diff --git a/modules/shared.py b/modules/shared.py
index e34a4a5f7..028056abf 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -1,872 +1,872 @@
-import os
-import sys
-import time
-import json
-import datetime
-import urllib.request
-from enum import Enum
-import gradio as gr
-import tqdm
-import requests
-from modules import errors, ui_components, shared_items, cmd_args
-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 # pylint: disable=W0611
-import modules.interrogate
-import modules.memmon
-import modules.styles
-import modules.devices as devices
-import modules.paths_internal as paths
-from installer import log as central_logger # pylint: disable=E0611
-
-
-errors.install(gr)
-demo: gr.Blocks = None
-log = central_logger
-progress_print_out = sys.stdout
-parser = cmd_args.parser
-url = 'https://github.com/vladmandic/automatic'
-cmd_opts, _ = parser.parse_known_args()
-hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
-xformers_available = False
-clip_model = None
-interrogator = modules.interrogate.InterrogateModels("interrogate")
-sd_upscalers = []
-face_restorers = []
-tab_names = []
-options_templates = {}
-hypernetworks = {}
-loaded_hypernetworks = []
-gradio_theme = gr.themes.Base()
-settings_components = None
-latent_upscale_default_mode = "Latent"
-latent_upscale_modes = {
-    "Latent": {"mode": "bilinear", "antialias": False},
-    "Latent (antialiased)": {"mode": "bilinear", "antialias": True},
-    "Latent (bicubic)": {"mode": "bicubic", "antialias": False},
-    "Latent (bicubic antialiased)": {"mode": "bicubic", "antialias": True},
-    "Latent (nearest)": {"mode": "nearest", "antialias": False},
-    "Latent (nearest-exact)": {"mode": "nearest-exact", "antialias": False},
-}
-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"
-}
-ui_reorder_categories = [
-    "inpaint",
-    "sampler",
-    "checkboxes",
-    "hires_fix",
-    "dimensions",
-    "cfg",
-    "seed",
-    "batch",
-    "override_settings",
-    "scripts",
-]
-
-
-class Backend(Enum):
-    ORIGINAL = 1
-    DIFFUSERS = 2
-
-
-def reload_hypernetworks():
-    from modules.hypernetworks import hypernetwork
-    global hypernetworks # pylint: disable=W0603
-    hypernetworks = hypernetwork.list_hypernetworks(opts.hypernetwork_dir)
-
-
-class State:
-    skipped = False
-    interrupted = False
-    paused = 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
-    need_restart = False
-    server_start = None
-
-    def skip(self):
-        log.debug('Requested skip')
-        self.skipped = True
-
-    def interrupt(self):
-        log.debug('Requested interrupt')
-        self.interrupted = True
-
-    def pause(self):
-        self.paused = not self.paused
-        log.debug(f'Requested {"pause" if self.paused else "continue"}')
-
-    def nextjob(self):
-        if opts.live_previews_enable and 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):
-        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.paused = False
-        self.textinfo = None
-        self.time_start = time.time()
-        devices.torch_gc()
-
-    def end(self):
-        self.job = ""
-        self.job_count = 0
-        self.paused = False
-        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 parallel_processing_allowed:
-            return
-        if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.live_previews_enable and 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 # pylint: disable=W0621
-        if 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
-
-    def assign_current_image(self, image):
-        self.current_image = image
-        self.id_live_preview += 1
-
-state = State()
-state.server_start = time.time()
-
-
-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.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, uri):
-        self.comment_before += f"[<a href='{uri}' 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 needs_restart(self):
-        self.comment_after += " <span class='info'>(requires restart)</span>"
-        return self
-
-
-def options_section(section_identifier, options_dict):
-    for v in options_dict.values():
-        v.section = section_identifier
-    return options_dict
-
-
-def list_checkpoint_tiles():
-    import modules.sd_models # pylint: disable=W0621
-    return modules.sd_models.checkpoint_tiles()
-
-
-default_checkpoint = list_checkpoint_tiles()[0] if len(list_checkpoint_tiles()) > 0 else "model.ckpt"
-
-
-def refresh_checkpoints():
-    import modules.sd_models # pylint: disable=W0621
-    return modules.sd_models.list_models()
-
-
-def list_samplers():
-    import modules.sd_samplers # pylint: disable=W0621
-    modules.sd_samplers.set_samplers()
-    return modules.sd_samplers.all_samplers
-
-def list_themes():
-    fn = os.path.join('html', 'themes.json')
-    if not os.path.exists(fn):
-        refresh_themes()
-    if os.path.exists(fn):
-        with open(fn, mode='r', encoding='utf=8') as f:
-            res = json.loads(f.read())
-    else:
-        res = []
-    builtin = ["black-orange", "gradio/default", "gradio/base", "gradio/glass", "gradio/monochrome", "gradio/soft"]
-    themes = sorted(set(builtin + [x['id'] for x in res if x['status'] == 'RUNNING' and 'test' not in x['id'].lower()]), key=str.casefold)
-    return themes
-
-
-def refresh_themes():
-    try:
-        req = requests.get('https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json', timeout=5)
-        if req.status_code == 200:
-            res = req.json()
-            fn = os.path.join('html', 'themes.json')
-            with open(fn, mode='w', encoding='utf=8') as f:
-                f.write(json.dumps(res))
-        else:
-            log.error('Error refreshing UI themes')
-    except:
-        log.error('Exception refreshing UI themes')
-
-
-if devices.backend == "cpu":
-    cross_attention_optimization_default = "Doggettx's"
-elif devices.backend == "mps":
-    cross_attention_optimization_default = "Doggettx's"
-elif devices.backend == "ipex":
-    cross_attention_optimization_default = "InvokeAI's"
-elif devices.backend == "directml":
-    cross_attention_optimization_default = "Sub-quadratic"
-elif devices.backend == "rocm":
-    cross_attention_optimization_default = "Sub-quadratic"
-else: # cuda
-    cross_attention_optimization_default ="Scaled-Dot-Product"
-
-options_templates.update(options_section(('sd', "Stable Diffusion"), {
-    "sd_model_checkpoint": OptionInfo(default_checkpoint, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
-    "sd_checkpoint_cache": OptionInfo(0, "Number of cached model checkpoints", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
-    "sd_vae_checkpoint_cache": OptionInfo(0, "Number of cached VAE checkpoints", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
-    "sd_vae": OptionInfo("Automatic", "Select VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
-    "sd_vae_sliced_encode": OptionInfo(False, "Enable splitting of hires batch processing"),
-    "stream_load": OptionInfo(False, "When loading models attempt stream loading optimized for slow or network storage"),
-    "model_reuse_dict": OptionInfo(False, "When loading models attempt to reuse previous model dictionary"),
-    "cross_attention_optimization": OptionInfo(cross_attention_optimization_default, "Cross-attention optimization method", gr.Radio, lambda: {"choices": shared_items.list_crossattention() }),
-    "cross_attention_options": OptionInfo([], "Cross-attention advanced options", gr.CheckboxGroup, lambda: {"choices": ['xFormers enable flash Attention', 'SDP disable memory attention']}),
-    "sub_quad_q_chunk_size": OptionInfo(512, "Sub-quadratic cross-attention query chunk size", gr.Slider, {"minimum": 16, "maximum": 8192, "step": 8}),
-    "sub_quad_kv_chunk_size": OptionInfo(512, "Sub-quadratic cross-attention kv chunk size", gr.Slider, {"minimum": 0, "maximum": 8192, "step": 8}),
-    "sub_quad_chunk_threshold": OptionInfo(80, "Sub-quadratic cross-attention chunking threshold", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}),
-    "prompt_attention": OptionInfo("Full parser", "Prompt attention parser", gr.Radio, lambda: {"choices": ["Full parser", "Compel parser", "A1111 parser", "Fixed attention"] }),
