device_for(task):
from modules import shared
if task in shared.cmd_opts.use_cpu:
return cpu
@ -97,8 +93,6 @@ nv_rng = None
def autocast(disable=False):
from modules import shared
if disable:
return contextlib.nullcontext()
@ -117,8 +111,6 @@ class NansException(Exception):
def test_for_nans(x, where):
from modules import shared
if shared.cmd_opts.disable_nan_check:
return

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@ -1,7 +1,7 @@
import os
import threading
from modules import shared, errors, cache
from modules import shared, errors, cache, scripts
from modules.gitpython_hack import Repo
from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path # noqa: F401
@ -90,8 +90,6 @@ class Extension:
self.have_info_from_repo = True
def list_files(self, subdir, extension):
from modules import scripts
dirpath = os.path.join(self.path, subdir)
if not os.path.isdir(dirpath):
return []

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@ -6,7 +6,7 @@ import re
import gradio as gr
from modules.paths import data_path
from modules import shared, ui_tempdir, script_callbacks
from modules import shared, ui_tempdir, script_callbacks, processing
from PIL import Image
re_param_code = r'\s*([\w ]+):\s*("(?:\\"[^,]|\\"|\\|[^\"])+"|[^,]*)(?:,|$)'
@ -198,7 +198,6 @@ def restore_old_hires_fix_params(res):
height = int(res.get("Size-2", 512))
if firstpass_width == 0 or firstpass_height == 0:
from modules import processing
firstpass_width, firstpass_height = processing.old_hires_fix_first_pass_dimensions(width, height)
res['Size-1'] = firstpass_width

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@ -21,8 +21,6 @@ from modules import sd_samplers, shared, script_callbacks, errors
from modules.paths_internal import roboto_ttf_file
from modules.shared import opts
import modules.sd_vae as sd_vae
LANCZOS = (Image.Resampling.LANCZOS if hasattr(Image, 'Resampling') else Image.LANCZOS)
@ -342,16 +340,6 @@ def sanitize_filename_part(text, replace_spaces=True):
class FilenameGenerator:
def get_vae_filename(self): #get the name of the VAE file.
if sd_vae.loaded_vae_file is None:
return "NoneType"
file_name = os.path.basename(sd_vae.loaded_vae_file)
split_file_name = file_name.split('.')
if len(split_file_name) > 1 and split_file_name[0] == '':
return split_file_name[1] # if the first character of the filename is "." then [1] is obtained.
else:
return split_file_name[0]
replacements = {
'seed': lambda self: self.seed if self.seed is not None else '',
'seed_first': lambda self: self.seed if self.p.batch_size == 1 else self.p.all_seeds[0],
@ -391,6 +379,22 @@ class FilenameGenerator:
self.image = image
self.zip = zip
def get_vae_filename(self):
"""Get the name of the VAE file."""
import modules.sd_vae as sd_vae
if sd_vae.loaded_vae_file is None:
return "NoneType"
file_name = os.path.basename(sd_vae.loaded_vae_file)
split_file_name = file_name.split('.')
if len(split_file_name) > 1 and split_file_name[0] == '':
return split_file_name[1] # if the first character of the filename is "." then [1] is obtained.
else:
return split_file_name[0]
def hasprompt(self, *args):
lower = self.prompt.lower()
if self.p is None or self.prompt is None:

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@ -1,7 +1,7 @@
import json
import os
from modules import errors
from modules import errors, scripts
localizations = {}
@ -16,7 +16,6 @@ def list_localizations(dirname):
localizations[fn] = os.path.join(dirname, file)
from modules import scripts
for file in scripts.list_scripts("localizations", ".json"):
fn, ext = os.path.splitext(file.filename)
localizations[fn] = file.path

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@ -4,6 +4,7 @@ import torch
import platform
from modules.sd_hijack_utils import CondFunc
from packaging import version
from modules import shared
log = logging.getLogger(__name__)
@ -30,8 +31,7 @@ has_mps = check_for_mps()
def torch_mps_gc() -> None:
try:
from modules.shared import state
if state.current_latent is not None:
if shared.state.current_latent is not None:
log.debug("`current_latent` is set, skipping MPS garbage collection")
return
from torch.mps import empty_cache

