stable-diffusion-webui/modules/shared.py

import datetime
import json
import os
import re
import sys
import threading
import time
import logging

import gradio as gr
import torch
import tqdm

import launch
import modules.interrogate
import modules.memmon
import modules.styles
import modules.devices as devices
from modules import localization, script_loading, 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  # noqa: F401
from ldm.models.diffusion.ddpm import LatentDiffusion
from typing import Optional

log = logging.getLogger(__name__)

demo = None

parser = cmd_args.parser

script_loading.preload_extensions(extensions_dir, parser, extension_list=launch.list_extensions(launch.args.ui_settings_file))
script_loading.preload_extensions(extensions_builtin_dir, parser)

if os.environ.get('IGNORE_CMD_ARGS_ERRORS', None) is None:
    cmd_opts = parser.parse_args()
else:
    cmd_opts, _ = parser.parse_known_args()


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"
}

# https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json
gradio_hf_hub_themes = [
    "gradio/base",
    "gradio/glass",
    "gradio/monochrome",
    "gradio/seafoam",
    "gradio/soft",
    "gradio/dracula_test",
    "abidlabs/dracula_test",
    "abidlabs/Lime",
    "abidlabs/pakistan",
    "Ama434/neutral-barlow",
    "dawood/microsoft_windows",
    "finlaymacklon/smooth_slate",
    "Franklisi/darkmode",
    "freddyaboulton/dracula_revamped",
    "freddyaboulton/test-blue",
    "gstaff/xkcd",
    "Insuz/Mocha",
    "Insuz/SimpleIndigo",
    "JohnSmith9982/small_and_pretty",
    "nota-ai/theme",
    "nuttea/Softblue",
    "ParityError/Anime",
    "reilnuud/polite",
    "remilia/Ghostly",
    "rottenlittlecreature/Moon_Goblin",
    "step-3-profit/Midnight-Deep",
    "Taithrah/Minimal",
    "ysharma/huggingface",
    "ysharma/steampunk"
]


cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access

devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \
    (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])

devices.dtype = torch.float32 if cmd_opts.no_half else torch.float16
devices.dtype_vae = torch.float32 if cmd_opts.no_half or cmd_opts.no_half_vae else torch.float16

device = devices.device
weight_load_location = None if cmd_opts.lowram else "cpu"

batch_cond_uncond = cmd_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
xformers_available = False
config_filename = cmd_opts.ui_settings_file

os.makedirs(cmd_opts.hypernetwork_dir, exist_ok=True)
hypernetworks = {}
loaded_hypernetworks = []


def reload_hypernetworks():
    from modules.hypernetworks import hypernetwork
    global hypernetworks

    hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir)


class State:
    skipped = False
    interrupted = False
    job = ""
    job_no = 0
    job_count = 0
    processing_has_refined_job_count = False
    job_timestamp = '0'
    sampling_step = 0
    sampling_steps = 0
    current_latent = None
    current_image = None
    current_image_sampling_step = 0
    id_live_preview = 0
    textinfo = None
    time_start = None
    server_start = None
    _server_command_signal = threading.Event()
    _server_command: Optional[str] = None

    @property
    def need_restart(self) -> bool:
        # Compatibility getter for need_restart.
        return self.server_command == "restart"

    @need_restart.setter
    def need_restart(self, value: bool) -> None:
        # Compatibility setter for need_restart.
        if value:
            self.server_command = "restart"

    @property
    def server_command(self):
        return self._server_command

    @server_command.setter
    def server_command(self, value: Optional[str]) -> None:
        """
        Set the server command to `value` and signal that it's been set.
        """
        self._server_command = value
        self._server_command_signal.set()

    def wait_for_server_command(self, timeout: Optional[float] = None) -> Optional[str]:
        """
        Wait for server command to get set; return and clear the value and signal.
        """
        if self._server_command_signal.wait(timeout):
            self._server_command_signal.clear()
            req = self._server_command
            self._server_command = None
            return req
        return None

    def request_restart(self) -> None:
        self.interrupt()
        self.server_command = "restart"
        log.info("Received restart request")

    def skip(self):
        self.skipped = True
        log.info("Received skip request")

    def interrupt(self):
        self.interrupted = True
        log.info("Received interrupt request")

