AI/sdnext
1
0
Fork 0
mirror of https://github.com/vladmandic/sdnext.git synced 2026-01-29 05:02:09 +03:00
Code Activity
Files
8091ef2fca9bac0271ce72fb5fb19bb0f7df945e
sdnext/modules/shared.py
Vladimir Mandic 5c516332d7 bug fixes
2023-05-23 18:48:37 -04:00

781 lines
44 KiB
Python
Raw Blame History

import os
import sys
import time
import json
import datetime
import urllib.request
import gradio as gr
import tqdm
import requests
# from ldm.models.diffusion.ddpm import LatentDiffusion
from modules import errors, ui_components, shared_items, cmd_args
from modules.paths_internal import models_path, script_path, data_path, sd_configs_path, sd_default_config, sd_model_file, default_sd_model_file, extensions_dir, extensions_builtin_dir # pylint: disable=W0611
import modules.interrogate
import modules.memmon
import modules.styles
import modules.devices as devices
import modules.paths_internal as paths
from installer import log as central_logger # pylint: disable=E0611
errors.install(gr)
demo: gr.Blocks = None
log = central_logger
progress_print_out = sys.stdout
parser = cmd_args.parser
url = 'https://github.com/vladmandic/automatic'
cmd_opts, _ = parser.parse_known_args()
hide_dirs = {"visible": not cmd_opts.hide_ui_dir_config}
is_device_dml = False
xformers_available = False
clip_model = None
interrogator = modules.interrogate.InterrogateModels("interrogate")
sd_upscalers = []
face_restorers = []
tab_names = []
options_templates = {}
hypernetworks = {}
loaded_hypernetworks = []
gradio_theme = gr.themes.Base()
settings_components = None
latent_upscale_default_mode = "Latent"
latent_upscale_modes = {
"Latent": {"mode": "bilinear", "antialias": False},
"Latent (antialiased)": {"mode": "bilinear", "antialias": True},
"Latent (bicubic)": {"mode": "bicubic", "antialias": False},
"Latent (bicubic antialiased)": {"mode": "bicubic", "antialias": True},
"Latent (nearest)": {"mode": "nearest", "antialias": False},
"Latent (nearest-exact)": {"mode": "nearest-exact", "antialias": False},
}
restricted_opts = {
"samples_filename_pattern",
"directories_filename_pattern",
"outdir_samples",
"outdir_txt2img_samples",
"outdir_img2img_samples",
"outdir_extras_samples",
"outdir_grids",
"outdir_txt2img_grids",
"outdir_save",
"outdir_init_images"
}
ui_reorder_categories = [
"inpaint",
"sampler",
"checkboxes",
"hires_fix",
"dimensions",
"cfg",
"seed",
"batch",
"override_settings",
"scripts",
]
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):
log.debug('Skip requested')
self.skipped = True
def interrupt(self):
log.debug('Interrupt requested')
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
import modules.sd_samplers # pylint: disable=W0621
if opts.show_progress_grid:
self.assign_current_image(modules.sd_samplers.samples_to_image_grid(self.current_latent))
else:
self.assign_current_image(modules.sd_samplers.sample_to_image(self.current_latent))
self.current_image_sampling_step = self.sampling_step
def assign_current_image(self, image):
self.current_image = image
self.id_live_preview += 1
state = State()
state.server_start = time.time()
class OptionInfo:
def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None):
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()
default_checkpoint = list_checkpoint_tiles()[0] if len(list_checkpoint_tiles()) > 0 else "model.ckpt"
def refresh_checkpoints():
import modules.sd_models # pylint: disable=W0621
return modules.sd_models.list_models()
def list_samplers():
import modules.sd_samplers # pylint: disable=W0621
modules.sd_samplers.set_samplers()
return modules.sd_samplers.all_samplers
def list_themes():
if not os.path.exists(os.path.join('javascript', 'themes.json')):
refresh_themes()
if os.path.exists(os.path.join('javascript', 'themes.json')):
with open(os.path.join('javascript', 'themes.json'), mode='r', encoding='utf=8') as f:
res = json.loads(f.read())
else:
res = []
builtin = ["black-orange", "gradio/default", "gradio/base", "gradio/glass", "gradio/monochrome", "gradio/soft"]
themes = sorted(set(builtin + [x['id'] for x in res if x['status'] == 'RUNNING' and 'test' not in x['id'].lower()]), key=str.casefold)
return themes
def refresh_themes():
try:
req = requests.get('https://huggingface.co/datasets/freddyaboulton/gradio-theme-subdomains/resolve/main/subdomains.json', timeout=5)
if req.status_code == 200:
res = req.json()
with open(os.path.join('javascript', 'themes.json'), mode='w', encoding='utf=8') as f:
f.write(json.dumps(res))
else:
log.error('Error refreshing UI themes')
except:
log.error('Exception refreshing UI themes')
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, "Model 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", "Select VAE", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