236
modules/options.py Normal file
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@ -0,0 +1,236 @@
import json
import sys
import gradio as gr
from modules import errors
from modules.shared_cmd_options import cmd_opts
class OptionInfo:
def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after=''):
self.default = default
self.label = label
self.component = component
self.component_args = component_args
self.onchange = onchange
self.section = section
self.refresh = refresh
self.do_not_save = False
self.comment_before = comment_before
"""HTML text that will be added after label in UI"""
self.comment_after = comment_after
"""HTML text that will be added before label in UI"""
def link(self, label, url):
self.comment_before += f"[<a href='{url}' target='_blank'>{label}</a>]"
return self
def js(self, label, js_func):
self.comment_before += f"[<a onclick='{js_func}(); return false'>{label}</a>]"
return self
def info(self, info):
self.comment_after += f"<span class='info'>({info})</span>"
return self
def html(self, html):
self.comment_after += html
return self
def needs_restart(self):
self.comment_after += " <span class='info'>(requires restart)</span>"
return self
def needs_reload_ui(self):
self.comment_after += " <span class='info'>(requires Reload UI)</span>"
return self
class OptionHTML(OptionInfo):
def __init__(self, text):
super().__init__(str(text).strip(), label='', component=lambda **kwargs: gr.HTML(elem_classes="settings-info", **kwargs))
self.do_not_save = True
def options_section(section_identifier, options_dict):
for v in options_dict.values():
v.section = section_identifier
return options_dict
options_builtin_fields = {"data_labels", "data", "restricted_opts", "typemap"}
class Options:
typemap = {int: float}
def __init__(self, data_labels, restricted_opts):
self.data_labels = data_labels
self.data = {k: v.default for k, v in self.data_labels.items()}
self.restricted_opts = restricted_opts
def __setattr__(self, key, value):
if key in options_builtin_fields:
return super(Options, self).__setattr__(key, value)
if self.data is not None:
if key in self.data or key in self.data_labels:
assert not cmd_opts.freeze_settings, "changing settings is disabled"
info = self.data_labels.get(key, None)
if info.do_not_save:
return
comp_args = info.component_args if info else None
if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
raise RuntimeError(f"not possible to set {key} because it is restricted")
if cmd_opts.hide_ui_dir_config and key in self.restricted_opts:
raise RuntimeError(f"not possible to set {key} because it is restricted")
self.data[key] = value
return
return super(Options, self).__setattr__(key, value)
def __getattr__(self, item):
if item in options_builtin_fields:
return super(Options, self).__getattribute__(item)
if self.data is not None:
if item in self.data:
return self.data[item]
if item in self.data_labels:
return self.data_labels[item].default
return super(Options, self).__getattribute__(item)
def set(self, key, value):
"""sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""
oldval = self.data.get(key, None)
if oldval == value:
return False
if self.data_labels[key].do_not_save:
return False
try:
setattr(self, key, value)
except RuntimeError:
return False
if self.data_labels[key].onchange is not None:
try:
self.data_labels[key].onchange()
except Exception as e:
errors.display(e, f"changing setting {key} to {value}")
setattr(self, key, oldval)
return False
return True
def get_default(self, key):
"""returns the default value for the key"""
data_label = self.data_labels.get(key)
if data_label is None:
return None
return data_label.default
def save(self, filename):
assert not cmd_opts.freeze_settings, "saving settings is disabled"
with open(filename, "w", encoding="utf8") as file:
json.dump(self.data, file, indent=4)
def same_type(self, x, y):
if x is None or y is None:
return True
type_x = self.typemap.get(type(x), type(x))
type_y = self.typemap.get(type(y), type(y))
return type_x == type_y
def load(self, filename):
with open(filename, "r", encoding="utf8") as file:
self.data = json.load(file)
# 1.6.0 VAE defaults
if self.data.get('sd_vae_as_default') is not None and self.data.get('sd_vae_overrides_per_model_preferences') is None:
self.data['sd_vae_overrides_per_model_preferences'] = not self.data.get('sd_vae_as_default')
# 1.1.1 quicksettings list migration
if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:
self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]
# 1.4.0 ui_reorder
if isinstance(self.data.get('ui_reorder'), str) and self.data.get('ui_reorder') and "ui_reorder_list" not in self.data:
self.data['ui_reorder_list'] = [i.strip() for i in self.data.get('ui_reorder').split(',')]
bad_settings = 0
for k, v in self.data.items():
info = self.data_labels.get(k, None)
if info is not None and not self.same_type(info.default, v):
print(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})", file=sys.stderr)
bad_settings += 1
if bad_settings > 0:
print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)
def onchange(self, key, func, call=True):
item = self.data_labels.get(key)
item.onchange = func
if call:
func()
def dumpjson(self):
d = {k: self.data.get(k, v.default) for k, v in self.data_labels.items()}
d["_comments_before"] = {k: v.comment_before for k, v in self.data_labels.items() if v.comment_before is not None}
d["_comments_after"] = {k: v.comment_after for k, v in self.data_labels.items() if v.comment_after is not None}
return json.dumps(d)
def add_option(self, key, info):
self.data_labels[key] = info
def reorder(self):
"""reorder settings so that all items related to section always go together"""
section_ids = {}
settings_items = self.data_labels.items()
for _, item in settings_items:
if item.section not in section_ids:
section_ids[item.section] = len(section_ids)
self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))
def cast_value(self, key, value):
"""casts an arbitrary to the same type as this setting's value with key
Example: cast_value("eta_noise_seed_delta", "12") -> returns 12 (an int rather than str)
"""
if value is None:
return None
default_value = self.data_labels[key].default
if default_value is None:
default_value = getattr(self, key, None)
if default_value is None:
return None
expected_type = type(default_value)
if expected_type == bool and value == "False":
value = False
else:
value = expected_type(value)
return value

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@ -63,9 +63,8 @@ def randn_without_seed(shape, generator=None):
def manual_seed(seed):
"""Set up a global random number generator using the specified seed."""
from modules.shared import opts
if opts.randn_source == "NV":
if shared.opts.randn_source == "NV":
global nv_rng
nv_rng = rng_philox.Generator(seed)
return

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@ -14,7 +14,7 @@ import ldm.modules.midas as midas
from ldm.util import instantiate_from_config
from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache
from modules import paths, shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors, hashes, sd_models_config, sd_unet, sd_models_xl, cache, extra_networks, processing, lowvram, sd_hijack
from modules.timer import Timer
import tomesd
@ -473,7 +473,6 @@ model_data = SdModelData()
def get_empty_cond(sd_model):
from modules import extra_networks, processing
p = processing.StableDiffusionProcessingTxt2Img()
extra_networks.activate(p, {})
@ -486,8 +485,6 @@ def get_empty_cond(sd_model):
def send_model_to_cpu(m):
from modules import lowvram
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
lowvram.send_everything_to_cpu()
else:
@ -497,8 +494,6 @@ def send_model_to_cpu(m):
def send_model_to_device(m):
from modules import lowvram
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
lowvram.setup_for_low_vram(m, shared.cmd_opts.medvram)
else:
@ -644,7 +639,6 @@ def reuse_model_from_already_loaded(sd_model, checkpoint_info, timer):
def reload_model_weights(sd_model=None, info=None):
from modules import devices, sd_hijack
checkpoint_info = info or select_checkpoint()
timer = Timer()
@ -707,7 +701,6 @@ def reload_model_weights(sd_model=None, info=None):
def unload_model_weights(sd_model=None, info=None):
from modules import devices, sd_hijack
timer = Timer()
if model_data.sd_model:

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@ -2,7 +2,7 @@ import os
import torch
from modules import shared, paths, sd_disable_initialization
from modules import shared, paths, sd_disable_initialization, devices
sd_configs_path = shared.sd_configs_path
sd_repo_configs_path = os.path.join(paths.paths['Stable Diffusion'], "configs", "stable-diffusion")
@ -29,7 +29,6 @@ def is_using_v_parameterization_for_sd2(state_dict):
"""
import ldm.modules.diffusionmodules.openaimodel
from modules import devices
device = devices.cpu

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@ -2,7 +2,8 @@ import os
import collections
from dataclasses import dataclass
from modules import paths, shared, devices, script_callbacks, sd_models, extra_networks
from modules import paths, shared, devices, script_callbacks, sd_models, extra_networks, lowvram, sd_hijack
import glob
from copy import deepcopy
@ -231,8 +232,6 @@ unspecified = object()
def reload_vae_weights(sd_model=None, vae_file=unspecified):
from modules import lowvram, devices, sd_hijack
if not sd_model:
sd_model = shared.sd_model

File diff suppressed because it is too large Load Diff

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@ -0,0 +1,18 @@
import os
import launch
from modules import cmd_args, script_loading
from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir # noqa: F401
parser = cmd_args.parser
script_loading.preload_extensions(extensions_dir, parser, extension_list=launch.list_extensions(launch.args.ui_settings_file))
script_loading.preload_extensions(extensions_builtin_dir, parser)
if os.environ.get('IGNORE_CMD_ARGS_ERRORS', None) is None:
cmd_opts = parser.parse_args()
else:
cmd_opts, _ = parser.parse_known_args()
cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access

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@ -0,0 +1,66 @@
import os
import gradio as gr
from modules import errors, shared
from modules.paths_internal import script_path
# https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json
gradio_hf_hub_themes = [
"gradio/base",
"gradio/glass",
"gradio/monochrome",
"gradio/seafoam",
"gradio/soft",
"gradio/dracula_test",
"abidlabs/dracula_test",
"abidlabs/Lime",
"abidlabs/pakistan",
"Ama434/neutral-barlow",
"dawood/microsoft_windows",
"finlaymacklon/smooth_slate",
"Franklisi/darkmode",
"freddyaboulton/dracula_revamped",
"freddyaboulton/test-blue",
"gstaff/xkcd",
"Insuz/Mocha",
"Insuz/SimpleIndigo",
"JohnSmith9982/small_and_pretty",
"nota-ai/theme",
"nuttea/Softblue",
"ParityError/Anime",
"reilnuud/polite",
"remilia/Ghostly",
"rottenlittlecreature/Moon_Goblin",
"step-3-profit/Midnight-Deep",
"Taithrah/Minimal",
"ysharma/huggingface",
"ysharma/steampunk"
]
def reload_gradio_theme(theme_name=None):
if not theme_name:
theme_name = shared.opts.gradio_theme
default_theme_args = dict(
font=["Source Sans Pro", 'ui-sans-serif', 'system-ui', 'sans-serif'],
font_mono=['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'],
)
if theme_name == "Default":
shared.gradio_theme = gr.themes.Default(**default_theme_args)
else:
try:
theme_cache_dir = os.path.join(script_path, 'tmp', 'gradio_themes')
theme_cache_path = os.path.join(theme_cache_dir, f'{theme_name.replace("/", "_")}.json')
if shared.opts.gradio_themes_cache and os.path.exists(theme_cache_path):
shared.gradio_theme = gr.themes.ThemeClass.load(theme_cache_path)
else:
os.makedirs(theme_cache_dir, exist_ok=True)
gradio_theme = gr.themes.ThemeClass.from_hub(theme_name)
gradio_theme.dump(theme_cache_path)
except Exception as e:
errors.display(e, "changing gradio theme")
shared.gradio_theme = gr.themes.Default(**default_theme_args)

51
modules/shared_init.py Normal file
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@ -0,0 +1,51 @@
import os
import torch
from modules import shared
from modules.shared import cmd_opts
import sys
sys.setrecursionlimit(1000)
def initialize():
"""Initializes fields inside the shared module in a controlled manner.
Should be called early because some other modules you can import mingt need these fields to be already set.
"""
os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
from modules import options, shared_options
shared.options_templates = shared_options.options_templates
shared.opts = options.Options(shared_options.options_templates, shared_options.restricted_opts)
if os.path.exists(shared.config_filename):
shared.opts.load(shared.config_filename)
from modules import devices
devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
(devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
devices.dtype = torch.float32 if cmd_opts.no_half else torch.float16
devices.dtype_vae = torch.float32 if cmd_opts.no_half or cmd_opts.no_half_vae else torch.float16
shared.device = devices.device
shared.weight_load_location = None if cmd_opts.lowram else "cpu"
from modules import shared_state
shared.state = shared_state.State()
from modules import styles
shared.prompt_styles = styles.StyleDatabase(shared.styles_filename)
from modules import interrogate
shared.interrogator = interrogate.InterrogateModels("interrogate")
from modules import shared_total_tqdm
shared.total_tqdm = shared_total_tqdm.TotalTQDM()
from modules import memmon, devices
shared.mem_mon = memmon.MemUsageMonitor("MemMon", devices.device, shared.opts)
shared.mem_mon.start()