-    "prompt_mean_norm": OptionInfo(True, "Prompt attention mean normalization"),
-    "always_batch_cond_uncond": OptionInfo(False, "Disable conditional batching enabled on low memory systems"),
-    "enable_quantization": OptionInfo(True, "Enable samplers quantization for sharper and cleaner results"),
-    "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
-    "sd_backend": OptionInfo("Original", "Stable Diffusion backend (experimental)", gr.Radio, lambda: {"choices": ["Original", "Diffusers"] }),
-}))
-
-options_templates.update(options_section(('cuda', "Compute Settings"), {
-    "memmon_poll_rate": OptionInfo(2, "VRAM usage polls per second during generation", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
-    "precision": OptionInfo("Autocast", "Precision type", gr.Radio, lambda: {"choices": ["Autocast", "Full"]}),
-    "cuda_dtype": OptionInfo("FP32" if sys.platform == "darwin" else "FP16", "Device precision type", gr.Radio, lambda: {"choices": ["FP32", "FP16", "BF16"]}),
-    "no_half": OptionInfo(False, "Use full precision for model (--no-half)", None, None, None),
-    "no_half_vae": OptionInfo(False, "Use full precision for VAE (--no-half-vae)"),
-    "upcast_sampling": OptionInfo(True if sys.platform == "darwin" else False, "Enable upcast sampling"),
-    "upcast_attn": OptionInfo(False, "Enable upcast cross attention layer"),
-    "disable_nan_check": OptionInfo(True, "Disable NaN check in produced images/latent spaces"),
-    "rollback_vae": OptionInfo(False, "Attempt to roll back VAE when produced NaN values, requires NaN check (experimental)"),
-    "opt_channelslast": OptionInfo(False, "Use channels last as torch memory format "),
-    "cudnn_benchmark": OptionInfo(False, "Enable full-depth cuDNN benchmark feature"),
-    "cuda_allow_tf32": OptionInfo(True, "Allow TF32 math ops"),
-    "cuda_allow_tf16_reduced": OptionInfo(True, "Allow TF16 reduced precision math ops"),
-    "cuda_compile": OptionInfo(False, "Enable model compile (experimental)"),
-    "cuda_compile_mode": OptionInfo("none", "Model compile mode (experimental)", gr.Radio, lambda: {"choices": ['none', 'inductor', 'cudagraphs', 'aot_ts_nvfuser', 'hidet', 'ipex']}),
-    "cuda_compile_verbose": OptionInfo(False, "Model compile verbose mode"),
-    "cuda_compile_errors": OptionInfo(True, "Model compile suppress errors"),
-    "disable_gc": OptionInfo(False, "Disable Torch memory garbage collection (experimental)"),
-}))
-
-options_templates.update(options_section(('system-paths', "System Paths"), {
-    "temp_dir":  OptionInfo("", "Directory for temporary images; leave empty for default"),
-    "clean_temp_dir_at_start": OptionInfo(True, "Cleanup non-default temporary directory when starting webui"),
-    "ckpt_dir": OptionInfo(os.path.join(paths.models_path, 'Stable-diffusion'), "Path to directory with stable diffusion checkpoints"),
-    "diffusers_dir": OptionInfo(os.path.join(paths.models_path, 'Diffusers'), "Path to directory with stable diffusion diffusers"),
-    "vae_dir": OptionInfo(os.path.join(paths.models_path, 'VAE'), "Path to directory with VAE files"),
-    "embeddings_dir": OptionInfo(os.path.join(paths.models_path, 'embeddings'), "Embeddings directory for textual inversion"),
-    "hypernetwork_dir": OptionInfo(os.path.join(paths.models_path, 'hypernetworks'), "Hypernetwork directory"),
-    "codeformer_models_path": OptionInfo(os.path.join(paths.models_path, 'Codeformer'), "Path to directory with codeformer model file(s)"),
-    "gfpgan_models_path": OptionInfo(os.path.join(paths.models_path, 'GFPGAN'), "Path to directory with GFPGAN model file(s)"),
-    "esrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'ESRGAN'), "Path to directory with ESRGAN model file(s)"),
-    "bsrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'BSRGAN'), "Path to directory with BSRGAN model file(s)"),
-    "realesrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'RealESRGAN'), "Path to directory with RealESRGAN model file(s)"),
-    "scunet_models_path": OptionInfo(os.path.join(paths.models_path, 'ScuNET'), "Path to directory with ScuNET model file(s)"),
-    "swinir_models_path": OptionInfo(os.path.join(paths.models_path, 'SwinIR'), "Path to directory with SwinIR model file(s)"),
-    "ldsr_models_path": OptionInfo(os.path.join(paths.models_path, 'LDSR'), "Path to directory with LDSR model file(s)"),
-    "clip_models_path": OptionInfo(os.path.join(paths.models_path, 'CLIP'), "Path to directory with CLIP model file(s)"),
-    "lora_dir": OptionInfo(os.path.join(paths.models_path, 'Lora'), "Path to directory with Lora network(s)"),
-    "lyco_dir": OptionInfo(os.path.join(paths.models_path, 'LyCORIS'), "Path to directory with LyCORIS network(s)"),
-    "styles_dir": OptionInfo(os.path.join(paths.data_path, 'styles.csv'), "Path to user-defined styles file"),
-}))
-
-options_templates.update(options_section(('saving-images', "Image Options"), {
-    "samples_save": OptionInfo(True, "Always save all generated images"),
-    "samples_format": OptionInfo('jpg', 'File format for generated images', gr.Dropdown, lambda: {"choices": ["jpg", "png", "webp", "tiff", "jp2"]}),
-    "samples_filename_pattern": OptionInfo("[seed]-[prompt_spaces]", "Images filename pattern", component_args=hide_dirs),
-    "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('jpg', 'File format for grids', gr.Dropdown, lambda: {"choices": ["jpg", "png", "webp", "tiff", "jp2"]}),
-    "grid_extended_filename": OptionInfo(True, "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(True, "Prevent empty spots in grid (when set to autodetect)"),
-    "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}),
-    "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters"),
-    "save_log_fn": OptionInfo("", "Create a JSON log file with image information for each saved image", component_args=hide_dirs),
-    "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, "Save a copy of the inpainting greyscale mask"),
-    "save_mask_composite": OptionInfo(False, "Save a copy of inpainting masked composite"),
-    "save_init_img": OptionInfo(False, "Save a copy of processing init images"),
-    "jpeg_quality": OptionInfo(85, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
-    "webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
-    "img_max_size_mp": OptionInfo(250, "Maximum allowed image size in megapixels", gr.Number),
-    "use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
-    "use_upscaler_name_as_suffix": OptionInfo(True, "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_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
-    "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"),
-    "use_save_to_dirs_for_ui": OptionInfo(False, "Save images to a subdirectory when using Save button"),
-    "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
-    "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(('image-processing', "Image Processing"), {
-    "img2img_color_correction": OptionInfo(False, "Apply color correction to match original colors"),
-    "img2img_fix_steps": OptionInfo(False, "For image processing do exact number of steps as specified"),
-    "img2img_background_color": OptionInfo("#ffffff", "Image transparent color fill", ui_components.FormColorPicker, {}),
-    "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 image processing", gr.Slider, {"minimum": 0.1, "maximum": 1.5, "step": 0.01}),
-    "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 8, "step": 1, "visible": False}),
-}))
-
-
-options_templates.update(options_section(('saving-paths', "Output Paths"), {
-    "outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
-    "outdir_txt2img_samples": OptionInfo("outputs/text", 'Output directory for txt2img images', component_args=hide_dirs),
-    "outdir_img2img_samples": OptionInfo("outputs/image", 'Output directory for img2img images', component_args=hide_dirs),
-    "outdir_extras_samples": OptionInfo("outputs/extras", '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/grids", 'Output directory for txt2img grids', component_args=hide_dirs),
-    "outdir_img2img_grids": OptionInfo("outputs/grids", 'Output directory for img2img grids', component_args=hide_dirs),