"stream_load": OptionInfo(False, "When loading models attempt stream loading optimized for slow or network storage"),
"model_reuse_dict": OptionInfo(False, "When loading models attempt to reuse previous model dictionary"),
"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": 0.1, "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, "For image processing do exactly the amount of steps as specified"),
"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"),
"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": 8, "step": 1, "visible": False}),
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to FP32"),
"cross_attention_optimization": OptionInfo("Sub-quadratic" if is_device_dml else "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']}),
"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"),
"prompt_attention": OptionInfo("Full parser", "Prompt attention parser", gr.Radio, lambda: {"choices": ["Full parser", "Compel parser", "A1111 parser", "Fixed attention"] }),
"prompt_mean_norm": OptionInfo(True, "Prompt attention mean normalization"),
}))
options_templates.update(options_section(('system-paths', "System Paths"), {
"temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default"),
"clean_temp_dir_at_start": OptionInfo(True, "Cleanup non-default temporary directory when starting webui"),
"ckpt_dir": OptionInfo(os.path.join(paths.models_path, 'Stable-diffusion'), "Path to directory with stable diffusion checkpoints"),
"vae_dir": OptionInfo(os.path.join(paths.models_path, 'VAE'), "Path to directory with VAE files"),
"embeddings_dir": OptionInfo(os.path.join(paths.models_path, 'embeddings'), "Embeddings directory for textual inversion"),
"hypernetwork_dir": OptionInfo(os.path.join(paths.models_path, 'hypernetworks'), "Hypernetwork directory"),
"codeformer_models_path": OptionInfo(os.path.join(paths.models_path, 'Codeformer'), "Path to directory with codeformer model file(s)"),
"gfpgan_models_path": OptionInfo(os.path.join(paths.models_path, 'GFPGAN'), "Path to directory with GFPGAN model file(s)"),
"esrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'ESRGAN'), "Path to directory with ESRGAN model file(s)"),
"bsrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'BSRGAN'), "Path to directory with BSRGAN model file(s)"),
"realesrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'RealESRGAN'), "Path to directory with RealESRGAN model file(s)"),
"scunet_models_path": OptionInfo(os.path.join(paths.models_path, 'ScuNET'), "Path to directory with ScuNET model file(s)"),
"swinir_models_path": OptionInfo(os.path.join(paths.models_path, 'SwinIR'), "Path to directory with SwinIR model file(s)"),
"ldsr_models_path": OptionInfo(os.path.join(paths.models_path, 'LDSR'), "Path to directory with LDSR model file(s)"),
"clip_models_path": OptionInfo(os.path.join(paths.models_path, 'CLIP'), "Path to directory with CLIP model file(s)"),
"lora_dir": OptionInfo(os.path.join(paths.models_path, 'Lora'), "Path to directory with Lora network(s)"),
"lyco_dir": OptionInfo(os.path.join(paths.models_path, 'LyCORIS'), "Path to directory with LyCORIS network(s)"),
"styles_dir": OptionInfo(os.path.join(paths.data_path, 'styles.csv'), "Path to user-defined styles file"),
# "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("[seed]-[prompt_spaces]", "Images filename pattern", component_args=hide_dirs),
"save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
"grid_save": OptionInfo(True, "Always save all generated image grids"),
"grid_format": OptionInfo('jpg', 'File format for grids'),
"grid_extended_filename": OptionInfo(True, "Add extended info (seed, prompt) to filename when saving grid"),
"grid_only_if_multiple": OptionInfo(True, "Do not save grids consisting of one picture"),
"grid_prevent_empty_spots": OptionInfo(True, "Prevent empty spots in grid (when set to autodetect)"),
"n_rows": OptionInfo(-1, "Grid row count; use -1 for autodetect and 0 for it to be same as batch size", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
"save_txt": OptionInfo(False, "Create a text file next to every image with generation parameters"),
"save_log_fn": OptionInfo("", "Create a log file with image information for each saved image", component_args=hide_dirs),
"save_images_before_face_restoration": OptionInfo(False, "Save a copy of image before doing face restoration"),
"save_images_before_highres_fix": OptionInfo(False, "Save a copy of image before applying highres fix"),
"save_images_before_color_correction": OptionInfo(False, "Save a copy of image before applying color correction to img2img results"),
"save_mask": OptionInfo(False, "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_init_img": OptionInfo(False, "Save init images when using image processing"),