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@ -1,3 +1,6 @@
import sys
from modules.shared_cmd_options import cmd_opts
def realesrgan_models_names():
@ -41,6 +44,28 @@ def refresh_unet_list():
modules.sd_unet.list_unets()
def list_checkpoint_tiles():
import modules.sd_models
return modules.sd_models.checkpoint_tiles()
def refresh_checkpoints():
import modules.sd_models
return modules.sd_models.list_models()
def list_samplers():
import modules.sd_samplers
return modules.sd_samplers.all_samplers
def reload_hypernetworks():
from modules.hypernetworks import hypernetwork
from modules import shared
shared.hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)
ui_reorder_categories_builtin_items = [
"inpaint",
"sampler",
@ -67,3 +92,27 @@ def ui_reorder_categories():
yield from sections
yield "scripts"
class Shared(sys.modules[__name__].__class__):
"""
this class is here to provide sd_model field as a property, so that it can be created and loaded on demand rather than
at program startup.
"""
sd_model_val = None
@property
def sd_model(self):
import modules.sd_models
return modules.sd_models.model_data.get_sd_model()
@sd_model.setter
def sd_model(self, value):
import modules.sd_models
modules.sd_models.model_data.set_sd_model(value)
sys.modules['modules.shared'].__class__ = Shared