-    "outdir_save": OptionInfo("outputs/save", "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(('ui', "User interface"), {
-    "gradio_theme": OptionInfo("black-orange", "UI theme", gr.Dropdown, lambda: {"choices": list_themes()}, refresh=refresh_themes),
-    "theme_style": OptionInfo("Auto", "Theme mode", gr.Radio, {"choices": ["Auto", "Dark", "Light"]}),
-    "return_grid": OptionInfo(True, "Show grid in results for web"),
-    "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"),
-    "disable_weights_auto_swap": OptionInfo(True, "Do not change the selected model when reading generation parameters"),
-    "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"),
-    "font": OptionInfo("", "Font for image grids that have text"),
-    "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"), # pylint: disable=anomalous-backslash-in-string
-    "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}),
-    "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
-    "ui_tab_reorder": OptionInfo("From Text, From Image, Process Image", "UI tabs order"),
-    "ui_scripts_reorder": OptionInfo("Enable Dynamic Thresholding, ControlNet", "UI scripts order"),
-    "ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
-    "ui_extra_networks_tab_reorder": OptionInfo("Checkpoints, Lora, LyCORIS, Textual Inversion, Hypernetworks", "Extra networks tab order"),
-}))
-
-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"),
-    "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
-    "notification_audio_enable": OptionInfo(False, "Play a sound when images are finished generating"),
-    "notification_audio_path": OptionInfo("html/notification.mp3","Path to notification sound", component_args=hide_dirs),
-    "show_progress_every_n_steps": OptionInfo(1, "Live preview display period", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
-    "show_progress_type": OptionInfo("TAESD", "Live preview method", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap", "TAESD"]}),
-    "live_preview_content": OptionInfo("Combined", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
-    "live_preview_refresh_period": OptionInfo(250, "Progressbar/preview update period, in milliseconds")
-}))
-
-options_templates.update(options_section(('sampler-params', "Sampler Settings"), {
-    "show_samplers": OptionInfo(["Euler a", "UniPC", "DDIM", "DPM++ 2M SDE", "DPM++ 2M SDE Karras", "DPM2 Karras", "DPM++ 2M Karras"], "Show samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers() if x.name != "PLMS"]}),
-    "fallback_sampler": OptionInfo("Euler a", "Secondary sampler", gr.Dropdown, lambda: {"choices": ["None"] + [x.name for x in list_samplers()]}),
-    "xyz_fallback_sampler": OptionInfo("None", "Force latent upscaler sampler", gr.Dropdown, lambda: {"choices": ["None"] + [x.name for x in list_samplers()]}),
-    "eta_ancestral": OptionInfo(1.0, "Noise multiplier for ancestral samplers (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "eta_ddim": OptionInfo(0.0, "Noise multiplier for DDIM (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "ddim_discretize": OptionInfo('uniform', "DDIM discretize img2img", gr.Radio, {"choices": ['uniform', 'quad']}),
-    's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    's_min_uncond': OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}),
-    's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    'eta_noise_seed_delta': OptionInfo(0, "Noise seed delta (eta)", gr.Number, {"precision": 0}),
-    'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
-    '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 (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}),
-    '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 addtional postprocessing operations", 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(('training', "Training"), {
-    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible"),
-    "pin_memory": OptionInfo(True, "Pin training dataset to memory"),
-    "save_optimizer_state": OptionInfo(False, "Saves resumable optimizer state when training embedding or hypernetwork"),
-    "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"),
-    "embeddings_templates_dir": OptionInfo(os.path.join(paths.script_path, 'train', 'templates'), "Embeddings train templates directory"),
-    "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(0, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
-    "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(('interrogate', "Interrogate"), {
-    "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
-    "interrogate_return_ranks": OptionInfo(True, "Interrogate: include ranks of model tags matches in results"),
-    "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
-    "interrogate_clip_min_length": OptionInfo(32, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
-    "interrogate_clip_max_length": OptionInfo(192, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
-    "interrogate_clip_dict_limit": OptionInfo(2048, "CLIP: maximum number of lines in text file (0 = No limit)"),
-    "interrogate_clip_skip_categories": OptionInfo(["artists", "movements", "flavors"], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
-    "interrogate_deepbooru_score_threshold": OptionInfo(0.65, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
-    "deepbooru_sort_alpha": OptionInfo(False, "Interrogate: deepbooru sort alphabetically"),
-    "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
-    "deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
-    "deepbooru_filter_tags": OptionInfo("", "filter out those tags from deepbooru output (separated by comma)"),
-}))
-
-options_templates.update(options_section(('upscaling', "Upscaling"), {
-    "upscaler_for_img2img": OptionInfo("None", "Default upscaler for image resize operations", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
-    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Real-ESRGAN available models", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
-    "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers (0 = no tiling)", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
-    "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap in pixels for ESRGAN upscalers", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
-    "SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
-    "SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),    
-    "use_old_hires_fix_width_height": OptionInfo(False, "Hires fix uses width & height to set final resolution rather than first pass"),
-    "dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers"),
-    "lyco_patch_lora": OptionInfo(False, "Use LyCoris handler for all Lora types", gr.Checkbox, { "visible": False }), # TODO: lyco-patch-lora
-    "lora_functional": OptionInfo(False, "Use Kohya method for handling multiple Loras", gr.Checkbox, { "visible": False }),
-}))
-
-# options_templates.update(options_section(('lora', "Lora"), {
-# }))
-
-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 face_restorers]}),
-    "code_former_weight": OptionInfo(0.2, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
-    "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
-}))
-
-options_templates.update(options_section(('extra_networks', "Extra Networks"), {
-    "extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, {"choices": ["cards", "thumbs"]}),
-    "extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
-    "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
-    "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"),
-    "extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"),
-    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
-}))
-
-options_templates.update(options_section(('token_merging', 'Token Merging'), {
-    "token_merging": OptionInfo(False, "Enable redundant token merging via tomesd for speed and memory improvements", gr.Checkbox),
-    "token_merging_ratio": OptionInfo(0.5, "Token merging Ratio. Higher merging ratio = faster generation, smaller VRAM usage, lower quality.", gr.Slider, {"minimum": 0, "maximum": 0.9, "step": 0.1}),
