import datetime import json import os import sys import time import gradio as gr import tqdm import modules.interrogate import modules.memmon import modules.styles import modules.devices as devices from modules import 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 # pylint: disable=W0611 import modules.paths_internal as paths from setup import log as setup_log # pylint: disable=E0611 errors.install() demo = None log = setup_log parser = cmd_args.parser script_loading.preload_extensions(paths.extensions_dir, parser) script_loading.preload_extensions(paths.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", } ui_reorder_categories = [ "inpaint", "sampler", "checkboxes", "hires_fix", "dimensions", "cfg", "seed", "batch", "override_settings", "scripts", ] cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \ (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer']) device = devices.device sd_upscalers = [] sd_model = None clip_model = None def reload_hypernetworks(): from modules.hypernetworks import hypernetwork global hypernetworks # pylint: disable=W0603 hypernetworks = hypernetwork.list_hypernetworks(opts.hypernetwork_dir) class State: skipped = False interrupted = False job = "" job_no = 0 job_count = 0 processing_has_refined_job_count = False job_timestamp = '0' sampling_step = 0 sampling_steps = 0 current_latent = None current_image = None current_image_sampling_step = 0 id_live_preview = 0 textinfo = None time_start = None need_restart = False server_start = None def skip(self): self.skipped = True def interrupt(self): self.interrupted = True def nextjob(self): if opts.live_previews_enable and opts.show_progress_every_n_steps == -1: self.do_set_current_image() self.job_no += 1 self.sampling_step = 0 self.current_image_sampling_step = 0 def dict(self): obj = { "skipped": self.skipped, "interrupted": self.interrupted, "job": self.job, "job_count": self.job_count, "job_timestamp": self.job_timestamp, "job_no": self.job_no, "sampling_step": self.sampling_step, "sampling_steps": self.sampling_steps, } return obj def begin(self): self.sampling_step = 0 self.job_count = -1 self.processing_has_refined_job_count = False self.job_no = 0 self.job_timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S") self.current_latent = None self.current_image = None self.current_image_sampling_step = 0 self.id_live_preview = 0 self.skipped = False self.interrupted = False self.textinfo = None self.time_start = time.time() devices.torch_gc() def end(self): 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 if opts.show_progress_grid: import modules.sd_samplers # pylint: disable=W0621 self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent)) else: self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent)) self.current_image_sampling_step = self.sampling_step def assign_current_image(self, image): self.current_image = image self.id_live_preview += 1 state = State() state.server_start = time.time() prompt_styles = modules.styles.StyleDatabase('styles.csv') 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): self.default = default self.label = label self.component = component self.component_args = component_args self.onchange = onchange self.section = section self.refresh = refresh def options_section(section_identifier, options_dict): for _k, v in options_dict.items(): v.section = section_identifier return options_dict def list_checkpoint_tiles(): import modules.sd_models # pylint: disable=W0621 return modules.sd_models.checkpoint_tiles() def refresh_checkpoints(): import modules.sd_models # pylint: disable=W0621 return modules.sd_models.list_models() def list_samplers(): import modules.sd_samplers # pylint: disable=W0621 return modules.sd_samplers.all_samplers hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config} tab_names = [] options_templates = {} default_checkpoint = list_checkpoint_tiles()[0] if len(list_checkpoint_tiles()) > 0 else "model.ckpt" options_templates.update(options_section(('sd', "Stable Diffusion"), { "sd_model_checkpoint": OptionInfo(default_checkpoint, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints), "sd_checkpoint_cache": OptionInfo(0, "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), "sd_vae_as_default": OptionInfo(True, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"), "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 (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(True, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."), "enable_emphasis": OptionInfo(True, "Emphasis: 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, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }), "CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}), "upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"), "cross_attention_optimization": OptionInfo("Scaled-Dot-Product", "Cross-attention optimization method", gr.Radio, lambda: {"choices": shared_items.list_crossattention() }), "cross_attention_options": OptionInfo([], "Cross-attention advanced options", gr.CheckboxGroup, lambda: {"choices": ['xFormers enable flash Attention', 'SDP disable memory attention']}), "disable_nan_check": OptionInfo(False, "Do