316
modules/shared_options.py Normal file
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@ -0,0 +1,316 @@
import gradio as gr
from modules import localization, ui_components, shared_items, shared, interrogate, shared_gradio_themes
from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir # noqa: F401
from modules.shared_cmd_options import cmd_opts
from modules.options import options_section, OptionInfo, OptionHTML
options_templates = {}
hide_dirs = shared.hide_dirs
restricted_opts = {
"samples_filename_pattern",
"directories_filename_pattern",
"outdir_samples",
"outdir_txt2img_samples",
"outdir_img2img_samples",
"outdir_extras_samples",
"outdir_grids",
"outdir_txt2img_grids",
"outdir_save",
"outdir_init_images"
}
options_templates.update(options_section(('saving-images', "Saving images/grids"), {
"samples_save": OptionInfo(True, "Always save all generated images"),
"samples_format": OptionInfo('png', 'File format for images'),
"samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
"save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
"grid_save": OptionInfo(True, "Always save all generated image grids"),
"grid_format": OptionInfo('png', 'File format for grids'),
"grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
"grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
"grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
"grid_zip_filename_pattern": OptionInfo("", "Archive filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
"n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
"font": OptionInfo("", "Font for image grids that have text"),
"grid_text_active_color": OptionInfo("#000000", "Text color for image grids", ui_components.FormColorPicker, {}),
"grid_text_inactive_color": OptionInfo("#999999", "Inactive text color for image grids", ui_components.FormColorPicker, {}),
"grid_background_color": OptionInfo("#ffffff", "Background color for image grids", ui_components.FormColorPicker, {}),
"enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
"save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
"save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
"save_mask": OptionInfo(False, "For inpainting, save a copy of the greyscale mask"),
"save_mask_composite": OptionInfo(False, "For inpainting, save a masked composite"),
"jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
"webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
"export_for_4chan": OptionInfo(True, "Save copy of large images as JPG").info("if the file size is above the limit, or either width or height are above the limit"),
"img_downscale_threshold": OptionInfo(4.0, "File size limit for the above option, MB", gr.Number),
"target_side_length": OptionInfo(4000, "Width/height limit for the above option, in pixels", gr.Number),
"img_max_size_mp": OptionInfo(200, "Maximum image size", gr.Number).info("in megapixels"),
"use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
"use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
"save_init_img": OptionInfo(False, "Save init images when using img2img"),
"temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default"),
"clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),
"save_incomplete_images": OptionInfo(False, "Save incomplete images").info("save images that has been interrupted in mid-generation; even if not saved, they will still show up in webui output."),
}))
options_templates.update(options_section(('saving-paths', "Paths for saving"), {
"outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
"outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
"outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
"outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output directory for images from extras tab', component_args=hide_dirs),
"outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below", component_args=hide_dirs),
"outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
"outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
"outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
"outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
}))
options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
"save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
"grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
"use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
"directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
"directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
}))
options_templates.update(options_section(('upscaling', "Upscaling"), {
"ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}).info("0 = no tiling"),
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}).info("Low values = visible seam"),
"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI.", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in shared.sd_upscalers]}),
}))
options_templates.update(options_section(('face-restoration', "Face restoration"), {
"face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in shared.face_restorers]}),
"code_former_weight": OptionInfo(0.5, "CodeFormer weight", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}).info("0 = maximum effect; 1 = minimum effect"),
"face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
}))
options_templates.update(options_section(('system', "System"), {
"auto_launch_browser": OptionInfo("Local", "Automatically open webui in browser on startup", gr.Radio, lambda: {"choices": ["Disable", "Local", "Remote"]}),
"show_warnings": OptionInfo(False, "Show warnings in console.").needs_reload_ui(),
"show_gradio_deprecation_warnings": OptionInfo(True, "Show gradio deprecation warnings in console.").needs_reload_ui(),
"memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}).info("0 = disable"),
"samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
"multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
"print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
"list_hidden_files": OptionInfo(True, "Load models/files in hidden directories").info("directory is hidden if its name starts with \".\""),
"disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
"hide_ldm_prints": OptionInfo(True, "Prevent Stability-AI's ldm/sgm modules from printing noise to console."),
}))
options_templates.update(options_section(('training', "Training"), {
"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
"pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
"save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
"save_training_settings_to_txt": OptionInfo(True, "Save textual inversion and hypernet settings to a text file whenever training starts."),
"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"),
"training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging."),
"training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard."),
"training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."),
}))
options_templates.update(options_section(('sd', "Stable Diffusion"), {
"sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": shared_items.list_checkpoint_tiles()}, refresh=shared_items.refresh_checkpoints),
"sd_checkpoints_limit": OptionInfo(1, "Maximum number of checkpoints loaded at the same time", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
"sd_checkpoints_keep_in_cpu": OptionInfo(True, "Only keep one model on device").info("will keep models other than the currently used one in RAM rather than VRAM"),
"sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}).info("obsolete; set to 0 and use the two settings above instead"),
"sd_unet": OptionInfo("Automatic", "SD Unet", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list).info("choose Unet model: Automatic = use one with same filename as checkpoint; None = use Unet from checkpoint"),
"enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds").needs_reload_ui(),
"enable_emphasis": OptionInfo(True, "Enable emphasis").info("use (text) to make model pay more attention to text and [text] to make it pay less attention"),
"enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
"comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
"randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU", "NV"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors; use NV to produce same picture as on NVidia videocards"),
}))
options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
"sdxl_crop_top": OptionInfo(0, "crop top coordinate"),
"sdxl_crop_left": OptionInfo(0, "crop left coordinate"),
"sdxl_refiner_low_aesthetic_score": OptionInfo(2.5, "SDXL low aesthetic score", gr.Number).info("used for refiner model negative prompt"),
"sdxl_refiner_high_aesthetic_score": OptionInfo(6.0, "SDXL high aesthetic score", gr.Number).info("used for refiner model prompt"),
}))
options_templates.update(options_section(('vae', "VAE"), {
"sd_vae_explanation": OptionHTML("""
<abbr title='Variational autoencoder'>VAE</abbr> is a neural network that transforms a standard <abbr title='red/green/blue'>RGB</abbr>
image into latent space representation and back. Latent space representation is what stable diffusion is working on during sampling
(i.e. when the progress bar is between empty and full). For txt2img, VAE is used to create a resulting image after the sampling is finished.
For img2img, VAE is used to process user's input image before the sampling, and to create an image after sampling.
"""),
"sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
"sd_vae_overrides_per_model_preferences": OptionInfo(True, "Selected VAE overrides per-model preferences").info("you can set per-model VAE either by editing user metadata for checkpoints, or by making the VAE have same name as checkpoint"),
"auto_vae_precision": OptionInfo(True, "Automatically revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),
"sd_vae_encode_method": OptionInfo("Full", "VAE type for encode", gr.Radio, {"choices": ["Full", "TAESD"]}).info("method to encode image to latent (use in img2img, hires-fix or inpaint mask)"),
"sd_vae_decode_method": OptionInfo("Full", "VAE type for decode", gr.Radio, {"choices": ["Full", "TAESD"]}).info("method to decode latent to image"),
}))
options_templates.update(options_section(('img2img', "img2img"), {
"inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
"img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
"img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies.").info("normally you'd do less with less denoising"),
"img2img_background_color": OptionInfo("#ffffff", "With img2img, fill transparent parts of the input image with this color.", ui_components.FormColorPicker, {}),
"img2img_editor_height": OptionInfo(720, "Height of the image editor", gr.Slider, {"minimum": 80, "maximum": 1600, "step": 1}).info("in pixels").needs_reload_ui(),
"img2img_sketch_default_brush_color": OptionInfo("#ffffff", "Sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img sketch").needs_reload_ui(),
"img2img_inpaint_mask_brush_color": OptionInfo("#ffffff", "Inpaint mask brush color", ui_components.FormColorPicker, {}).info("brush color of inpaint mask").needs_reload_ui(),
