| import argparse | |
| import datetime | |
| import json | |
| import os | |
| import sys | |
| import time | |
| from PIL import Image | |
| import gradio as gr | |
| import tqdm | |
| import modules.artists | |
| import modules.interrogate | |
| import modules.memmon | |
| import modules.styles | |
| import modules.devices as devices | |
| from modules import localization, sd_vae, extensions, script_loading | |
| from modules.paths import models_path, script_path, sd_path | |
| demo = None | |
| sd_model_file = os.path.join(script_path, 'model.ckpt') | |
| default_sd_model_file = sd_model_file | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--config", type=str, default=os.path.join(script_path, "configs/v1-inference.yaml"), help="path to config which constructs model",) | |
| parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",) | |
| parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints") | |
| parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN')) | |
| parser.add_argument("--gfpgan-model", type=str, help="GFPGAN model file name", default=None) | |
| parser.add_argument("--no-half", action='store_true', help="do not switch the model to 16-bit floats") | |
| parser.add_argument("--no-half-vae", action='store_true', help="do not switch the VAE model to 16-bit floats") | |
| parser.add_argument("--no-progressbar-hiding", action='store_true', help="do not hide progressbar in gradio UI (we hide it because it slows down ML if you have hardware acceleration in browser)") | |
| parser.add_argument("--max-batch-count", type=int, default=16, help="maximum batch count value for the UI") | |
| parser.add_argument("--embeddings-dir", type=str, default=os.path.join(script_path, 'embeddings'), help="embeddings directory for textual inversion (default: embeddings)") | |
| parser.add_argument("--hypernetwork-dir", type=str, default=os.path.join(models_path, 'hypernetworks'), help="hypernetwork directory") | |
| parser.add_argument("--localizations-dir", type=str, default=os.path.join(script_path, 'localizations'), help="localizations directory") | |
| parser.add_argument("--allow-code", action='store_true', help="allow custom script execution from webui") | |
| parser.add_argument("--medvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a little speed for low VRM usage") | |
| parser.add_argument("--lowvram", action='store_true', help="enable stable diffusion model optimizations for sacrificing a lot of speed for very low VRM usage") | |
| parser.add_argument("--lowram", action='store_true', help="load stable diffusion checkpoint weights to VRAM instead of RAM") | |
| parser.add_argument("--always-batch-cond-uncond", action='store_true', help="disables cond/uncond batching that is enabled to save memory with --medvram or --lowvram") | |
| parser.add_argument("--unload-gfpgan", action='store_true', help="does not do anything.") | |
| parser.add_argument("--precision", type=str, help="evaluate at this precision", choices=["full", "autocast"], default="autocast") | |
| parser.add_argument("--share", action='store_true', help="use share=True for gradio and make the UI accessible through their site") | |
| parser.add_argument("--ngrok", type=str, help="ngrok authtoken, alternative to gradio --share", default=None) | |
| parser.add_argument("--ngrok-region", type=str, help="The region in which ngrok should start.", default="us") | |
| parser.add_argument("--enable-insecure-extension-access", action='store_true', help="enable extensions tab regardless of other options") | |
| parser.add_argument("--codeformer-models-path", type=str, help="Path to directory with codeformer model file(s).", default=os.path.join(models_path, 'Codeformer')) | |
| parser.add_argument("--gfpgan-models-path", type=str, help="Path to directory with GFPGAN model file(s).", default=os.path.join(models_path, 'GFPGAN')) | |
| parser.add_argument("--esrgan-models-path", type=str, help="Path to directory with ESRGAN model file(s).", default=os.path.join(models_path, 'ESRGAN')) | |
| parser.add_argument("--bsrgan-models-path", type=str, help="Path to directory with BSRGAN model file(s).", default=os.path.join(models_path, 'BSRGAN')) | |
| parser.add_argument("--realesrgan-models-path", type=str, help="Path to directory with RealESRGAN model file(s).", default=os.path.join(models_path, 'RealESRGAN')) | |
| parser.add_argument("--clip-models-path", type=str, help="Path to directory with CLIP model file(s).", default=None) | |
| parser.add_argument("--xformers", action='store_true', help="enable xformers for cross attention layers") | |
