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/glass", "gradio/monochrome", "gradio/seafoam", "gradio/soft", "freddyaboulton/dracula_revamped", "gradio/dracula_test", "abidlabs/dracula_test", "abidlabs/pakistan", "dawood/microsoft_windows", "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.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"[{label}]" return self def js(self, label, js_func): self.comment_before += f"[{label}]" return self def info(self, info): self.comment_after += f"({info})" return self def html(self, html): self.comment_after += html return self def needs_restart(self): self.comment_after += " (requires restart)" 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 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"), })) 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."), "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"), })) 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_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}), "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"), "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"), "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 image's transparent parts with this color.", ui_components.FormColorPicker, {}), "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."), "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"), "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"), "randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors"), })) 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(('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"), "experimental_persistent_cond_cache": OptionInfo(False, "persistent cond cache").info("Experimental, keep cond caches across jobs, reduce overhead."), })) 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 Options"), { "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_restart(), "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_restart(), "gradio_theme": OptionInfo("Default", "Gradio theme", ui_components.DropdownEditable, lambda: {"choices": ["Default"] + gradio_hf_hub_themes}).needs_restart(), "img2img_editor_height": OptionInfo(720, "img2img: height of image editor", gr.Slider, {"minimum": 80, "maximum": 1600, "step": 1}).info("in pixels").needs_restart(), "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"), "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_restart(), "dimensions_and_batch_together": OptionInfo(True, "Show Width/Height and Batch sliders in same row").needs_restart(), "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 ", 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_restart(), "ui_tab_order": OptionInfo([], "UI tab order", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(), "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}).needs_restart(), "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_restart(), "hires_fix_show_sampler": OptionInfo(False, "Hires fix: show hires checkpoint and sampler selection").needs_restart(), "hires_fix_show_prompts": OptionInfo(False, "Hires fix: show hires prompt and negative prompt").needs_restart(), "disable_token_counters": OptionInfo(False, "Disable prompt token counters").needs_restart(), })) 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(""""""), })) 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_restart(), "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": 1.0, "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}), '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) 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 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: gradio_theme = gr.themes.ThemeClass.from_hub(theme_name) 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)