"img2img_inpaint_sketch_default_brush_color": OptionInfo("#ffffff", "Inpaint sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img inpaint sketch").needs_reload_ui(),
"return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
"return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
}))
options_templates.update(options_section(('optimizations', "Optimizations"), {
"cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
"s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
"token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
"token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
"token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
"pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt to be same length").info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
"persistent_cond_cache": OptionInfo(True, "Persistent cond cache").info("Do not recalculate conds from prompts if prompts have not changed since previous calculation"),
}))
options_templates.update(options_section(('compatibility', "Compatibility"), {
"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
"use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
"no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
"use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
"dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
"hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),
}))
options_templates.update(options_section(('interrogate', "Interrogate"), {
"interrogate_keep_models_in_memory": OptionInfo(False, "Keep models in VRAM"),
"interrogate_return_ranks": OptionInfo(False, "Include ranks of model tags matches in results.").info("booru only"),
"interrogate_clip_num_beams": OptionInfo(1, "BLIP: num_beams", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
"interrogate_clip_min_length": OptionInfo(24, "BLIP: minimum description length", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
"interrogate_clip_max_length": OptionInfo(48, "BLIP: 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").info("0 = No limit"),
"interrogate_clip_skip_categories": OptionInfo([], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": interrogate.category_types()}, refresh=interrogate.category_types),
"interrogate_deepbooru_score_threshold": OptionInfo(0.5, "deepbooru: score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
"deepbooru_sort_alpha": OptionInfo(True, "deepbooru: sort tags alphabetically").info("if not: sort by score"),
"deepbooru_use_spaces": OptionInfo(True, "deepbooru: use spaces in tags").info("if not: use underscores"),
"deepbooru_escape": OptionInfo(True, "deepbooru: escape (\\) brackets").info("so they are used as literal brackets and not for emphasis"),
"deepbooru_filter_tags": OptionInfo("", "deepbooru: filter out those tags").info("separate by comma"),
}))
options_templates.update(options_section(('extra_networks', "Extra Networks"), {
"extra_networks_show_hidden_directories": OptionInfo(True, "Show hidden directories").info("directory is hidden if its name starts with \".\"."),
"extra_networks_hidden_models": OptionInfo("When searched", "Show cards for models in hidden directories", gr.Radio, {"choices": ["Always", "When searched", "Never"]}).info('"When searched" option will only show the item when the search string has 4 characters or more'),
"extra_networks_default_multiplier": OptionInfo(1.0, "Default multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}),
"extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks").info("in pixels"),
"extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
"extra_networks_card_text_scale": OptionInfo(1.0, "Card text scale", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}).info("1 = original size"),
"extra_networks_card_show_desc": OptionInfo(True, "Show description on card"),
"extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_reload_ui(),
"textual_inversion_print_at_load": OptionInfo(False, "Print a list of Textual Inversion embeddings when loading model"),
"textual_inversion_add_hashes_to_infotext": OptionInfo(True, "Add Textual Inversion hashes to infotext"),
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *shared.hypernetworks]}, refresh=shared_items.reload_hypernetworks),
}))
options_templates.update(options_section(('ui', "User interface"), {
"localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_reload_ui(),
"gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + shared_gradio_themes.gradio_hf_hub_themes}).info("you can also manually enter any of themes from the <a href='https://huggingface.co/spaces/gradio/theme-gallery'>gallery</a>.").needs_reload_ui(),
"gradio_themes_cache": OptionInfo(True, "Cache gradio themes locally").info("disable to update the selected Gradio theme"),
"return_grid": OptionInfo(True, "Show grid in results for web"),
"do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
"js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
"js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
"js_modal_lightbox_gamepad": OptionInfo(False, "Navigate image viewer with gamepad"),
"js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Gamepad repeat period, in milliseconds"),
"show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
"samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group").needs_reload_ui(),
"dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_reload_ui(),
"keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_delimiters": OptionInfo(".,\\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
"keyedit_move": OptionInfo(True, "Alt+left/right moves prompt elements"),
"quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(shared.opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_reload_ui(),
"ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
"hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(shared.tab_names)}).needs_reload_ui(),
"ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_reload_ui(),
"hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_reload_ui(),
"hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_reload_ui(),
"disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_reload_ui(),
}))
options_templates.update(options_section(('infotext', "Infotext"), {
"add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
"add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
"add_user_name_to_info": OptionInfo(False, "Add user name to generation information when authenticated"),
"add_version_to_infotext": OptionInfo(True, "Add program version to generation information"),
"disable_weights_auto_swap": OptionInfo(True, "Disregard checkpoint information from pasted infotext").info("when reading generation parameters from text into UI"),
"infotext_styles": OptionInfo("Apply if any", "Infer styles from prompts of pasted infotext", gr.Radio, {"choices": ["Ignore", "Apply", "Discard", "Apply if any"]}).info("when reading generation parameters from text into UI)").html("""<ul style='margin-left: 1.5em'>
<li>Ignore: keep prompt and styles dropdown as it is.</li>
<li>Apply: remove style text from prompt, always replace styles dropdown value with found styles (even if none are found).</li>
<li>Discard: remove style text from prompt, keep styles dropdown as it is.</li>
<li>Apply if any: remove style text from prompt; if any styles are found in prompt, put them into styles dropdown, otherwise keep it as it is.</li>
</ul>"""),
}))
options_templates.update(options_section(('ui', "Live previews"), {
"show_progressbar": OptionInfo(True, "Show progressbar"),
"live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
"live_previews_image_format": OptionInfo("png", "Live preview file format", gr.Radio, {"choices": ["jpeg", "png", "webp"]}),
"show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
"show_progress_every_n_steps": OptionInfo(10, "Live preview display period", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}).info("in sampling steps - show new live preview image every N sampling steps; -1 = only show after completion of batch"),
"show_progress_type": OptionInfo("Approx NN", "Live preview method", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap", "TAESD"]}).info("Full = slow but pretty; Approx NN and TAESD = fast but low quality; Approx cheap = super fast but terrible otherwise"),
"live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
"live_preview_refresh_period": OptionInfo(1000, "Progressbar and preview update period").info("in milliseconds"),
}))
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
"hide_samplers": OptionInfo([], "Hide samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in shared_items.list_samplers()]}).needs_reload_ui(),
"eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; higher = more unperdictable results"),
"eta_ancestral": OptionInfo(1.0, "Eta for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; applies to Euler a and other samplers that have a in them"),
"ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 100.0, "step": 0.01}).info('amount of stochasticity; only applies to Euler, Heun, and DPM2'),
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 10.0, "step": 0.01}).info('enable stochasticity; start value of the sigma range; only applies to Euler, Heun, and DPM2'),
's_tmax': OptionInfo(0.0, "sigma tmax", gr.Slider, {"minimum": 0.0, "maximum": 999.0, "step": 0.01}).info("0 = inf; end value of the sigma range; only applies to Euler, Heun, and DPM2"),
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.1, "step": 0.001}).info('amount of additional noise to counteract loss of detail during sampling; only applies to Euler, Heun, and DPM2'),
'k_sched_type': OptionInfo("Automatic", "Scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}).info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
'sigma_min': OptionInfo(0.0, "sigma min", gr.Number).info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
'sigma_max': OptionInfo(0.0, "sigma max", gr.Number).info("0 = default (~14.6); maximum noise strength for k-diffusion noise scheduler"),
'rho': OptionInfo(0.0, "rho", gr.Number).info("0 = default (7 for karras, 1 for polyexponential); higher values result in a steeper noise schedule (decreases faster)"),
'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}).info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),
'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma").link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
'uni_pc_order': OptionInfo(3, "UniPC order", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}).info("must be < sampling steps"),
'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),
}))
options_templates.update(options_section(('postprocessing', "Postprocessing"), {
'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
}))
options_templates.update(options_section((None, "Hidden options"), {
"disabled_extensions": OptionInfo([], "Disable these extensions"),
"disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "extra", "all"]}),
"restore_config_state_file": OptionInfo("", "Config state file to restore from, under 'config-states/' folder"),
"sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
}))