-    "token_merging_hr_only": OptionInfo(True, "Apply only to high-res fix pass. Disabling can yield a ~20-35% speedup on contemporary resolutions.", gr.Checkbox),
-    "token_merging_ratio_hr": OptionInfo(0.5, "Merging Ratio (high-res pass) - If 'Apply only to high-res' is enabled, this will always be the ratio used.", gr.Slider, {"minimum": 0, "maximum": 0.9, "step": 0.1}),
-    "token_merging_random": OptionInfo(False, "Use random perturbations - Can improve outputs for certain samplers. For others, it may cause visual artifacting.", gr.Checkbox),
-    "token_merging_merge_attention": OptionInfo(True, "Merge attention (Recommend on)", gr.Checkbox),
-    "token_merging_merge_cross_attention": OptionInfo(False, "Merge cross attention (Recommend off)", gr.Checkbox),
-    "token_merging_merge_mlp": OptionInfo(False, "Merge mlp (Strongly recommend off)", gr.Checkbox),
-    "token_merging_maximum_down_sampling": OptionInfo(1, "Maximum down sampling", gr.Radio, lambda: {"choices": [1, 2, 4, 8]}),
-    "token_merging_stride_x": OptionInfo(2, "Stride - X", gr.Slider, {"minimum": 2, "maximum": 8, "step": 2}),
-    "token_merging_stride_y": OptionInfo(2, "Stride - Y", gr.Slider, {"minimum": 2, "maximum": 8, "step": 2})
-}))
-
-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", "user", "all"]}),
-    "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
-}))
-
-options_templates.update()
-
-
-class Options:
-    data = None
-    data_labels = options_templates
-    typemap = {int: float}
-
-    def __init__(self):
-        self.data = {k: v.default for k, v in self.data_labels.items()}
-
-    def __setattr__(self, key, value):
-        if self.data is not None:
-            if key in self.data or key in self.data_labels:
-                if cmd_opts.freeze:
-                    log.warning(f'Settings are frozen: {key}')
-                    return
-                if cmd_opts.hide_ui_dir_config and key in restricted_opts:
-                    log.warning(f'Settings key is restricted: {key}')
-                    return
-                else:
-                    self.data[key] = value
-                return
-
-        return super(Options, self).__setattr__(key, value)
-
-    def __getattr__(self, 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
-        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):
-        if cmd_opts.freeze:
-            log.warning(f'Settings saving is disabled: {filename}')
-            return
-        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):
-        if not os.path.isfile(filename):
-            log.debug(f'Created default config: {filename}')
-            self.save(filename)
-            return
-        with open(filename, "r", encoding="utf8") as file:
-            self.data = json.load(file)
-        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(',')]
-        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):
-                log.error(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})")
-                bad_settings += 1
-        if bad_settings > 0:
-            log.error(f"Error: Bad settings found in {filename}")
-
-    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, self.data_labels.get(k).default) for k in self.data_labels.keys()}
-        metadata = {
-            k: {
-                "is_stored": k in self.data,
-                "tab_name": v.section[0]
-            } for k, v in self.data_labels.items()
-        }
-        return json.dumps({"values": d, "metadata": metadata})
-
-    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 k, item in settings_items:
-            if item.section not in section_ids:
-                section_ids[item.section] = len(section_ids)
-        self.data_labels = {k: v for k, v in 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
-        elif expected_type == type(value):
-            pass
-        else:
-            value = expected_type(value)
-        return value
-
-
-opts = Options()
-config_filename = cmd_opts.config
-opts.load(config_filename)
-cmd_opts = cmd_args.compatibility_args(opts, cmd_opts)
-if cmd_opts.backend == 'diffusers':
-    log.info('Overriding backend to Diffusers')
-    opts.data['sd_backend'] = 'Diffusers'
-if cmd_opts.backend == 'original':
-    log.info('Overriding backend to Diffusers')
-    opts.data['sd_backend'] = 'Original'
-backend = Backend.DIFFUSERS if opts.sd_backend == 'Diffusers' else Backend.ORIGINAL
-
-prompt_styles = modules.styles.StyleDatabase(opts.styles_dir)
-cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or (cmd_opts.server_name or False)) and not cmd_opts.insecure
-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'])
-device = devices.device
-batch_cond_uncond = opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
-parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
-mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
-mem_mon.start()
-if device.type == 'privateuseone':
-    import modules.dml # pylint: disable=ungrouped-imports
-
-
-def reload_gradio_theme(theme_name=None):
-    global gradio_theme # pylint: disable=global-statement
-    if not theme_name:
-        theme_name = opts.gradio_theme
-    default_font_params = {}
-    res = 0
-    try:
-        req = urllib.request.Request("https://fonts.googleapis.com/css2?family=IBM+Plex+Mono", method="HEAD")
-        res = urllib.request.urlopen(req, timeout=3.0).status
-    except:
-        res = 0
-    if res != 200:
-        log.info('No internet access detected, using default fonts')
-        default_font_params = {
-            'font':['Helvetica', 'ui-sans-serif', 'system-ui', 'sans-serif'],
-            'font_mono':['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace']
-        }
-    if theme_name == "black-orange":
-        gradio_theme = gr.themes.Default(**default_font_params)
-    elif theme_name.startswith("gradio/"):
-        if theme_name == "gradio/default":
-            gradio_theme = gr.themes.Default(**default_font_params)
-        if theme_name == "gradio/base":
-            gradio_theme = gr.themes.Base(**default_font_params)
-        if theme_name == "gradio/glass":
-            gradio_theme = gr.themes.Glass(**default_font_params)
-        if theme_name == "gradio/monochrome":
-            gradio_theme = gr.themes.Monochrome(**default_font_params)
-        if theme_name == "gradio/soft":
-            gradio_theme = gr.themes.Soft(**default_font_params)
-    else:
-        try:
-            gradio_theme = gr.themes.ThemeClass.from_hub(theme_name)
-        except:
-            log.error("Theme download error accessing HuggingFace")
-            gradio_theme = gr.themes.Default(**default_font_params)
-    log.info(f'Loading UI theme: name={theme_name} style={opts.theme_style}')
-
-
-class TotalTQDM:
-    def __init__(self):
-        self._tqdm = None
-
-    def reset(self):
-        self._tqdm = tqdm.tqdm(
-            desc="Total",
-            total=state.job_count * state.sampling_steps,
-            position=1,
-        )
-
-    def update(self):
-        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
-            return
-        if self._tqdm is None:
-            self.reset()
-        self._tqdm.update()
-
-    def updateTotal(self, new_total):
-        if not opts.multiple_tqdm or 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
-
-total_tqdm = TotalTQDM()
-
-
-def restart_server(restart=True):
-    if demo is None:
-        return
-    log.info('Server shutdown requested')
-    try:
-        demo.server.wants_restart = restart
-        demo.server.should_exit = True
-        demo.server.force_exit = True
-        demo.close(verbose=False)
-        demo.server.close()
-        demo.fns = []
-    except:
-        pass
-    if restart:
-        log.info('Server will restart')
-
-
-def restore_defaults(restart=True):
-    if os.path.exists(cmd_opts.config):
-        log.info('Restoring server defaults')
-        os.remove(cmd_opts.config)
-    if os.path.exists(cmd_opts.ui_config):
-        log.info('Restoring UI defaults')
-        os.remove(cmd_opts.ui_config)
-    restart_server(restart)
-
-
-def listfiles(dirname):
-    filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=str.lower) if not x.startswith(".")]