    def nextjob(self):
        if 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, job: str = "(unknown)"):
        self.sampling_step = 0
        self.job_count = -1
        self.processing_has_refined_job_count = False
        self.job_no = 0
        self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
        self.current_latent = None
        self.current_image = None
        self.current_image_sampling_step = 0
        self.id_live_preview = 0
        self.skipped = False
        self.interrupted = False
        self.textinfo = None
        self.time_start = time.time()
        self.job = job
        devices.torch_gc()
        log.info("Starting job %s", job)

    def end(self):
        duration = time.time() - self.time_start
        log.info("Ending job %s (%.2f seconds)", self.job, duration)
        self.job = ""
        self.job_count = 0

        devices.torch_gc()

    def set_current_image(self):
        """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this"""
        if not 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

        try:
            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

        except Exception:
            # when switching models during genration, VAE would be on CPU, so creating an image will fail.
            # we silently ignore this error
            errors.record_exception()

    def assign_current_image(self, image):
        self.current_image = image
        self.id_live_preview += 1


state = State()
state.server_start = time.time()

styles_filename = cmd_opts.styles_file
prompt_styles = modules.styles.StyleDatabase(styles_filename)

interrogator = modules.interrogate.InterrogateModels("interrogate")

face_restorers = []


class OptionInfo:
    def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, comment_before='', comment_after=''):
        self.default = default
        self.label = label
        self.component = component
        self.component_args = component_args
        self.onchange = onchange
        self.section = section
        self.refresh = refresh
        self.do_not_save = False

        self.comment_before = comment_before
        """HTML text that will be added after label in UI"""

        self.comment_after = comment_after
        """HTML text that will be added before label in UI"""

    def link(self, label, url):
        self.comment_before += f"[<a href='{url}' target='_blank'>{label}</a>]"
        return self

    def js(self, label, js_func):
        self.comment_before += f"[<a onclick='{js_func}(); return false'>{label}</a>]"
        return self

    def info(self, info):
        self.comment_after += f"<span class='info'>({info})</span>"
        return self

    def html(self, html):
        self.comment_after += html
        return self

    def needs_restart(self):
        self.comment_after += " <span class='info'>(requires restart)</span>"
        return self

    def needs_reload_ui(self):
        self.comment_after += " <span class='info'>(requires Reload UI)</span>"
        return self


class OptionHTML(OptionInfo):
    def __init__(self, text):
        super().__init__(str(text).strip(), label='', component=lambda **kwargs: gr.HTML(elem_classes="settings-info", **kwargs))

        self.do_not_save = True


def options_section(section_identifier, options_dict):
    for v in options_dict.values():
        v.section = section_identifier

    return options_dict


def list_checkpoint_tiles():
    import modules.sd_models
    return modules.sd_models.checkpoint_tiles()


def refresh_checkpoints():
    import modules.sd_models
    return modules.sd_models.list_models()


def list_samplers():
    import modules.sd_samplers
    return modules.sd_samplers.all_samplers


hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
tab_names = []

options_templates = {}

options_templates.update(options_section(('saving-images', "Saving images/grids"), {
    "samples_save": OptionInfo(True, "Always save all generated images"),
    "samples_format": OptionInfo('png', 'File format for images'),
    "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
    "save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),

    "grid_save": OptionInfo(True, "Always save all generated image grids"),
    "grid_format": OptionInfo('png', 'File format for grids'),
    "grid_extended_filename": OptionInfo(False, "Add extended info (seed, prompt) to filename when saving grid"),
    "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
    "grid_prevent_empty_spots": OptionInfo(False, "Prevent empty spots in grid (when set to autodetect)"),
    "grid_zip_filename_pattern": OptionInfo("", "Archive filename pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
    "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
    "font": OptionInfo("", "Font for image grids that have text"),
    "grid_text_active_color": OptionInfo("#000000", "Text color for image grids", ui_components.FormColorPicker, {}),
    "grid_text_inactive_color": OptionInfo("#999999", "Inactive text color for image grids", ui_components.FormColorPicker, {}),
    "grid_background_color": OptionInfo("#ffffff", "Background color for image grids", ui_components.FormColorPicker, {}),

    "enable_pnginfo": OptionInfo(True, "Save text information about generation parameters as chunks to png files"),
    "save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters."),
    "save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration."),
    "save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix."),
    "save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
    "save_mask": OptionInfo(False, "For inpainting, save a copy of the greyscale mask"),
    "save_mask_composite": OptionInfo(False, "For inpainting, save a masked composite"),
    "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
    "webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
    "export_for_4chan": OptionInfo(True, "Save copy of large images as JPG").info("if the file size is above the limit, or either width or height are above the limit"),
    "img_downscale_threshold": OptionInfo(4.0, "File size limit for the above option, MB", gr.Number),
    "target_side_length": OptionInfo(4000, "Width/height limit for the above option, in pixels", gr.Number),
    "img_max_size_mp": OptionInfo(200, "Maximum image size", gr.Number).info("in megapixels"),