159
modules/shared_state.py Normal file
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@ -0,0 +1,159 @@
import datetime
import logging
import threading
import time
from modules import errors, shared, devices
from typing import Optional
log = logging.getLogger(__name__)
class State:
skipped = False
interrupted = False
job = ""
job_no = 0
job_count = 0
processing_has_refined_job_count = False
job_timestamp = '0'
sampling_step = 0
sampling_steps = 0
current_latent = None
current_image = None
current_image_sampling_step = 0
id_live_preview = 0
textinfo = None
time_start = None
server_start = None
_server_command_signal = threading.Event()
_server_command: Optional[str] = None
def __init__(self):
self.server_start = time.time()
@property
def need_restart(self) -> bool:
# Compatibility getter for need_restart.
return self.server_command == "restart"
@need_restart.setter
def need_restart(self, value: bool) -> None:
# Compatibility setter for need_restart.
if value:
self.server_command = "restart"
@property
def server_command(self):
return self._server_command
@server_command.setter
def server_command(self, value: Optional[str]) -> None:
"""
Set the server command to `value` and signal that it's been set.
"""
self._server_command = value
self._server_command_signal.set()
def wait_for_server_command(self, timeout: Optional[float] = None) -> Optional[str]:
"""
Wait for server command to get set; return and clear the value and signal.
"""
if self._server_command_signal.wait(timeout):
self._server_command_signal.clear()
req = self._server_command
self._server_command = None
return req
return None
def request_restart(self) -> None:
self.interrupt()
self.server_command = "restart"
log.info("Received restart request")
def skip(self):
self.skipped = True
log.info("Received skip request")
def interrupt(self):
self.interrupted = True
log.info("Received interrupt request")
def nextjob(self):
if shared.opts.live_previews_enable and shared.opts.show_progress_every_n_steps == -1:
self.do_set_current_image()
self.job_no += 1
self.sampling_step = 0
self.current_image_sampling_step = 0
def dict(self):
obj = {
"skipped": self.skipped,
"interrupted": self.interrupted,
"job": self.job,
"job_count": self.job_count,
"job_timestamp": self.job_timestamp,
"job_no": self.job_no,
"sampling_step": self.sampling_step,
"sampling_steps": self.sampling_steps,
}
return obj
def begin(self, job: str = "(unknown)"):
self.sampling_step = 0
self.job_count = -1
self.processing_has_refined_job_count = False
self.job_no = 0
self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
self.current_latent = None
self.current_image = None
self.current_image_sampling_step = 0
self.id_live_preview = 0
self.skipped = False
self.interrupted = False
self.textinfo = None
self.time_start = time.time()
self.job = job
devices.torch_gc()
log.info("Starting job %s", job)
def end(self):
duration = time.time() - self.time_start
log.info("Ending job %s (%.2f seconds)", self.job, duration)
self.job = ""
self.job_count = 0
devices.torch_gc()
def set_current_image(self):
"""sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
if not shared.parallel_processing_allowed:
return
if self.sampling_step - self.current_image_sampling_step >= shared.opts.show_progress_every_n_steps and shared.opts.live_previews_enable and shared.opts.show_progress_every_n_steps != -1:
self.do_set_current_image()
def do_set_current_image(self):
if self.current_latent is None:
return
import modules.sd_samplers
try:
if shared.opts.show_progress_grid:
self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent))
else:
self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent))
self.current_image_sampling_step = self.sampling_step
except Exception:
# when switching models during genration, VAE would be on CPU, so creating an image will fail.
# we silently ignore this error
errors.record_exception()
def assign_current_image(self, image):
self.current_image = image
self.id_live_preview += 1