-    return [file for file in filenames if os.path.isfile(file)]
-
-
-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)
-    for root, _dirs, files in os.walk(path, followlinks=True):
-        for filename in files:
-            if allowed_extensions is not None:
-                _, ext = os.path.splitext(filename)
-                if ext not in allowed_extensions:
-                    continue
-            yield os.path.join(root, filename)
-
-
-def html_path(filename):
-    return os.path.join(paths.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 get_version():
-    version = None
-    if version is None:
-        try:
-            import subprocess
-            res = subprocess.run('git log --pretty=format:"%h %ad" -1 --date=short', stdout = subprocess.PIPE, stderr = subprocess.PIPE, shell=True, check=True)
-            ver = res.stdout.decode(encoding = 'utf8', errors='ignore') if len(res.stdout) > 0 else '  '
-            githash, updated = ver.split(' ')
-            res = subprocess.run('git remote get-url origin', stdout = subprocess.PIPE, stderr = subprocess.PIPE, shell=True, check=True)
-            origin = res.stdout.decode(encoding = 'utf8', errors='ignore') if len(res.stdout) > 0 else ''
-            res = subprocess.run('git branch --show-current', stdout = subprocess.PIPE, stderr = subprocess.PIPE, shell=True, check=True)
-            branch = res.stdout.decode(encoding = 'utf8', errors='ignore') if len(res.stdout) > 0 else ''
-            version = {
-                'app': 'sd.next',
-                'updated': updated,
-                'hash': githash,
-                'url': origin.replace('\n', '') + '/tree/' + branch.replace('\n', '')
-            }
-        except:
-            version = { 'app': 'sd.next' }
-    return version
-
-
-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 # pylint: disable=W0621
-        # return modules.sd_models.model_data.sd_model
-        return modules.sd_models.model_data.get_sd_model()
-
-    @sd_model.setter
-    def sd_model(self, value):
-        import modules.sd_models # pylint: disable=W0621
-        modules.sd_models.model_data.set_sd_model(value)
-
-# sd_model: LatentDiffusion = None  # this var is here just for IDE's type checking; it cannot be accessed because the class field above will be accessed instead
-sd_model = None
-sys.modules[__name__].__class__ = Shared
+import os
+import sys
+import time
+import json
+import datetime
+import urllib.request
+from enum import Enum
+import gradio as gr
+import tqdm
+import requests
+from modules import errors, ui_components, shared_items, cmd_args
+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 # pylint: disable=W0611
+import modules.interrogate
+import modules.memmon
+import modules.styles
+import modules.devices as devices
+import modules.paths_internal as paths
+from installer import log as central_logger # pylint: disable=E0611
+
+
+errors.install(gr)
+demo: gr.Blocks = None
+log = central_logger
+progress_print_out = sys.stdout
+parser = cmd_args.parser
+url = 'https://github.com/vladmandic/automatic'
+cmd_opts, _ = parser.parse_known_args()
+hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
+xformers_available = False
+clip_model = None
+interrogator = modules.interrogate.InterrogateModels("interrogate")
+sd_upscalers = []
+face_restorers = []
+tab_names = []
+options_templates = {}
+hypernetworks = {}
+loaded_hypernetworks = []
+gradio_theme = gr.themes.Base()
+settings_components = None
+latent_upscale_default_mode = "Latent"
+latent_upscale_modes = {
+    "Latent": {"mode": "bilinear", "antialias": False},
+    "Latent (antialiased)": {"mode": "bilinear", "antialias": True},
+    "Latent (bicubic)": {"mode": "bicubic", "antialias": False},
+    "Latent (bicubic antialiased)": {"mode": "bicubic", "antialias": True},
+    "Latent (nearest)": {"mode": "nearest", "antialias": False},
+    "Latent (nearest-exact)": {"mode": "nearest-exact", "antialias": False},
+}
+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"
+}
+ui_reorder_categories = [
+    "inpaint",
+    "sampler",
+    "checkboxes",
+    "hires_fix",
+    "dimensions",
+    "cfg",
+    "seed",
+    "batch",
+    "override_settings",
+    "scripts",
+]
+
+
+class Backend(Enum):
+    ORIGINAL = 1
+    DIFFUSERS = 2
+
+
+def reload_hypernetworks():
+    from modules.hypernetworks import hypernetwork
+    global hypernetworks # pylint: disable=W0603
+    hypernetworks = hypernetwork.list_hypernetworks(opts.hypernetwork_dir)
+
+
+class State:
+    skipped = False
+    interrupted = False
+    paused = 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
+    need_restart = False
+    server_start = None
+
+    def skip(self):
+        log.debug('Requested skip')
+        self.skipped = True
+
+    def interrupt(self):
+        log.debug('Requested interrupt')
+        self.interrupted = True
+
+    def pause(self):
+        self.paused = not self.paused
+        log.debug(f'Requested {"pause" if self.paused else "continue"}')
+
+    def nextjob(self):
+        if opts.live_previews_enable and 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):
+        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.paused = False
+        self.textinfo = None
+        self.time_start = time.time()
+        devices.torch_gc()
+
+    def end(self):
+        self.job = ""
+        self.job_count = 0
+        self.paused = False
+        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 parallel_processing_allowed:
+            return
+        if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.live_previews_enable and 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 # pylint: disable=W0621
+        if 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
+
+    def assign_current_image(self, image):
+        self.current_image = image
+        self.id_live_preview += 1
+
+state = State()
+state.server_start = time.time()
+
+
+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.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, uri):
+        self.comment_before += f"[<a href='{uri}' 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 needs_restart(self):
+        self.comment_after += " <span class='info'>(requires restart)</span>"
+        return self
+
+
+def options_section(section_identifier, options_dict):
+    for v in options_dict.values():
+        v.section = section_identifier
+    return options_dict
+
+
+def list_checkpoint_tiles():
+    import modules.sd_models # pylint: disable=W0621
+    return modules.sd_models.checkpoint_tiles()
+
+
+default_checkpoint = list_checkpoint_tiles()[0] if len(list_checkpoint_tiles()) > 0 else "model.ckpt"
+
+
+def refresh_checkpoints():
+    import modules.sd_models # pylint: disable=W0621
+    return modules.sd_models.list_models()
+
+
+def list_samplers():
+    import modules.sd_samplers # pylint: disable=W0621
+    modules.sd_samplers.set_samplers()
+    return modules.sd_samplers.all_samplers
+
+def list_themes():
+    fn = os.path.join('html', 'themes.json')
+    if not os.path.exists(fn):
+        refresh_themes()
+    if os.path.exists(fn):
+        with open(fn, mode='r', encoding='utf=8') as f:
+            res = json.loads(f.read())
+    else:
+        res = []
+    builtin = ["black-orange", "gradio/default", "gradio/base", "gradio/glass", "gradio/monochrome", "gradio/soft"]
+    themes = sorted(set(builtin + [x['id'] for x in res if x['status'] == 'RUNNING' and 'test' not in x['id'].lower()]), key=str.casefold)
+    return themes
+
+
+def refresh_themes():
+    try:
+        req = requests.get('https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json', timeout=5)
+        if req.status_code == 200:
+            res = req.json()
+            fn = os.path.join('html', 'themes.json')
+            with open(fn, mode='w', encoding='utf=8') as f:
+                f.write(json.dumps(res))
+        else:
+            log.error('Error refreshing UI themes')
+    except:
+        log.error('Exception refreshing UI themes')
+
+
+if devices.backend == "cpu":
+    cross_attention_optimization_default = "Doggettx's"
+elif devices.backend == "mps":
+    cross_attention_optimization_default = "Doggettx's"
+elif devices.backend == "ipex":
+    cross_attention_optimization_default = "InvokeAI's"
+elif devices.backend == "directml":
+    cross_attention_optimization_default = "Sub-quadratic"
+elif devices.backend == "rocm":
+    cross_attention_optimization_default = "Sub-quadratic"
+else: # cuda
+    cross_attention_optimization_default ="Scaled-Dot-Product"
+
+options_templates.update(options_section(('sd', "Stable Diffusion"), {
+    "sd_model_checkpoint": OptionInfo(default_checkpoint, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
+    "sd_checkpoint_cache": OptionInfo(0, "Number of cached model checkpoints", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
+    "sd_vae_checkpoint_cache": OptionInfo(0, "Number of cached VAE checkpoints", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
+    "sd_vae": OptionInfo("Automatic", "Select VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
+    "sd_vae_sliced_encode": OptionInfo(False, "Enable splitting of hires batch processing"),
+    "stream_load": OptionInfo(False, "When loading models attempt stream loading optimized for slow or network storage"),
+    "model_reuse_dict": OptionInfo(False, "When loading models attempt to reuse previous model dictionary"),