| parser.add_argument("--force-enable-xformers", action='store_true', help="enable xformers for cross attention layers regardless of whether the checking code thinks you can run it; do not make bug reports if this fails to work") | |
| parser.add_argument("--deepdanbooru", action='store_true', help="does not do anything") | |
| parser.add_argument("--opt-split-attention", action='store_true', help="force-enables Doggettx's cross-attention layer optimization. By default, it's on for torch cuda.") | |
| parser.add_argument("--opt-split-attention-invokeai", action='store_true', help="force-enables InvokeAI's cross-attention layer optimization. By default, it's on when cuda is unavailable.") | |
| parser.add_argument("--opt-split-attention-v1", action='store_true', help="enable older version of split attention optimization that does not consume all the VRAM it can find") | |
| parser.add_argument("--disable-opt-split-attention", action='store_true', help="force-disables cross-attention layer optimization") | |
| parser.add_argument("--use-cpu", nargs='+', help="use CPU as torch device for specified modules", default=[], type=str.lower) | |
| parser.add_argument("--listen", action='store_true', help="launch gradio with 0.0.0.0 as server name, allowing to respond to network requests") | |
| parser.add_argument("--port", type=int, help="launch gradio with given server port, you need root/admin rights for ports < 1024, defaults to 7860 if available", default=None) | |
| parser.add_argument("--show-negative-prompt", action='store_true', help="does not do anything", default=False) | |
| parser.add_argument("--ui-config-file", type=str, help="filename to use for ui configuration", default=os.path.join(script_path, 'ui-config.json')) | |
| parser.add_argument("--hide-ui-dir-config", action='store_true', help="hide directory configuration from webui", default=False) | |
| parser.add_argument("--freeze-settings", action='store_true', help="disable editing settings", default=False) | |
| parser.add_argument("--ui-settings-file", type=str, help="filename to use for ui settings", default=os.path.join(script_path, 'config.json')) | |
| parser.add_argument("--gradio-debug", action='store_true', help="launch gradio with --debug option") | |
| parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None) | |
| parser.add_argument("--gradio-img2img-tool", type=str, help='gradio image uploader tool: can be either editor for ctopping, or color-sketch for drawing', choices=["color-sketch", "editor"], default="editor") | |
| parser.add_argument("--gradio-inpaint-tool", type=str, choices=["sketch", "color-sketch"], default="sketch", help="gradio inpainting editor: can be either sketch to only blur/noise the input, or color-sketch to paint over it") | |
| parser.add_argument("--opt-channelslast", action='store_true', help="change memory type for stable diffusion to channels last") | |
| parser.add_argument("--styles-file", type=str, help="filename to use for styles", default=os.path.join(script_path, 'styles.csv')) | |
| parser.add_argument("--autolaunch", action='store_true', help="open the webui URL in the system's default browser upon launch", default=False) | |
| parser.add_argument("--theme", type=str, help="launches the UI with light or dark theme", default=None) | |
| parser.add_argument("--use-textbox-seed", action='store_true', help="use textbox for seeds in UI (no up/down, but possible to input long seeds)", default=False) | |
| parser.add_argument("--disable-console-progressbars", action='store_true', help="do not output progressbars to console", default=False) | |
| parser.add_argument("--enable-console-prompts", action='store_true', help="print prompts to console when generating with txt2img and img2img", default=False) | |
| parser.add_argument('--vae-path', type=str, help='Path to Variational Autoencoders model', default=None) | |
| parser.add_argument("--disable-safe-unpickle", action='store_true', help="disable checking pytorch models for malicious code", default=False) | |
| parser.add_argument("--api", action='store_true', help="use api=True to launch the API together with the webui (use --nowebui instead for only the API)") | |
| parser.add_argument("--api-auth", type=str, help='Set authentication for API like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None) | |
| parser.add_argument("--nowebui", action='store_true', help="use api=True to launch the API instead of the webui") | |
| parser.add_argument("--ui-debug-mode", action='store_true', help="Don't load model to quickly launch UI") | |
| parser.add_argument("--device-id", type=str, help="Select the default CUDA device to use (export CUDA_VISIBLE_DEVICES=0,1,etc might be needed before)", default=None) | |