"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}),
}))
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),
"outdir_init_images": OptionInfo("outputs/init-images", "Directory for saving init images when using img2img", component_args=hide_dirs),
}))
options_templates.update(options_section(('cuda', "Compute Settings"), {
"memmon_poll_rate": OptionInfo(2, "VRAM usage polls per second during generation", gr.Slider, {"minimum": 0, "maximum": 40, "step": 1}),
"precision": OptionInfo("Autocast", "Precision type", gr.Radio, lambda: {"choices": ["Autocast", "Full"]}),
"cuda_dtype": OptionInfo("FP32" if sys.platform == "darwin" else "FP16", "Device precision type", gr.Radio, lambda: {"choices": ["FP32", "FP16", "BF16"]}),
"no_half": OptionInfo(True if is_device_dml else False, "Use full precision for model (--no-half)", None, None, None),
"no_half_vae": OptionInfo(True if is_device_dml else False, "Use full precision for VAE (--no-half-vae)"),
"upcast_sampling": OptionInfo(True if sys.platform == "darwin" or cmd_opts.use_ipex else False, "Enable upcast sampling. Usually produces similar results to --no-half with better performance while using less memory"),
"disable_nan_check": OptionInfo(True, "Do not check if produced images/latent spaces have NaN values"),
"rollback_vae": OptionInfo(False, "Attempt to roll back VAE when produced NaN values, requires NaN check (experimental)"),
"opt_channelslast": OptionInfo(False, "Use channels last as torch memory format "),
"cudnn_benchmark": OptionInfo(False, "Enable cuDNN benchmark feature"),
"cuda_allow_tf32": OptionInfo(True, "Allow TF32 math ops"),
"cuda_allow_tf16_reduced": OptionInfo(True, "Allow TF16 reduced precision math ops"),
"cuda_compile": OptionInfo(False, "Enable model compile (experimental)"),
"cuda_compile_mode": OptionInfo("none", "Model compile mode (experimental)", gr.Radio, lambda: {"choices": ['none', 'inductor', 'cudagraphs', 'aot_ts_nvfuser', 'hidet', 'ipex']}),
"cuda_compile_verbose": OptionInfo(False, "Model compile verbose mode"),
"cuda_compile_errors": OptionInfo(True, "Model compile suppress errors"),
"disable_gc": OptionInfo(False, "Disable Torch memory garbage collection (experimental)"),
}))
options_templates.update(options_section(('upscaling', "Upscaling"), {
"upscaler_for_img2img": OptionInfo("None", "Default upscaler for image resize operations", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Real-ESRGAN available models", gr.CheckboxGroup, lambda: {"choices": shared_items.realesrgan_models_names()}),
"ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers (0 = no tiling)", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap in pixels for ESRGAN upscalers", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
"SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers. 0 = no tiling.", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
"SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
"use_old_hires_fix_width_height": OptionInfo(False, "Hires fix uses width & height to set final resolution rather than first pass"),
"dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers"),
}))
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(('training', "Training"), {
"unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible"),
"pin_memory": OptionInfo(True, "Pin training dataset to memory"),
"save_optimizer_state": OptionInfo(False, "Saves resumable optimizer state when training embedding or hypernetwork"),
"save_training_settings_to_txt": OptionInfo(True, "Save textual inversion and hypernet settings to a text file whenever training starts"),
"dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
"dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
"embeddings_templates_dir": OptionInfo(os.path.join(paths.script_path, 'train', 'templates'), "Embeddings train templates directory"),
"training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),
"training_write_csv_every": OptionInfo(0, "Save an csv containing the loss to log directory every N steps, 0 to disable"),
"training_enable_tensorboard": OptionInfo(False, "Enable tensorboard logging"),
"training_tensorboard_save_images": OptionInfo(False, "Save generated images within tensorboard"),
"training_tensorboard_flush_every": OptionInfo(120, "How often, in seconds, to flush the pending tensorboard events and summaries to disk"),
}))
options_templates.update(options_section(('interrogate', "Interrogate 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"),
"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": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
}))