    "use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"),
    "use_upscaler_name_as_suffix": OptionInfo(False, "Use upscaler name as filename suffix in the extras tab"),
    "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
    "save_init_img": OptionInfo(False, "Save init images when using img2img"),

    "temp_dir":  OptionInfo("", "Directory for temporary images; leave empty for default"),
    "clean_temp_dir_at_start": OptionInfo(False, "Cleanup non-default temporary directory when starting webui"),

    "save_incomplete_images": OptionInfo(False, "Save incomplete images").info("save images that has been interrupted in mid-generation; even if not saved, they will still show up in webui output."),
}))

options_templates.update(options_section(('saving-paths', "Paths for saving"), {
    "outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs),
    "outdir_txt2img_samples": OptionInfo("outputs/txt2img-images", 'Output directory for txt2img images', component_args=hide_dirs),
    "outdir_img2img_samples": OptionInfo("outputs/img2img-images", 'Output directory for img2img images', component_args=hide_dirs),
    "outdir_extras_samples": OptionInfo("outputs/extras-images", 'Output directory for images from extras tab', component_args=hide_dirs),
    "outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below", component_args=hide_dirs),
    "outdir_txt2img_grids": OptionInfo("outputs/txt2img-grids", 'Output directory for txt2img grids', component_args=hide_dirs),
    "outdir_img2img_grids": OptionInfo("outputs/img2img-grids", 'Output directory for img2img grids', component_args=hide_dirs),
    "outdir_save": OptionInfo("log/images", "Directory for saving images using the Save button", component_args=hide_dirs),
    "outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
}))

options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), {
    "save_to_dirs": OptionInfo(True, "Save images to a subdirectory"),
    "grid_save_to_dirs": OptionInfo(True, "Save grids to a subdirectory"),
    "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"),
    "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Images-Filename-Name-and-Subdirectory"),
    "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}),
}))

options_templates.update(options_section(('upscaling', "Upscaling"), {
    "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}).info("0 = no tiling"),
    "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap for ESRGAN upscalers.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}).info("Low values = visible seam"),
    "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI.", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
    "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
}))

options_templates.update(options_section(('face-restoration', "Face restoration"), {
    "face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}),
    "code_former_weight": OptionInfo(0.5, "CodeFormer weight", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}).info("0 = maximum effect; 1 = minimum effect"),
    "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
}))

options_templates.update(options_section(('system', "System"), {
    "show_warnings": OptionInfo(False, "Show warnings in console.").needs_reload_ui(),
    "show_gradio_deprecation_warnings": OptionInfo(True, "Show gradio deprecation warnings in console.").needs_reload_ui(),
    "memmon_poll_rate": OptionInfo(8, "VRAM usage polls per second during generation.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}).info("0 = disable"),
    "samples_log_stdout": OptionInfo(False, "Always print all generation info to standard output"),
    "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
    "print_hypernet_extra": OptionInfo(False, "Print extra hypernetwork information to console."),
    "list_hidden_files": OptionInfo(True, "Load models/files in hidden directories").info("directory is hidden if its name starts with \".\""),
    "disable_mmap_load_safetensors": OptionInfo(False, "Disable memmapping for loading .safetensors files.").info("fixes very slow loading speed in some cases"),
    "hide_ldm_prints": OptionInfo(True, "Prevent Stability-AI's ldm/sgm modules from printing noise to console."),
}))

options_templates.update(options_section(('training', "Training"), {
    "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
    "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
    "save_training_settings_to_txt": OptionInfo(True, "Save textual inversion and hypernet settings to a text file whenever training starts."),
    "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
    "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
    "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
    "training_write_csv_every": OptionInfo(500, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
    "training_xattention_optimizations": OptionInfo(False, "Use cross attention optimizations while training"),
    "training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging."),
    "training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard."),
    "training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."),
}))