| parser.add_argument("--administrator", action='store_true', help="Administrator rights", default=False) | |
| parser.add_argument("--cors-allow-origins", type=str, help="Allowed CORS origin(s) in the form of a comma-separated list (no spaces)", default=None) | |
| parser.add_argument("--cors-allow-origins-regex", type=str, help="Allowed CORS origin(s) in the form of a single regular expression", default=None) | |
| parser.add_argument("--tls-keyfile", type=str, help="Partially enables TLS, requires --tls-certfile to fully function", default=None) | |
| parser.add_argument("--tls-certfile", type=str, help="Partially enables TLS, requires --tls-keyfile to fully function", default=None) | |
| parser.add_argument("--server-name", type=str, help="Sets hostname of server", default=None) | |
| script_loading.preload_extensions(extensions.extensions_dir, parser) | |
| script_loading.preload_extensions(extensions.extensions_builtin_dir, parser) | |
| cmd_opts = parser.parse_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", | |
| } | |
| 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']) | |
| 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_hypernetwork = None | |
| def reload_hypernetworks(): | |
| from modules.hypernetworks import hypernetwork | |
| global hypernetworks | |
| hypernetworks = hypernetwork.list_hypernetworks(cmd_opts.hypernetwork_dir) | |
| hypernetwork.load_hypernetwork(opts.sd_hypernetwork) | |
| class State: | |
| skipped = False | |
| interrupted = False | |
| job = "" | |
| job_no = 0 | |
| job_count = 0 | |
| job_timestamp = '0' | |
| sampling_step = 0 | |
| sampling_steps = 0 | |
| current_latent = None | |
| current_image = None | |
| current_image_sampling_step = 0 | |
| textinfo = None | |
| time_start = None | |
| need_restart = False | |
| def skip(self): | |
| self.skipped = True | |
| def interrupt(self): | |
| self.interrupted = True | |
| def nextjob(self): | |
| if 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_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.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.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() | |
| """sets self.current_image from self.current_latent if enough sampling steps have been made after the last call to this""" | |
| def set_current_image(self): | |
| if self.sampling_step - self.current_image_sampling_step >= opts.show_progress_every_n_steps and opts.show_progress_every_n_steps > 0: | |
| self.do_set_current_image() | |
| def do_set_current_image(self): | |
| if not parallel_processing_allowed: | |
| return | |
| if self.current_latent is None: | |
| return | |
| import modules.sd_samplers | |
| if opts.show_progress_grid: | |
| self.current_image = modules.sd_samplers.samples_to_image_grid(self.current_latent) | |
| else: | |
| self.current_image = modules.sd_samplers.sample_to_image(self.current_latent) | |
| self.current_image_sampling_step = self.sampling_step | |
| state = State() | |
| artist_db = modules.artists.ArtistsDatabase(os.path.join(script_path, 'artists.csv')) | |
| styles_filename = cmd_opts.styles_file | |
| prompt_styles = modules.styles.StyleDatabase(styles_filename) | |
| interrogator = modules.interrogate.InterrogateModels("interrogate") | |
| face_restorers = [] | |
| def realesrgan_models_names(): | |
| import modules.realesrgan_model | |
| return [x.name for x in modules.realesrgan_model.get_realesrgan_models(None)] | |
| 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 | |
| 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} | |
| 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), | |
| "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)"), | |
| "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(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"), | |
| "jpeg_quality": OptionInfo(80, "Quality for saved jpeg images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}), | |
| "export_for_4chan": OptionInfo(True, "If PNG image is larger than 4MB or any dimension is larger than 4000, downscale and save copy as JPG"), | |
| "use_original_name_batch": OptionInfo(False, "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"), | |
| "do_not_add_watermark": OptionInfo(False, "Do not add watermark to images"), | |
| "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), | |
| })) | |
| options_templates.update(options_section(('saving-to-dirs', "Saving to a directory"), { | |
| "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("", "Directory name pattern", component_args=hide_dirs), | |
| "directories_max_prompt_words": OptionInfo(8, "Max prompt words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 20, "step": 1, **hide_dirs}), | |