not check if produced images/latent spaces have NaN values"), "opt_channelslast": OptionInfo(False, "Use channels last as torch memory format "), "cudnn_benchmark": OptionInfo(False, "Enable CUDA cuDNN benchmark feature"), "sub_quad_q_chunk_size": OptionInfo(512, "Sub-quadratic cross-attention query chunk size for the layer optimization to use", gr.Slider, {"minimum": 16, "maximum": 8192, "step": 8}), "sub_quad_kv_chunk_size": OptionInfo(512, "Sub-quadratic cross-attentionkv chunk size for the sub-quadratic cross-attention layer optimization to use", gr.Slider, {"minimum": 0, "maximum": 8192, "step": 8}), "sub_quad_chunk_threshold": OptionInfo(80, "Sub-quadratic cross-attention percentage of VRAM chunking threshold", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}), "always_batch_cond_uncond": OptionInfo(False, "Disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram"), "upcast_sampling": OptionInfo(False, "Enable upcast sampling. Usually produces similar results to --no-half with better performance while using less memory"), })) options_templates.update(options_section(('system-paths', "System Paths"), { "ckpt_dir": OptionInfo(os.path.join(paths.models_path, 'Stable-diffusion'), "Path to directory with stable diffusion checkpoints"), "vae_dir": OptionInfo(os.path.join(paths.models_path, 'VAE'), "Path to directory with VAE files"), "embeddings_dir": OptionInfo(os.path.join(paths.models_path, 'embeddings'), "Embeddings directory for textual inversion"), "embeddings_templates_dir": OptionInfo(os.path.join(paths.script_path, 'train/templates'), "Embeddings train templates directory"), "hypernetwork_dir": OptionInfo(os.path.join(paths.models_path, 'hypernetworks'), "Hypernetwork directory"), "codeformer_models_path": OptionInfo(os.path.join(paths.models_path, 'Codeformer'), "Path to directory with codeformer model file(s)."), "gfpgan_models_path": OptionInfo(os.path.join(paths.models_path, 'GFPGAN'), "Path to directory with GFPGAN model file(s)"), "esrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'ESRGAN'), "Path to directory with ESRGAN model file(s)"), "bsrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'BSRGAN'), "Path to directory with BSRGAN model file(s)"), "realesrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'RealESRGAN'), "Path to directory with RealESRGAN model file(s)"), "clip_models_path": OptionInfo(os.path.join(paths.models_path, 'CLIP'), "Path to directory with CLIP model file(s)"), "lora_dir": OptionInfo(os.path.join(paths.models_path, 'Lora'), "Path to directory with Lora network(s)"), "lyco_dir": OptionInfo(os.path.join(paths.models_path, 'LyCORIS'), "Path to directory with LyCORIS network(s)"), # "gfpgan_model": OptionInfo("", "GFPGAN model file name"), })) options_templates.update(options_section(('saving-images', "Image options"), { "samples_save": OptionInfo(True, "Always save all generated images"), "samples_format": OptionInfo('jpg', 'File format for images'), "samples_filename_pattern": OptionInfo("", "Images filename pattern", component_args=hide_dirs), "save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs), "grid_save": OptionInfo(True, "Always save all generated image grids"), "grid_format": OptionInfo('jpg', 'File format for grids'), "grid_extended_filename": OptionInfo(True, "Add extended info (seed, prompt) to filename when saving grid"), "grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"), "grid_prevent_empty_spots": OptionInfo(True, "Prevent empty spots in grid (when set to autodetect)"), "n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}), "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(True, "Save a copy of image before doing face restoration."), "save_images_before_highres_fix": OptionInfo(True, "Save a copy of image before applying highres fix."), "save_images_before_color_correction": OptionInfo(True, "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(85, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}), "webp_lossless": OptionInfo(False, "Use lossless compression for webp images"), "img_max_size_mp": OptionInfo(200, "Maximum image size, in megapixels", gr.Number), "use_original_name_batch": OptionInfo(True, "Use original name for output filename during batch process in extras tab"), "use_upscaler_name_as_suffix": OptionInfo(True, "Use upscaler name as filename suffix in the extras tab"), "save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"), "save_to_dirs": OptionInfo(False, "Save images to a subdirectory"), "grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory"), "use_save_to_dirs_for_ui": OptionInfo(False, "When using \"Save\" button, save images to a subdirectory"), "directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs), "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}), "temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default"), "clean_temp_dir_at_start": OptionInfo(True, "Cleanup non-default temporary directory when starting webui"), })) options_templates.update(options_section(('saving-paths', "Image Paths"), { "outdir_samples": OptionInfo("", "Output directory for images; if