options_templates.update(options_section(('sd', "Stable Diffusion"), {
    "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
    "sd_checkpoints_limit": OptionInfo(1, "Maximum number of checkpoints loaded at the same time", gr.Slider, {"minimum": 1, "maximum": 10, "step": 1}),
    "sd_checkpoints_keep_in_cpu": OptionInfo(True, "Only keep one model on device").info("will keep models other than the currently used one in RAM rather than VRAM"),
    "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}).info("obsolete; set to 0 and use the two settings above instead"),
    "sd_unet": OptionInfo("Automatic", "SD Unet", gr.Dropdown, lambda: {"choices": shared_items.sd_unet_items()}, refresh=shared_items.refresh_unet_list).info("choose Unet model: Automatic = use one with same filename as checkpoint; None = use Unet from checkpoint"),
    "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds").needs_reload_ui(),
    "enable_emphasis": OptionInfo(True, "Enable emphasis").info("use (text) to make model pay more attention to text and [text] to make it pay less attention"),
    "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"),
    "comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
    "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP network; 1 ignores none, 2 ignores one layer"),
    "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
    "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU", "NV"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors; use NV to produce same picture as on NVidia videocards"),
    "sd_refiner_checkpoint": OptionInfo(None, "Refiner checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints).info("switch to another model in the middle of generation"),
    "sd_refiner_switch_at": OptionInfo(1.0, "Refiner switch at", gr.Slider, {"minimum": 0.01, "maximum": 1.0, "step": 0.01}).info("fraction of sampling steps when the swtch to refiner model should happen; 1=never, 0.5=switch in the middle of generation"),
}))

options_templates.update(options_section(('sdxl', "Stable Diffusion XL"), {
    "sdxl_crop_top": OptionInfo(0, "crop top coordinate"),
    "sdxl_crop_left": OptionInfo(0, "crop left coordinate"),
    "sdxl_refiner_low_aesthetic_score": OptionInfo(2.5, "SDXL low aesthetic score", gr.Number).info("used for refiner model negative prompt"),
    "sdxl_refiner_high_aesthetic_score": OptionInfo(6.0, "SDXL high aesthetic score", gr.Number).info("used for refiner model prompt"),
}))

options_templates.update(options_section(('vae', "VAE"), {
    "sd_vae_explanation": OptionHTML("""
<abbr title='Variational autoencoder'>VAE</abbr> is a neural network that transforms a standard <abbr title='red/green/blue'>RGB</abbr>
image into latent space representation and back. Latent space representation is what stable diffusion is working on during sampling
(i.e. when the progress bar is between empty and full). For txt2img, VAE is used to create a resulting image after the sampling is finished.
For img2img, VAE is used to process user's input image before the sampling, and to create an image after sampling.
"""),
    "sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
    "sd_vae": OptionInfo("Automatic", "SD VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list).info("choose VAE model: Automatic = use one with same filename as checkpoint; None = use VAE from checkpoint"),
    "sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"),
    "auto_vae_precision": OptionInfo(True, "Automaticlly revert VAE to 32-bit floats").info("triggers when a tensor with NaNs is produced in VAE; disabling the option in this case will result in a black square image"),
    "sd_vae_encode_method": OptionInfo("Full", "VAE type for encode", gr.Radio, {"choices": ["Full", "TAESD"]}).info("method to encode image to latent (use in img2img, hires-fix or inpaint mask)"),
    "sd_vae_decode_method": OptionInfo("Full", "VAE type for decode", gr.Radio, {"choices": ["Full", "TAESD"]}).info("method to decode latent to image"),
}))

options_templates.update(options_section(('img2img', "img2img"), {
    "inpainting_mask_weight": OptionInfo(1.0, "Inpainting conditioning mask strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
    "initial_noise_multiplier": OptionInfo(1.0, "Noise multiplier for img2img", gr.Slider, {"minimum": 0.5, "maximum": 1.5, "step": 0.01}),
    "img2img_color_correction": OptionInfo(False, "Apply color correction to img2img results to match original colors."),
    "img2img_fix_steps": OptionInfo(False, "With img2img, do exactly the amount of steps the slider specifies.").info("normally you'd do less with less denoising"),
    "img2img_background_color": OptionInfo("#ffffff", "With img2img, fill transparent parts of the input image with this color.", ui_components.FormColorPicker, {}),
    "img2img_editor_height": OptionInfo(720, "Height of the image editor", gr.Slider, {"minimum": 80, "maximum": 1600, "step": 1}).info("in pixels").needs_reload_ui(),
    "img2img_sketch_default_brush_color": OptionInfo("#ffffff", "Sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img sketch").needs_reload_ui(),
    "img2img_inpaint_mask_brush_color": OptionInfo("#ffffff", "Inpaint mask brush color", ui_components.FormColorPicker,  {}).info("brush color of inpaint mask").needs_reload_ui(),
    "img2img_inpaint_sketch_default_brush_color": OptionInfo("#ffffff", "Inpaint sketch initial brush color", ui_components.FormColorPicker, {}).info("default brush color of img2img inpaint sketch").needs_reload_ui(),
    "return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
    "return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
}))