| })) | |
| options_templates.update(options_section(('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": realesrgan_models_names()}), | |
| "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}), | |
| "use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space image when doing hires. fix"), | |
| })) | |
| options_templates.update(options_section(('face-restoration', "Face restoration"), { | |
| "face_restoration_model": OptionInfo(None, "Face restoration model", gr.Radio, lambda: {"choices": [x.name() for x in face_restorers]}), | |
| "code_former_weight": OptionInfo(0.5, "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(8, "VRAM usage polls per second during generation. Set to 0 to disable.", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}), | |
| "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."), | |
| })) | |
| 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 can be resumed with HN itself and matching optim file."), | |
| "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"), | |
| })) | |
| 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("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list), | |
| "sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"), | |
| "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks), | |
| "sd_hypernetwork_strength": OptionInfo(1.0, "Hypernetwork strength", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.001}), | |
| "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.", gr.ColorPicker, {}), | |
| "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, "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}), | |
| "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}), | |
| })) | |
| 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)."), | |
| })) | |
| options_templates.update(options_section(('interrogate', "Interrogate Options"), { | |
| "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"), | |
| "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"), | |
| "interrogate_return_ranks": OptionInfo(False, "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(24, "Interrogate: minimum description length (excluding artists, etc..)", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1}), | |
| "interrogate_clip_max_length": OptionInfo(48, "Interrogate: 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 (0 = No limit)"), | |
| "interrogate_deepbooru_score_threshold": OptionInfo(0.5, "Interrogate: deepbooru score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}), | |
| "deepbooru_sort_alpha": OptionInfo(True, "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(('ui', "User interface"), { | |
| "show_progressbar": OptionInfo(True, "Show progressbar"), | |
| "show_progress_every_n_steps": OptionInfo(0, "Show image creation progress every N sampling steps. Set to 0 to disable. 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"]}), | |
| "show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"), | |
| "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"), | |
| "add_model_hash_to_info": OptionInfo(True, "Add model hash to generation information"), | |
| "add_model_name_to_info": OptionInfo(False, "Add model name to generation information"), | |
| "disable_weights_auto_swap": OptionInfo(False, "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(True, "Show generation progress in window title."), | |
| "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group"), | |
| 'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"), | |
| 'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)), | |
| })) | |
| options_templates.update(options_section(('sampler-params', "Sampler parameters"), { | |
| "hide_samplers": OptionInfo([], "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}), | |
| })) | |
| options_templates.update(options_section((None, "Hidden options"), { | |
| "disabled_extensions": OptionInfo([], "Disable those extensions"), | |
| })) | |
| 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: | |
| self.data_labels[key].onchange() | |
| return True | |
| 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): | |
| 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, 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])} | |
| opts = Options() | |
| if os.path.exists(config_filename): | |
| opts.load(config_filename) | |
| sd_upscalers = [] | |
| sd_model = None | |
| clip_model = None | |
| progress_print_out = sys.stdout | |
| 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.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)) if not x.startswith(".")] | |
| return [file for file in filenames if os.path.isfile(file)] | |