Merge pull request #12426 from AUTOMATIC1111/split_shared

Split shared.py into multiple files

modules/devices.py

@@ -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
 

modules/extensions.py

@@ -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 []

modules/generation_parameters_copypaste.py

@@ -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

modules/images.py

@@ -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:

modules/localization.py

@@ -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

modules/mac_specific.py

@@ -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

modules/options.py

@@ -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

modules/rng.py

@@ -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

modules/sd_models.py

@@ -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:

modules/sd_models_config.py

@@ -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
 

modules/sd_vae.py

@@ -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
 

modules/shared_cmd_options.py

@@ -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

modules/shared_gradio_themes.py

@@ -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)

modules/shared_init.py

@@ -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()
+

modules/shared_items.py

@@ -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

modules/shared_options.py

@@ -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"),
+}))
+

modules/shared_state.py

@@ -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

modules/shared_total_tqdm.py

@@ -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
+

modules/sysinfo.py

@@ -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)

modules/ui.py

@@ -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)
 

modules/ui_common.py

@@ -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):

modules/util.py

@@ -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)

webui.py

@@ -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()