options_templates.update(options_section(('optimizations', "Optimizations"), {
    "cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
    "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 15.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
    "token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
    "token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
    "token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
    "pad_cond_uncond": OptionInfo(False, "Pad prompt/negative prompt to be same length").info("improves performance when prompt and negative prompt have different lengths; changes seeds"),
    "persistent_cond_cache": OptionInfo(True, "Persistent cond cache").info("Do not recalculate conds from prompts if prompts have not changed since previous calculation"),
}))

options_templates.update(options_section(('compatibility', "Compatibility"), {
    "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
    "use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
    "no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
    "use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
    "dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers."),
    "hires_fix_use_firstpass_conds": OptionInfo(False, "For hires fix, calculate conds of second pass using extra networks of first pass."),
}))

options_templates.update(options_section(('interrogate', "Interrogate"), {
    "interrogate_keep_models_in_memory": OptionInfo(False, "Keep models in VRAM"),
    "interrogate_return_ranks": OptionInfo(False, "Include ranks of model tags matches in results.").info("booru only"),
    "interrogate_clip_num_beams": OptionInfo(1, "BLIP: num_beams", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}),
    "interrogate_clip_min_length": OptionInfo(24, "BLIP: minimum description length", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}),
    "interrogate_clip_max_length": OptionInfo(48, "BLIP: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}),
    "interrogate_clip_dict_limit": OptionInfo(1500, "CLIP: maximum number of lines in text file").info("0 = No limit"),
    "interrogate_clip_skip_categories": OptionInfo([], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types),
    "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "deepbooru: score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
    "deepbooru_sort_alpha": OptionInfo(True, "deepbooru: sort tags alphabetically").info("if not: sort by score"),
    "deepbooru_use_spaces": OptionInfo(True, "deepbooru: use spaces in tags").info("if not: use underscores"),
    "deepbooru_escape": OptionInfo(True, "deepbooru: escape (\\) brackets").info("so they are used as literal brackets and not for emphasis"),
    "deepbooru_filter_tags": OptionInfo("", "deepbooru: filter out those tags").info("separate by comma"),
}))

options_templates.update(options_section(('extra_networks', "Extra Networks"), {
    "extra_networks_show_hidden_directories": OptionInfo(True, "Show hidden directories").info("directory is hidden if its name starts with \".\"."),
    "extra_networks_hidden_models": OptionInfo("When searched", "Show cards for models in hidden directories", gr.Radio, {"choices": ["Always", "When searched", "Never"]}).info('"When searched" option will only show the item when the search string has 4 characters or more'),
    "extra_networks_default_multiplier": OptionInfo(1.0, "Default multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}),
    "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks").info("in pixels"),
    "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks").info("in pixels"),
    "extra_networks_card_text_scale": OptionInfo(1.0, "Card text scale", gr.Slider, {"minimum": 0.0, "maximum": 2.0, "step": 0.01}).info("1 = original size"),
    "extra_networks_card_show_desc": OptionInfo(True, "Show description on card"),
    "extra_networks_add_text_separator": OptionInfo(" ", "Extra networks separator").info("extra text to add before <...> when adding extra network to prompt"),
    "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order").needs_reload_ui(),
    "textual_inversion_print_at_load": OptionInfo(False, "Print a list of Textual Inversion embeddings when loading model"),
    "textual_inversion_add_hashes_to_infotext": OptionInfo(True, "Add Textual Inversion hashes to infotext"),
    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *hypernetworks]}, refresh=reload_hypernetworks),
}))