+    "cross_attention_optimization": OptionInfo(cross_attention_optimization_default, "Cross-attention optimization method", gr.Radio, lambda: {"choices": shared_items.list_crossattention() }),
+    "cross_attention_options": OptionInfo([], "Cross-attention advanced options", gr.CheckboxGroup, lambda: {"choices": ['xFormers enable flash Attention', 'SDP disable memory attention']}),
+    "sub_quad_q_chunk_size": OptionInfo(512, "Sub-quadratic cross-attention query chunk size", gr.Slider, {"minimum": 16, "maximum": 8192, "step": 8}),
+    "sub_quad_kv_chunk_size": OptionInfo(512, "Sub-quadratic cross-attention kv chunk size", gr.Slider, {"minimum": 0, "maximum": 8192, "step": 8}),
+    "sub_quad_chunk_threshold": OptionInfo(80, "Sub-quadratic cross-attention chunking threshold", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}),
+    "prompt_attention": OptionInfo("Full parser", "Prompt attention parser", gr.Radio, lambda: {"choices": ["Full parser", "Compel parser", "A1111 parser", "Fixed attention"] }),
+    "prompt_mean_norm": OptionInfo(True, "Prompt attention mean normalization"),
+    "always_batch_cond_uncond": OptionInfo(False, "Disable conditional batching enabled on low memory systems"),
+    "enable_quantization": OptionInfo(True, "Enable samplers quantization for sharper and cleaner results"),
+    "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
+    "sd_backend": OptionInfo("Original", "Stable Diffusion backend (experimental)", gr.Radio, lambda: {"choices": ["Original", "Diffusers"] }),
+}))
+
+options_templates.update(options_section(('cuda', "Compute Settings"), {
+    "memmon_poll_rate": OptionInfo(2, "VRAM usage polls per second during generation", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
+    "precision": OptionInfo("Autocast", "Precision type", gr.Radio, lambda: {"choices": ["Autocast", "Full"]}),
+    "cuda_dtype": OptionInfo("FP32" if sys.platform == "darwin" else "FP16", "Device precision type", gr.Radio, lambda: {"choices": ["FP32", "FP16", "BF16"]}),
+    "no_half": OptionInfo(False, "Use full precision for model (--no-half)", None, None, None),
+    "no_half_vae": OptionInfo(False, "Use full precision for VAE (--no-half-vae)"),
+    "upcast_sampling": OptionInfo(True if sys.platform == "darwin" else False, "Enable upcast sampling"),
+    "upcast_attn": OptionInfo(False, "Enable upcast cross attention layer"),
+    "disable_nan_check": OptionInfo(True, "Disable NaN check in produced images/latent spaces"),
+    "rollback_vae": OptionInfo(False, "Attempt to roll back VAE when produced NaN values, requires NaN check (experimental)"),
+    "opt_channelslast": OptionInfo(False, "Use channels last as torch memory format "),
+    "cudnn_benchmark": OptionInfo(False, "Enable full-depth cuDNN benchmark feature"),
+    "cuda_allow_tf32": OptionInfo(True, "Allow TF32 math ops"),
+    "cuda_allow_tf16_reduced": OptionInfo(True, "Allow TF16 reduced precision math ops"),
+    "cuda_compile": OptionInfo(False, "Enable model compile (experimental)"),
+    "cuda_compile_mode": OptionInfo("none", "Model compile mode (experimental)", gr.Radio, lambda: {"choices": ['none', 'inductor', 'cudagraphs', 'aot_ts_nvfuser', 'hidet', 'ipex']}),
+    "cuda_compile_verbose": OptionInfo(False, "Model compile verbose mode"),
+    "cuda_compile_errors": OptionInfo(True, "Model compile suppress errors"),
+    "disable_gc": OptionInfo(False, "Disable Torch memory garbage collection (experimental)"),
+}))
+
+options_templates.update(options_section(('system-paths', "System Paths"), {
+    "temp_dir":  OptionInfo("", "Directory for temporary images; leave empty for default"),
+    "clean_temp_dir_at_start": OptionInfo(True, "Cleanup non-default temporary directory when starting webui"),
+    "ckpt_dir": OptionInfo(os.path.join(paths.models_path, 'Stable-diffusion'), "Path to directory with stable diffusion checkpoints"),
+    "diffusers_dir": OptionInfo(os.path.join(paths.models_path, 'Diffusers'), "Path to directory with stable diffusion diffusers"),
+    "vae_dir": OptionInfo(os.path.join(paths.models_path, 'VAE'), "Path to directory with VAE files"),
+    "embeddings_dir": OptionInfo(os.path.join(paths.models_path, 'embeddings'), "Embeddings directory for textual inversion"),
+    "hypernetwork_dir": OptionInfo(os.path.join(paths.models_path, 'hypernetworks'), "Hypernetwork directory"),
+    "codeformer_models_path": OptionInfo(os.path.join(paths.models_path, 'Codeformer'), "Path to directory with codeformer model file(s)"),
+    "gfpgan_models_path": OptionInfo(os.path.join(paths.models_path, 'GFPGAN'), "Path to directory with GFPGAN model file(s)"),
+    "esrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'ESRGAN'), "Path to directory with ESRGAN model file(s)"),
+    "bsrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'BSRGAN'), "Path to directory with BSRGAN model file(s)"),
+    "realesrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'RealESRGAN'), "Path to directory with RealESRGAN model file(s)"),
+    "scunet_models_path": OptionInfo(os.path.join(paths.models_path, 'ScuNET'), "Path to directory with ScuNET model file(s)"),
+    "swinir_models_path": OptionInfo(os.path.join(paths.models_path, 'SwinIR'), "Path to directory with SwinIR model file(s)"),
+    "ldsr_models_path": OptionInfo(os.path.join(paths.models_path, 'LDSR'), "Path to directory with LDSR model file(s)"),
+    "clip_models_path": OptionInfo(os.path.join(paths.models_path, 'CLIP'), "Path to directory with CLIP model file(s)"),
+    "lora_dir": OptionInfo(os.path.join(paths.models_path, 'Lora'), "Path to directory with Lora network(s)"),
+    "lyco_dir": OptionInfo(os.path.join(paths.models_path, 'LyCORIS'), "Path to directory with LyCORIS network(s)"),
+    "styles_dir": OptionInfo(os.path.join(paths.data_path, 'styles.csv'), "Path to user-defined styles file"),
+}))
+
+options_templates.update(options_section(('saving-images', "Image Options"), {
+    "samples_save": OptionInfo(True, "Always save all generated images"),
+    "samples_format": OptionInfo('jpg', 'File format for generated images', gr.Dropdown, lambda: {"choices": ["jpg", "png", "webp", "tiff", "jp2"]}),
+    "samples_filename_pattern": OptionInfo("[seed]-[prompt_spaces]", "Images filename pattern", component_args=hide_dirs),
+    "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('jpg', 'File format for grids', gr.Dropdown, lambda: {"choices": ["jpg", "png", "webp", "tiff", "jp2"]}),
+    "grid_extended_filename": OptionInfo(True, "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(True, "Prevent empty spots in grid (when set to autodetect)"),
+    "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}),
+    "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters"),
+    "save_log_fn": OptionInfo("", "Create a JSON log file with image information for each saved image", component_args=hide_dirs),
+    "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, "Save a copy of the inpainting greyscale mask"),
+    "save_mask_composite": OptionInfo(False, "Save a copy of inpainting masked composite"),
+    "save_init_img": OptionInfo(False, "Save a copy of processing init images"),
+    "jpeg_quality": OptionInfo(85, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
+    "webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
+    "img_max_size_mp": OptionInfo(250, "Maximum allowed image size in megapixels", gr.Number),
+    "use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
+    "use_upscaler_name_as_suffix": OptionInfo(True, "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_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
+    "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"),
+    "use_save_to_dirs_for_ui": OptionInfo(False, "Save images to a subdirectory when using Save button"),
+    "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
+    "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(('image-processing', "Image Processing"), {
+    "img2img_color_correction": OptionInfo(False, "Apply color correction to match original colors"),
+    "img2img_fix_steps": OptionInfo(False, "For image processing do exact number of steps as specified"),
+    "img2img_background_color": OptionInfo("#ffffff", "Image transparent color fill", ui_components.FormColorPicker, {}),
+    "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 image processing", gr.Slider, {"minimum": 0.1, "maximum": 1.5, "step": 0.01}),
+    "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 8, "step": 1, "visible": False}),
+}))
+
+
+options_templates.update(options_section(('saving-paths', "Output Paths"), {
+    "outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
+    "outdir_txt2img_samples": OptionInfo("outputs/text", 'Output directory for txt2img images', component_args=hide_dirs),
+    "outdir_img2img_samples": OptionInfo("outputs/image", 'Output directory for img2img images', component_args=hide_dirs),
+    "outdir_extras_samples": OptionInfo("outputs/extras", '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/grids", 'Output directory for txt2img grids', component_args=hide_dirs),
+    "outdir_img2img_grids": OptionInfo("outputs/grids", 'Output directory for img2img grids', component_args=hide_dirs),
+    "outdir_save": OptionInfo("outputs/save", "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(('ui', "User interface"), {