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@ -0,0 +1,37 @@
import tqdm
from modules import shared
class TotalTQDM:
def __init__(self):
self._tqdm = None
def reset(self):
self._tqdm = tqdm.tqdm(
desc="Total progress",
total=shared.state.job_count * shared.state.sampling_steps,
position=1,
file=shared.progress_print_out
)
def update(self):
if not shared.opts.multiple_tqdm or shared.cmd_opts.disable_console_progressbars:
return
if self._tqdm is None:
self.reset()
self._tqdm.update()
def updateTotal(self, new_total):
if not shared.opts.multiple_tqdm or shared.cmd_opts.disable_console_progressbars:
return
if self._tqdm is None:
self.reset()
self._tqdm.total = new_total
def clear(self):
if self._tqdm is not None:
self._tqdm.refresh()
self._tqdm.close()
self._tqdm = None

View File

@ -10,7 +10,7 @@ import psutil
import re
import launch
from modules import paths_internal, timer
from modules import paths_internal, timer, shared, extensions, errors
checksum_token = "DontStealMyGamePlz__WINNERS_DONT_USE_DRUGS__DONT_COPY_THAT_FLOPPY"
environment_whitelist = {
@ -115,8 +115,6 @@ def format_exception(e, tb):
def get_exceptions():
try:
from modules import errors
return list(reversed(errors.exception_records))
except Exception as e:
return str(e)
@ -142,8 +140,6 @@ def get_torch_sysinfo():
def get_extensions(*, enabled):
try:
from modules import extensions
def to_json(x: extensions.Extension):
return {
"name": x.name,
@ -160,7 +156,6 @@ def get_extensions(*, enabled):
def get_config():
try:
from modules import shared
return shared.opts.data
except Exception as e:
return str(e)

View File

@ -13,7 +13,7 @@ from PIL import Image, PngImagePlugin # noqa: F401
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
from modules import gradio_extensons # noqa: F401
from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles, scripts, sd_samplers
from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, errors, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, ui_prompt_styles, scripts, sd_samplers, processing, devices, ui_extra_networks
from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
from modules.paths import script_path
from modules.ui_common import create_refresh_button
@ -91,8 +91,6 @@ def send_gradio_gallery_to_image(x):
def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
from modules import processing, devices
if not enable:
return ""
@ -630,7 +628,6 @@ def create_ui():
toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps], outputs=[toprow.token_counter])
toprow.negative_token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.negative_prompt, steps], outputs=[toprow.negative_token_counter])
from modules import ui_extra_networks
extra_networks_ui = ui_extra_networks.create_ui(txt2img_interface, [txt2img_generation_tab], 'txt2img')
ui_extra_networks.setup_ui(extra_networks_ui, txt2img_gallery)
@ -995,7 +992,6 @@ def create_ui():
paste_button=toprow.paste, tabname="img2img", source_text_component=toprow.prompt, source_image_component=None,
))
from modules import ui_extra_networks
extra_networks_ui_img2img = ui_extra_networks.create_ui(img2img_interface, [img2img_generation_tab], 'img2img')
ui_extra_networks.setup_ui(extra_networks_ui_img2img, img2img_gallery)

View File

@ -11,7 +11,7 @@ from modules import call_queue, shared
from modules.generation_parameters_copypaste import image_from_url_text
import modules.images
from modules.ui_components import ToolButton
import modules.generation_parameters_copypaste as parameters_copypaste
folder_symbol = '\U0001f4c2' # 📂
refresh_symbol = '\U0001f504' # 🔄
@ -105,8 +105,6 @@ def save_files(js_data, images, do_make_zip, index):
def create_output_panel(tabname, outdir):
from modules import shared
import modules.generation_parameters_copypaste as parameters_copypaste
def open_folder(f):
if not os.path.exists(f):

58
modules/util.py Normal file
View File

@ -0,0 +1,58 @@
import os
import re
from modules import shared
from modules.paths_internal import script_path
def natural_sort_key(s, regex=re.compile('([0-9]+)')):
return [int(text) if text.isdigit() else text.lower() for text in regex.split(s)]
def listfiles(dirname):
filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=natural_sort_key) if not x.startswith(".")]
return [file for file in filenames if os.path.isfile(file)]
def html_path(filename):
return os.path.join(script_path, "html", filename)
def html(filename):
path = html_path(filename)
if os.path.exists(path):
with open(path, encoding="utf8") as file:
return file.read()
return ""
def walk_files(path, allowed_extensions=None):
if not os.path.exists(path):
return
if allowed_extensions is not None:
allowed_extensions = set(allowed_extensions)
items = list(os.walk(path, followlinks=True))
items = sorted(items, key=lambda x: natural_sort_key(x[0]))
for root, _, files in items:
for filename in sorted(files, key=natural_sort_key):
if allowed_extensions is not None:
_, ext = os.path.splitext(filename)
if ext not in allowed_extensions:
continue
if not shared.opts.list_hidden_files and ("/." in root or "\\." in root):
continue
yield os.path.join(root, filename)
def ldm_print(*args, **kwargs):
if shared.opts.hide_ldm_prints:
return
print(*args, **kwargs)

View File

@ -43,12 +43,15 @@ startup_timer.record("import torch")
import gradio # noqa: F401
startup_timer.record("import gradio")
from modules import paths, timer, import_hook, errors, devices # noqa: F401
from modules import paths, timer, import_hook, errors # noqa: F401
startup_timer.record("setup paths")
import ldm.modules.encoders.modules # noqa: F401
startup_timer.record("import ldm")
from modules import shared_init, shared, shared_items
shared_init.initialize()
startup_timer.record("initialize shared")
from modules import extra_networks
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, queue_lock # noqa: F401
@ -58,8 +61,6 @@ if ".dev" in torch.__version__ or "+git" in torch.__version__:
torch.__long_version__ = torch.__version__
torch.__version__ = re.search(r'[\d.]+[\d]', torch.__version__).group(0)
from modules import shared
if not shared.cmd_opts.skip_version_check:
errors.check_versions()
@ -82,7 +83,7 @@ import modules.textual_inversion.textual_inversion
import modules.progress
import modules.ui
from modules import modelloader
from modules import modelloader, devices
from modules.shared import cmd_opts
import modules.hypernetworks.hypernetwork
@ -297,7 +298,7 @@ def initialize_rest(*, reload_script_modules=False):
Thread(target=load_model).start()
shared.reload_hypernetworks()
shared_items.reload_hypernetworks()
startup_timer.record("reload hypernetworks")
ui_extra_networks.initialize()