options_templates.update(options_section(('ui', "User interface"), {
    "localization": OptionInfo("None", "Localization", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)).needs_reload_ui(),
    "gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes}).info("you can also manually enter any of themes from the <a href='https://huggingface.co/spaces/gradio/theme-gallery'>gallery</a>.").needs_reload_ui(),
    "gradio_themes_cache": OptionInfo(True, "Cache gradio themes locally").info("disable to update the selected Gradio theme"),
    "return_grid": OptionInfo(True, "Show grid in results for web"),
    "do_not_show_images": OptionInfo(False, "Do not show any images in results for web"),
    "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
    "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
    "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"),
    "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"),
    "js_modal_lightbox_gamepad": OptionInfo(False, "Navigate image viewer with gamepad"),
    "js_modal_lightbox_gamepad_repeat": OptionInfo(250, "Gamepad repeat period, in milliseconds"),
    "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."),
    "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group").needs_reload_ui(),
    "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_reload_ui(),
    "keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
    "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
    "keyedit_delimiters": OptionInfo(".,\\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"),
    "keyedit_move": OptionInfo(True, "Alt+left/right moves prompt elements"),
    "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}).js("info", "settingsHintsShowQuicksettings").info("setting entries that appear at the top of page rather than in settings tab").needs_reload_ui(),
    "ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_reload_ui(),
    "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_reload_ui(),
    "ui_reorder_list": OptionInfo([], "txt2img/img2img UI item order", ui_components.DropdownMulti, lambda: {"choices": list(shared_items.ui_reorder_categories())}).info("selected items appear first").needs_reload_ui(),
    "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_reload_ui(),
    "hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_reload_ui(),
    "disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_reload_ui(),
}))


options_templates.update(options_section(('infotext', "Infotext"), {
    "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"),
    "add_model_name_to_info": OptionInfo(True, "Add model name to generation information"),
    "add_user_name_to_info": OptionInfo(False, "Add user name to generation information when authenticated"),
    "add_version_to_infotext": OptionInfo(True, "Add program version to generation information"),
    "disable_weights_auto_swap": OptionInfo(True, "Disregard checkpoint information from pasted infotext").info("when reading generation parameters from text into UI"),
    "infotext_styles": OptionInfo("Apply if any", "Infer styles from prompts of pasted infotext", gr.Radio, {"choices": ["Ignore", "Apply", "Discard", "Apply if any"]}).info("when reading generation parameters from text into UI)").html("""<ul style='margin-left: 1.5em'>
<li>Ignore: keep prompt and styles dropdown as it is.</li>
<li>Apply: remove style text from prompt, always replace styles dropdown value with found styles (even if none are found).</li>
<li>Discard: remove style text from prompt, keep styles dropdown as it is.</li>
<li>Apply if any: remove style text from prompt; if any styles are found in prompt, put them into styles dropdown, otherwise keep it as it is.</li>
</ul>"""),

}))

options_templates.update(options_section(('ui', "Live previews"), {
    "show_progressbar": OptionInfo(True, "Show progressbar"),
    "live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
    "live_previews_image_format": OptionInfo("png", "Live preview file format", gr.Radio, {"choices": ["jpeg", "png", "webp"]}),
    "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
    "show_progress_every_n_steps": OptionInfo(10, "Live preview display period", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}).info("in sampling steps - show new live preview image every N sampling steps; -1 = only show after completion of batch"),
    "show_progress_type": OptionInfo("Approx NN", "Live preview method", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap", "TAESD"]}).info("Full = slow but pretty; Approx NN and TAESD = fast but low quality; Approx cheap = super fast but terrible otherwise"),
    "live_preview_content": OptionInfo("Prompt", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}),
    "live_preview_refresh_period": OptionInfo(1000, "Progressbar and preview update period").info("in milliseconds"),
}))

options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
    "hide_samplers": OptionInfo([], "Hide samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}).needs_reload_ui(),
    "eta_ddim": OptionInfo(0.0, "Eta for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; higher = more unperdictable results"),
    "eta_ancestral": OptionInfo(1.0, "Eta for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}).info("noise multiplier; applies to Euler a and other samplers that have a in them"),
    "ddim_discretize": OptionInfo('uniform', "img2img DDIM discretize", gr.Radio, {"choices": ['uniform', 'quad']}),
    's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 100.0, "step": 0.01}),
    's_tmin':  OptionInfo(0.0, "sigma tmin",  gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
    's_tmax':  OptionInfo(0.0, "sigma tmax",  gr.Slider, {"minimum": 0.0, "maximum": 999.0, "step": 0.01}).info("0 = inf"),
    's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
    'k_sched_type':  OptionInfo("Automatic", "scheduler type", gr.Dropdown, {"choices": ["Automatic", "karras", "exponential", "polyexponential"]}).info("lets you override the noise schedule for k-diffusion samplers; choosing Automatic disables the three parameters below"),
    'sigma_min': OptionInfo(0.0, "sigma min", gr.Number).info("0 = default (~0.03); minimum noise strength for k-diffusion noise scheduler"),
    'sigma_max': OptionInfo(0.0, "sigma max", gr.Number).info("0 = default (~14.6); maximum noise strength for k-diffusion noise schedule"),
    'rho':  OptionInfo(0.0, "rho", gr.Number).info("0 = default (7 for karras, 1 for polyexponential); higher values result in a more steep noise schedule (decreases faster)"),
    'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}).info("ENSD; does not improve anything, just produces different results for ancestral samplers - only useful for reproducing images"),
    'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma").link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/6044"),
    'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
    'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
    'uni_pc_order': OptionInfo(3, "UniPC order", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}).info("must be < sampling steps"),
    'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),
}))