empty, defaults to three directories below", component_args=hide_dirs), "outdir_txt2img_samples": OptionInfo("outputs/text", 'Output directory for txt2img images', component_args=hide_dirs), "outdir_img2img_samples": OptionInfo("outputs/image", 'Output directory for img2img images', component_args=hide_dirs), "outdir_extras_samples": OptionInfo("outputs/extras", 'Output directory for images from extras tab', component_args=hide_dirs), "outdir_grids": OptionInfo("", "Output directory for grids; if empty, defaults to two directories below", component_args=hide_dirs), "outdir_txt2img_grids": OptionInfo("outputs/grids", 'Output directory for txt2img grids', component_args=hide_dirs), "outdir_img2img_grids": OptionInfo("outputs/grids", 'Output directory for img2img grids', component_args=hide_dirs), "outdir_save": OptionInfo("outputs/save", "Directory for saving images using the Save button", component_args=hide_dirs), })) options_templates.update(options_section(('upscaling', "Upscaling"), { "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}), "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}), "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}), "upscaler_for_img2img": OptionInfo("SwinIR_4x", "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}), "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)."), })) options_templates.update(options_section(('face-restoration', "Face restoration"), { "face_restoration_model": OptionInfo("CodeFormer", "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}), "code_former_weight": OptionInfo(0.2, "CodeFormer weight parameter; 0 = maximum effect; 1 = minimum effect", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}), "face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"), })) options_templates.update(options_section(('system', "System"), { "memmon_poll_rate": OptionInfo(2, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}), "multiple_tqdm": OptionInfo(False, "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."), })) 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(True, "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(0, "Save an csv containing the loss to log directory every N steps, 0 to disable"), "training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging."), "training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard."), "training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk."), })) options_templates.update(options_section(('interrogate', "Interrogate Options"), { "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"), "interrogate_return_ranks": OptionInfo(True, "Interrogate: include ranks of model tags matches in results (Has no effect on caption-based interrogators)."), "interrogate_clip_num_beams": OptionInfo(1, "Interrogate: num_beams for BLIP", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1}), "interrogate_clip_min_length": OptionInfo(32, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}), "interrogate_clip_max_length": OptionInfo(192, "Interrogate: maximum description length", gr.Slider, {"minimum": 1, "maximum": 256, "step": 1}), "interrogate_clip_dict_limit": OptionInfo(2048, "CLIP: maximum number of lines in text file (0 = No limit)"), "interrogate_clip_skip_categories": OptionInfo(["artists", "movements", "flavors"], "CLIP: skip inquire categories", gr.CheckboxGroup, lambda: {"choices": modules.interrogate.category_types()}, refresh=modules.interrogate.category_types), "interrogate_deepbooru_score_threshold": OptionInfo(0.65, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}), "deepbooru_sort_alpha": OptionInfo(False, "Interrogate: deepbooru sort alphabetically"), "deepbooru_use_spaces": OptionInfo(False, "use spaces for tags in deepbooru"), "deepbooru_escape": OptionInfo(True, "escape (\\) brackets in deepbooru (so they are used as literal brackets and not for emphasis)"), "deepbooru_filter_tags": OptionInfo("", "filter out those tags from deepbooru output (separated by comma)"), })) options_templates.update(options_section(('extra_networks', "Extra Networks"), { "extra_networks_default_view": OptionInfo("cards", "Default view for Extra Networks", gr.Dropdown, {"choices": ["cards", "thumbs"]}), "extra_networks_default_multiplier": OptionInfo(1.0, "Multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"), "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"), "extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"), "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": [""] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks), })) options_templates.update(options_section(('ui', "User interface"), { "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"), "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"), "disable_weights_auto_swap": OptionInfo(True, "When reading generation parameters from text into UI (from PNG info or pasted text), do not change the selected model/checkpoint."), "send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"), "send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"), "font": OptionInfo("", "Font for image grids that have