+    "gradio_theme": OptionInfo("black-orange", "UI theme", gr.Dropdown, lambda: {"choices": list_themes()}, refresh=refresh_themes),
+    "theme_style": OptionInfo("Auto", "Theme mode", gr.Radio, {"choices": ["Auto", "Dark", "Light"]}),
+    "return_grid": OptionInfo(True, "Show grid in results for web"),
+    "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"),
+    "disable_weights_auto_swap": OptionInfo(True, "Do not change the selected model when reading generation parameters"),
+    "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"),
+    "font": OptionInfo("", "Font for image grids that have text"),
+    "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"), # pylint: disable=anomalous-backslash-in-string
+    "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}),
+    "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
+    "ui_tab_reorder": OptionInfo("From Text, From Image, Process Image", "UI tabs order"),
+    "ui_scripts_reorder": OptionInfo("Enable Dynamic Thresholding, ControlNet", "UI scripts order"),
+    "ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
+    "ui_extra_networks_tab_reorder": OptionInfo("Checkpoints, Lora, LyCORIS, Textual Inversion, Hypernetworks", "Extra networks tab order"),
+}))
+
+options_templates.update(options_section(('live-preview', "Live previews"), {
+    "show_progressbar": OptionInfo(True, "Show progressbar"),
+    "live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
+    "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
+    "notification_audio_enable": OptionInfo(False, "Play a sound when images are finished generating"),
+    "notification_audio_path": OptionInfo("html/notification.mp3","Path to notification sound", component_args=hide_dirs),
+    "show_progress_every_n_steps": OptionInfo(1, "Live preview display period", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
+    "show_progress_type": OptionInfo("TAESD", "Live preview method", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap", "TAESD"]}),
+    "live_preview_content": OptionInfo("Combined", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
+    "live_preview_refresh_period": OptionInfo(250, "Progressbar/preview update period, in milliseconds")
+}))
+
+options_templates.update(options_section(('sampler-params', "Sampler Settings"), {
+    "show_samplers": OptionInfo(["Euler a", "UniPC", "DDIM", "DPM++ 2M SDE", "DPM++ 2M SDE Karras", "DPM2 Karras", "DPM++ 2M Karras"], "Show samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers() if x.name != "PLMS"]}),
+    "fallback_sampler": OptionInfo("Euler a", "Secondary sampler", gr.Dropdown, lambda: {"choices": ["None"] + [x.name for x in list_samplers()]}),
+    "xyz_fallback_sampler": OptionInfo("None", "Force latent upscaler sampler", gr.Dropdown, lambda: {"choices": ["None"] + [x.name for x in list_samplers()]}),
+    "eta_ancestral": OptionInfo(1.0, "Noise multiplier for ancestral samplers (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "eta_ddim": OptionInfo(0.0, "Noise multiplier for DDIM (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "ddim_discretize": OptionInfo('uniform', "DDIM discretize img2img", gr.Radio, {"choices": ['uniform', 'quad']}),
+    's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    's_min_uncond': OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}),
+    's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    'eta_noise_seed_delta': OptionInfo(0, "Noise seed delta (eta)", gr.Number, {"precision": 0}),
+    'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
+    '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 (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}),
+    '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 addtional postprocessing operations", 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(('training', "Training"), {
+    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible"),
+    "pin_memory": OptionInfo(True, "Pin training dataset to memory"),
+    "save_optimizer_state": OptionInfo(False, "Saves resumable optimizer state when training embedding or hypernetwork"),
+    "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"),
+    "embeddings_templates_dir": OptionInfo(os.path.join(paths.script_path, 'train', 'templates'), "Embeddings train templates directory"),
+    "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(0, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
+    "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(('interrogate', "Interrogate"), {
+    "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"),
+    "interrogate_return_ranks": OptionInfo(True, "Interrogate: include ranks of model tags matches in results"),
+    "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
+    "interrogate_clip_min_length": OptionInfo(32, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
+    "interrogate_clip_max_length": OptionInfo(192, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
+    "interrogate_clip_dict_limit": OptionInfo(2048, "CLIP: maximum number of lines in text file (0 = No limit)"),
+    "interrogate_clip_skip_categories": OptionInfo(["artists", "movements", "flavors"], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
+    "interrogate_deepbooru_score_threshold": OptionInfo(0.65, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
+    "deepbooru_sort_alpha": OptionInfo(False, "Interrogate: deepbooru sort alphabetically"),
+    "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"),
+    "deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"),
+    "deepbooru_filter_tags": OptionInfo("", "filter out those tags from deepbooru output (separated by comma)"),
+}))
+
+options_templates.update(options_section(('upscaling', "Upscaling"), {
+    "upscaler_for_img2img": OptionInfo("None", "Default upscaler for image resize operations", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
+    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Real-ESRGAN available models", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
+    "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers (0 = no tiling)", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
+    "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap in pixels for ESRGAN upscalers", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
+    "SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
+    "SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),    
+    "use_old_hires_fix_width_height": OptionInfo(False, "Hires fix uses width & height to set final resolution rather than first pass"),
+    "dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers"),
+    "lyco_patch_lora": OptionInfo(False, "Use LyCoris handler for all Lora types", gr.Checkbox, { "visible": False }), # TODO: lyco-patch-lora
+    "lora_functional": OptionInfo(False, "Use Kohya method for handling multiple Loras", gr.Checkbox, { "visible": False }),
+}))
+
+# options_templates.update(options_section(('lora', "Lora"), {
+# }))
+
+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 face_restorers]}),
+    "code_former_weight": OptionInfo(0.2, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
+    "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
+}))
+
+options_templates.update(options_section(('extra_networks', "Extra Networks"), {
+    "extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, {"choices": ["cards", "thumbs"]}),
+    "extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
+    "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
+    "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"),
+    "extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"),
+    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
+}))
+
+options_templates.update(options_section(('token_merging', 'Token Merging'), {
+    "token_merging": OptionInfo(False, "Enable redundant token merging via tomesd for speed and memory improvements", gr.Checkbox),
+    "token_merging_ratio": OptionInfo(0.5, "Token merging Ratio. Higher merging ratio = faster generation, smaller VRAM usage, lower quality.", gr.Slider, {"minimum": 0, "maximum": 0.9, "step": 0.1}),
+    "token_merging_hr_only": OptionInfo(True, "Apply only to high-res fix pass. Disabling can yield a ~20-35% speedup on contemporary resolutions.", gr.Checkbox),
+    "token_merging_ratio_hr": OptionInfo(0.5, "Merging Ratio (high-res pass) - If 'Apply only to high-res' is enabled, this will always be the ratio used.", gr.Slider, {"minimum": 0, "maximum": 0.9, "step": 0.1}),
+    "token_merging_random": OptionInfo(False, "Use random perturbations - Can improve outputs for certain samplers. For others, it may cause visual artifacting.", gr.Checkbox),
+    "token_merging_merge_attention": OptionInfo(True, "Merge attention (Recommend on)", gr.Checkbox),
+    "token_merging_merge_cross_attention": OptionInfo(False, "Merge cross attention (Recommend off)", gr.Checkbox),
+    "token_merging_merge_mlp": OptionInfo(False, "Merge mlp (Strongly recommend off)", gr.Checkbox),
+    "token_merging_maximum_down_sampling": OptionInfo(1, "Maximum down sampling", gr.Radio, lambda: {"choices": [1, 2, 4, 8]}),
+    "token_merging_stride_x": OptionInfo(2, "Stride - X", gr.Slider, {"minimum": 2, "maximum": 8, "step": 2}),
+    "token_merging_stride_y": OptionInfo(2, "Stride - Y", gr.Slider, {"minimum": 2, "maximum": 8, "step": 2})