options_templates.update(options_section(('postprocessing', "Postprocessing"), {
    'postprocessing_enable_in_main_ui': OptionInfo([], "Enable postprocessing operations in txt2img and img2img tabs", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
    'postprocessing_operation_order': OptionInfo([], "Postprocessing operation order", ui_components.DropdownMulti, lambda: {"choices": [x.name for x in shared_items.postprocessing_scripts()]}),
    'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
}))

options_templates.update(options_section((None, "Hidden options"), {
    "disabled_extensions": OptionInfo([], "Disable these extensions"),
    "disable_all_extensions": OptionInfo("none", "Disable all extensions (preserves the list of disabled extensions)", gr.Radio, {"choices": ["none", "extra", "all"]}),
    "restore_config_state_file": OptionInfo("", "Config state file to restore from, under 'config-states/' folder"),
    "sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
}))


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:
                assert not cmd_opts.freeze_settings, "changing settings is disabled"

                info = opts.data_labels.get(key, None)
                if info.do_not_save:
                    return

                comp_args = info.component_args if info else None
                if isinstance(comp_args, dict) and comp_args.get('visible', True) is False:
                    raise RuntimeError(f"not possible to set {key} because it is restricted")

                if cmd_opts.hide_ui_dir_config and key in restricted_opts:
                    raise RuntimeError(f"not possible to set {key} because it is restricted")

                self.data[key] = value
                return

        return super(Options, self).__setattr__(key, value)

    def __getattr__(self, item):
        if self.data is not None:
            if item in self.data:
                return self.data[item]

        if item in self.data_labels:
            return self.data_labels[item].default

        return super(Options, self).__getattribute__(item)

    def set(self, key, value):
        """sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""

        oldval = self.data.get(key, None)
        if oldval == value:
            return False

        if self.data_labels[key].do_not_save:
            return False

        try:
            setattr(self, key, value)
        except RuntimeError:
            return False

        if self.data_labels[key].onchange is not None:
            try:
                self.data_labels[key].onchange()
            except Exception as e:
                errors.display(e, f"changing setting {key} to {value}")
                setattr(self, key, oldval)
                return False

        return True

    def get_default(self, key):
        """returns the default value for the key"""

        data_label = self.data_labels.get(key)
        if data_label is None:
            return None

        return data_label.default

    def save(self, filename):
        assert not cmd_opts.freeze_settings, "saving settings is disabled"

        with open(filename, "w", encoding="utf8") as file:
            json.dump(self.data, file, indent=4)

    def same_type(self, x, y):
        if x is None or y is None:
            return True

        type_x = self.typemap.get(type(x), type(x))
        type_y = self.typemap.get(type(y), type(y))

        return type_x == type_y

    def load(self, filename):
        with open(filename, "r", encoding="utf8") as file:
            self.data = json.load(file)

        # 1.1.1 quicksettings list migration
        if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:
            self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]

        # 1.4.0 ui_reorder
        if isinstance(self.data.get('ui_reorder'), str) and self.data.get('ui_reorder') and "ui_reorder_list" not in self.data:
            self.data['ui_reorder_list'] = [i.strip() for i in self.data.get('ui_reorder').split(',')]

        bad_settings = 0
        for k, v in self.data.items():
            info = self.data_labels.get(k, None)
            if info is not None and not self.same_type(info.default, v):
                print(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})", file=sys.stderr)
                bad_settings += 1

        if bad_settings > 0:
            print(f"The program is likely to not work with bad settings.\nSettings file: {filename}\nEither fix the file, or delete it and restart.", file=sys.stderr)

    def onchange(self, key, func, call=True):
        item = self.data_labels.get(key)
        item.onchange = func

        if call:
            func()

    def dumpjson(self):
        d = {k: self.data.get(k, v.default) for k, v in self.data_labels.items()}
        d["_comments_before"] = {k: v.comment_before for k, v in self.data_labels.items() if v.comment_before is not None}
        d["_comments_after"] = {k: v.comment_after for k, v in self.data_labels.items() if v.comment_after is not None}
        return json.dumps(d)