text"), "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"), "show_progress_in_title": OptionInfo(False, "Show generation progress in window title."), "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}), "quicksettings": OptionInfo("sd_model_checkpoint", "Quicksettings list"), "hidden_tabs": OptionInfo([], "Hidden UI tabs (requires restart)", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}), "ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"), "ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"), })) options_templates.update(options_section(('ui', "Live previews"), { "show_progressbar": OptionInfo(True, "Show progressbar"), "live_previews_enable": OptionInfo(True, "Show live previews of the created image"), "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"), "show_progress_every_n_steps": OptionInfo(-1, "Show new live preview image every N sampling steps. Set to -1 to show after completion of batch.", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}), "show_progress_type": OptionInfo("Full", "Image creation progress preview mode", gr.Radio, {"choices": ["Full", "Approx NN", "Approx cheap"]}), "live_preview_content": OptionInfo("Combined", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"]}), "live_preview_refresh_period": OptionInfo(1000, "Progressbar/preview update period, in milliseconds") })) options_templates.update(options_section(('sampler-params', "Sampler parameters"), { "hide_samplers": OptionInfo(["Euler", "LMS", "Heun", "DPM2", "DPM2 a", "DPM++ 2M", "DPM fast", "DPM adaptive", "DPM++ 2S a Karras", "DPM++ 2S a", "DPM++ SDE Karras", "DPM2 a Karras", "LMS Karras"], "Hide samplers in user interface (requires restart)", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}), "eta_ddim": OptionInfo(0.0, "eta (noise multiplier) for DDIM", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), "eta_ancestral": OptionInfo(1.0, "eta (noise multiplier) for ancestral samplers", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}), "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}), 'eta_noise_seed_delta': OptionInfo(0, "Eta noise seed delta", gr.Number, {"precision": 0}), 'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"), 'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}), 'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}), 'uni_pc_order': OptionInfo(3, "UniPC order (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}), 'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"), })) options_templates.update(options_section(('postprocessing', "Postprocessing"), { 'postprocessing_enable_in_main_ui': OptionInfo([], "Enable 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"]}), "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) bad_settings = 0 for k, v in self.data.items(): info = self.data_labels.get(k, None) if info is not None and not self.same_type(info.default, v): log.error(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})", file=sys.stderr) bad_settings += 1 if bad_settings > 0: log.error(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, self.data_labels.get(k).default) for k in self.data_labels.keys()} 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 k, item in settings_items: if item.section not in section_ids: section_ids[item.section] = len(section_ids) self.data_labels = {k: v for k, v in sorted(settings_items, key=lambda x: section_ids[x[1].section])} def cast_value(self, key, value): """casts an arbitrary to the same type as this setting's value with key Example: cast_value("eta_noise_seed_delta", "12") -> returns 12 (an int rather than str) """ if value is None: return None default_value = self.data_labels[key].default if default_value is None: default_value = getattr(self, key, None) if default_value is None: return None expected_type = type(default_value) if expected_type == bool and value == "False": value = False else: value = expected_type(value) return value opts = Options() batch_cond_uncond = opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram) parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram xformers_available = False config_filename = cmd_opts.ui_settings_file os.makedirs(opts.hypernetwork_dir, exist_ok=True) hypernetworks = {} loaded_hypernetworks = [] if os.path.exists(config_filename): opts.load(config_filename) cmd_opts = cmd_args.compatibility_args(opts, cmd_opts) settings_components = None """assinged from ui.py, a mapping on setting anmes 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}, } progress_print_out = sys.stdout class TotalTQDM: def __init__(self): self._tqdm = None def reset(self): self._tqdm = tqdm.tqdm( desc="Total", total=state.job_count * state.sampling_steps, position=1, 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 listfiles(dirname): filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=str.lower) if not x.startswith(".")] return [file for file in filenames if os.path.isfile(file)] def html_path(filename): return os.path.join(paths.script_path, "html", filename) def html(filename): path = html_path(filename) if os.path.exists(path): with open(path, encoding="utf8") as file: return file.read() return ""