+}))
+
+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", "user", "all"]}),
+    "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
+}))
+
+options_templates.update()
+
+
+class Options:
+    data = None
+    data_labels = options_templates
+    typemap = {int: float}
+
+    def __init__(self):
+        self.data = {k: v.default for k, v in self.data_labels.items()}
+
+    def __setattr__(self, key, value):
+        if self.data is not None:
+            if key in self.data or key in self.data_labels:
+                if cmd_opts.freeze:
+                    log.warning(f'Settings are frozen: {key}')
+                    return
+                if cmd_opts.hide_ui_dir_config and key in restricted_opts:
+                    log.warning(f'Settings key is restricted: {key}')
+                    return
+                else:
+                    self.data[key] = value
+                return
+
+        return super(Options, self).__setattr__(key, value)
+
+    def __getattr__(self, 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
+        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):
+        if cmd_opts.freeze:
+            log.warning(f'Settings saving is disabled: {filename}')
+            return
+        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):
+        if not os.path.isfile(filename):
+            log.debug(f'Created default config: {filename}')
+            self.save(filename)
+            return
+        with open(filename, "r", encoding="utf8") as file:
+            self.data = json.load(file)
+        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(',')]
+        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):
+                log.error(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})")
+                bad_settings += 1
+        if bad_settings > 0:
+            log.error(f"Error: Bad settings found in {filename}")
+
+    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, self.data_labels.get(k).default) for k in self.data_labels.keys()}
+        metadata = {
+            k: {
+                "is_stored": k in self.data,
+                "tab_name": v.section[0]
+            } for k, v in self.data_labels.items()
+        }
+        return json.dumps({"values": d, "metadata": metadata})
+
+    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 k, item in settings_items:
+            if item.section not in section_ids:
+                section_ids[item.section] = len(section_ids)
+        self.data_labels = {k: v for k, v in 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
+        elif expected_type == type(value):
+            pass
+        else:
+            value = expected_type(value)
+        return value
+
+
+opts = Options()
+config_filename = cmd_opts.config
+opts.load(config_filename)
+cmd_opts = cmd_args.compatibility_args(opts, cmd_opts)
+if cmd_opts.backend == 'diffusers':
+    log.info('Overriding backend to Diffusers')
+    opts.data['sd_backend'] = 'Diffusers'
+if cmd_opts.backend == 'original':
+    log.info('Overriding backend to Diffusers')
+    opts.data['sd_backend'] = 'Original'
+backend = Backend.DIFFUSERS if opts.sd_backend == 'Diffusers' else Backend.ORIGINAL
+
+prompt_styles = modules.styles.StyleDatabase(opts.styles_dir)
+cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or (cmd_opts.server_name or False)) and not cmd_opts.insecure
+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'])
+device = devices.device
+batch_cond_uncond = opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
+parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
+mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
+mem_mon.start()
+if device.type == 'privateuseone':
+    import modules.dml # pylint: disable=ungrouped-imports
+
+
+def reload_gradio_theme(theme_name=None):
+    global gradio_theme # pylint: disable=global-statement
+    if not theme_name:
+        theme_name = opts.gradio_theme
+    default_font_params = {}
+    res = 0
+    try:
+        req = urllib.request.Request("https://fonts.googleapis.com/css2?family=IBM+Plex+Mono", method="HEAD")
+        res = urllib.request.urlopen(req, timeout=3.0).status
+    except:
+        res = 0
+    if res != 200:
+        log.info('No internet access detected, using default fonts')
+        default_font_params = {
+            'font':['Helvetica', 'ui-sans-serif', 'system-ui', 'sans-serif'],
+            'font_mono':['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace']
+        }
+    if theme_name == "black-orange":
+        gradio_theme = gr.themes.Default(**default_font_params)
+    elif theme_name.startswith("gradio/"):
+        if theme_name == "gradio/default":
+            gradio_theme = gr.themes.Default(**default_font_params)
+        if theme_name == "gradio/base":
+            gradio_theme = gr.themes.Base(**default_font_params)
+        if theme_name == "gradio/glass":
+            gradio_theme = gr.themes.Glass(**default_font_params)
+        if theme_name == "gradio/monochrome":
+            gradio_theme = gr.themes.Monochrome(**default_font_params)
+        if theme_name == "gradio/soft":
+            gradio_theme = gr.themes.Soft(**default_font_params)
+    else:
+        try:
+            gradio_theme = gr.themes.ThemeClass.from_hub(theme_name)
+        except:
+            log.error("Theme download error accessing HuggingFace")
+            gradio_theme = gr.themes.Default(**default_font_params)
+    log.info(f'Loading UI theme: name={theme_name} style={opts.theme_style}')
+
+
+class TotalTQDM:
+    def __init__(self):
+        self._tqdm = None
+
+    def reset(self):
+        self._tqdm = tqdm.tqdm(
+            desc="Total",
+            total=state.job_count * state.sampling_steps,
+            position=1,
+        )
+
+    def update(self):
+        if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
+            return
+        if self._tqdm is None:
+            self.reset()
+        self._tqdm.update()
+
+    def updateTotal(self, new_total):
+        if not opts.multiple_tqdm or 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
+
+total_tqdm = TotalTQDM()
+
+
+def restart_server(restart=True):
+    if demo is None:
+        return
+    log.info('Server shutdown requested')
+    try:
+        demo.server.wants_restart = restart
+        demo.server.should_exit = True
+        demo.server.force_exit = True
+        demo.close(verbose=False)
+        demo.server.close()
+        demo.fns = []
+    except:
+        pass
+    if restart:
+        log.info('Server will restart')
+
+
+def restore_defaults(restart=True):
+    if os.path.exists(cmd_opts.config):
+        log.info('Restoring server defaults')
+        os.remove(cmd_opts.config)
+    if os.path.exists(cmd_opts.ui_config):
+        log.info('Restoring UI defaults')
+        os.remove(cmd_opts.ui_config)
+    restart_server(restart)
+
+
+def listfiles(dirname):
+    filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=str.lower) if not x.startswith(".")]
+    return [file for file in filenames if os.path.isfile(file)]
+
+
+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)
+    for root, _dirs, files in os.walk(path, followlinks=True):
+        for filename in files:
+            if allowed_extensions is not None:
+                _, ext = os.path.splitext(filename)
+                if ext not in allowed_extensions:
+                    continue
+            yield os.path.join(root, filename)
+
+
+def html_path(filename):
+    return os.path.join(paths.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 get_version():
+    version = None
+    if version is None:
+        try:
+            import subprocess
+            res = subprocess.run('git log --pretty=format:"%h %ad" -1 --date=short', stdout = subprocess.PIPE, stderr = subprocess.PIPE, shell=True, check=True)
+            ver = res.stdout.decode(encoding = 'utf8', errors='ignore') if len(res.stdout) > 0 else '  '
+            githash, updated = ver.split(' ')
+            res = subprocess.run('git remote get-url origin', stdout = subprocess.PIPE, stderr = subprocess.PIPE, shell=True, check=True)
+            origin = res.stdout.decode(encoding = 'utf8', errors='ignore') if len(res.stdout) > 0 else ''
+            res = subprocess.run('git branch --show-current', stdout = subprocess.PIPE, stderr = subprocess.PIPE, shell=True, check=True)
+            branch = res.stdout.decode(encoding = 'utf8', errors='ignore') if len(res.stdout) > 0 else ''
+            version = {
+                'app': 'sd.next',
+                'updated': updated,
+                'hash': githash,
+                'url': origin.replace('\n', '') + '/tree/' + branch.replace('\n', '')
+            }
+        except:
+            version = { 'app': 'sd.next' }
+    return version
+
+
+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 # pylint: disable=W0621
+        # return modules.sd_models.model_data.sd_model
+        return modules.sd_models.model_data.get_sd_model()
+
+    @sd_model.setter
+    def sd_model(self, value):
+        import modules.sd_models # pylint: disable=W0621
+        modules.sd_models.model_data.set_sd_model(value)
+
+# sd_model: LatentDiffusion = None  # this var is here just for IDE's type checking; it cannot be accessed because the class field above will be accessed instead
+sd_model = None
+sys.modules[__name__].__class__ = Shared