    def add_option(self, key, info):
        self.data_labels[key] = info

    def reorder(self):
        """reorder settings so that all items related to section always go together"""

        section_ids = {}
        settings_items = self.data_labels.items()
        for _, item in settings_items:
            if item.section not in section_ids:
                section_ids[item.section] = len(section_ids)

        self.data_labels = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))

    def cast_value(self, key, value):
        """casts an arbitrary to the same type as this setting's value with key
        Example: cast_value("eta_noise_seed_delta", "12") -> returns 12 (an int rather than str)
        """

        if value is None:
            return None

        default_value = self.data_labels[key].default
        if default_value is None:
            default_value = getattr(self, key, None)
        if default_value is None:
            return None

        expected_type = type(default_value)
        if expected_type == bool and value == "False":
            value = False
        else:
            value = expected_type(value)

        return value


opts = Options()
if os.path.exists(config_filename):
    opts.load(config_filename)


class Shared(sys.modules[__name__].__class__):
    """
    this class is here to provide sd_model field as a property, so that it can be created and loaded on demand rather than
    at program startup.
    """

    sd_model_val = None

    @property
    def sd_model(self):
        import modules.sd_models

        return modules.sd_models.model_data.get_sd_model()

    @sd_model.setter
    def sd_model(self, value):
        import modules.sd_models

        modules.sd_models.model_data.set_sd_model(value)


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
sys.modules[__name__].__class__ = Shared

settings_components = None
"""assinged from ui.py, a mapping on setting names to gradio components repsponsible for those settings"""

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},
}

sd_upscalers = []

clip_model = None

progress_print_out = sys.stdout

gradio_theme = gr.themes.Base()


def reload_gradio_theme(theme_name=None):
    global gradio_theme
    if not theme_name:
        theme_name = opts.gradio_theme

    default_theme_args = dict(
        font=["Source Sans Pro", 'ui-sans-serif', 'system-ui', 'sans-serif'],
        font_mono=['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'],
    )

    if theme_name == "Default":
        gradio_theme = gr.themes.Default(**default_theme_args)
    else:
        try:
            theme_cache_dir = os.path.join(script_path, 'tmp', 'gradio_themes')
            theme_cache_path = os.path.join(theme_cache_dir, f'{theme_name.replace("/", "_")}.json')
            if opts.gradio_themes_cache and os.path.exists(theme_cache_path):
                gradio_theme = gr.themes.ThemeClass.load(theme_cache_path)
            else:
                os.makedirs(theme_cache_dir, exist_ok=True)
                gradio_theme = gr.themes.ThemeClass.from_hub(theme_name)
                gradio_theme.dump(theme_cache_path)
        except Exception as e:
            errors.display(e, "changing gradio theme")
            gradio_theme = gr.themes.Default(**default_theme_args)


class TotalTQDM:
    def __init__(self):
        self._tqdm = None

    def reset(self):
        self._tqdm = tqdm.tqdm(
            desc="Total progress",
            total=state.job_count * state.sampling_steps,
            position=1,
            file=progress_print_out
        )

    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()

mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
mem_mon.start()


def natural_sort_key(s, regex=re.compile('([0-9]+)')):
    return [int(text) if text.isdigit() else text.lower() for text in regex.split(s)]


def listfiles(dirname):
    filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=natural_sort_key) if not x.startswith(".")]
    return [file for file in filenames if os.path.isfile(file)]


def html_path(filename):
    return os.path.join(script_path, "html", filename)


def html(filename):
    path = html_path(filename)

    if os.path.exists(path):
        with open(path, encoding="utf8") as file:
            return file.read()

    return ""


def walk_files(path, allowed_extensions=None):
    if not os.path.exists(path):
        return

    if allowed_extensions is not None:
        allowed_extensions = set(allowed_extensions)

    items = list(os.walk(path, followlinks=True))
    items = sorted(items, key=lambda x: natural_sort_key(x[0]))

    for root, _, files in items:
        for filename in sorted(files, key=natural_sort_key):
            if allowed_extensions is not None:
                _, ext = os.path.splitext(filename)
                if ext not in allowed_extensions:
                    continue

            if not opts.list_hidden_files and ("/." in root or "\\." in root):
                continue

            yield os.path.join(root, filename)


def ldm_print(*args, **kwargs):
    if opts.hide_ldm_prints:
        return

    print(*args, **kwargs)