options_templates.update(options_section(('ui', "User interface"), {
"gradio_theme": OptionInfo("black-orange", "UI theme", gr.Dropdown, lambda: {"choices": list_themes()}, refresh=refresh_themes),
"theme_style": OptionInfo("Auto", "Theme mode", gr.Radio, {"choices": ["Auto", "Dark", "Light"]}),
"return_grid": OptionInfo(True, "Show grid in results for web"),
"return_mask": OptionInfo(False, "For inpainting, include the greyscale mask in results for web"),
"return_mask_composite": OptionInfo(False, "For inpainting, include masked composite in results for web"),
"disable_weights_auto_swap": OptionInfo(True, "Do not change the selected model when reading generation parameters"),
"send_seed": OptionInfo(True, "Send seed when sending prompt or image to other interface"),
"send_size": OptionInfo(True, "Send size when sending prompt or image to another interface"),
"font": OptionInfo("", "Font for image grids that have text"),
"keyedit_precision_attention": OptionInfo(0.1, "Ctrl+up/down precision when editing (attention:1.1)", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
"keyedit_delimiters": OptionInfo(".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"), # pylint: disable=anomalous-backslash-in-string
"quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}),
"hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
"ui_tab_reorder": OptionInfo("From Text, From Image, Process Image", "UI tabs order"),
"ui_scripts_reorder": OptionInfo("Enable Dynamic Thresholding, ControlNet", "UI scripts order"),
"ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
"ui_extra_networks_tab_reorder": OptionInfo("", "Extra networks tab order"),
}))
options_templates.update(options_section(('ui', "Live previews"), {
"show_progressbar": OptionInfo(True, "Show progressbar"),
"live_previews_enable": OptionInfo(True, "Show live previews of the created image"),
"show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid"),
"notification_audio_enable": OptionInfo(False, "Play a sound when images are finished generating"),
"notification_audio_path": OptionInfo("html/notification.mp3","Path to notification sound",component_args=hide_dirs),
"show_progress_every_n_steps": OptionInfo(1, "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("Approx NN", "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(250, "Progressbar/preview update period, in milliseconds")
}))
options_templates.update(options_section(('sampler-params', "Sampler parameters"), {
"show_samplers": OptionInfo(["Euler a", "UniPC", "DDIM", "DPM++ SDE", "DPM++ SDE", "DPM2 Karras", "DPM++ 2M Karras"], "Show samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers() if x.name != "PLMS"]}),
"fallback_sampler": OptionInfo("Euler a", "Secondary sampler", gr.Dropdown, lambda: {"choices": ["None"] + [x.name for x in list_samplers()]}),
"xyz_fallback_sampler": OptionInfo("None", "Force latent upscaler sampler", gr.Dropdown, lambda: {"choices": ["None"] + [x.name for x in list_samplers()]}),
"eta_ancestral": OptionInfo(1.0, "Noise multiplier for ancestral samplers (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"eta_ddim": OptionInfo(0.0, "Noise multiplier for DDIM (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"ddim_discretize": OptionInfo('uniform', "DDIM discretize img2img", gr.Radio, {"choices": ['uniform', 'quad']}),
's_churn': OptionInfo(0.0, "sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_min_uncond': OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}),
's_tmin': OptionInfo(0.0, "sigma tmin", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_noise': OptionInfo(1.0, "sigma noise", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
'eta_noise_seed_delta': OptionInfo(0, "Noise seed delta (eta)", gr.Number, {"precision": 0}),
'always_discard_next_to_last_sigma': OptionInfo(False, "Always discard next-to-last sigma"),
'uni_pc_variant': OptionInfo("bh1", "UniPC variant", gr.Radio, {"choices": ["bh1", "bh2", "vary_coeff"]}),
'uni_pc_skip_type': OptionInfo("time_uniform", "UniPC skip type", gr.Radio, {"choices": ["time_uniform", "time_quadratic", "logSNR"]}),
'uni_pc_order': OptionInfo(3, "UniPC order (must be < sampling steps)", gr.Slider, {"minimum": 1, "maximum": 50, "step": 1}),
'uni_pc_lower_order_final': OptionInfo(True, "UniPC lower order final"),
}))
options_templates.update(options_section(('token_merging', 'Token Merging'), {
"token_merging": OptionInfo(False, "Enable redundant token merging via tomesd for speed and memory improvements", gr.Checkbox),
"token_merging_ratio": OptionInfo(0.5, "Token merging Ratio. Higher merging ratio = faster generation, smaller VRAM usage, lower quality.", gr.Slider, {"minimum": 0, "maximum": 0.9, "step": 0.1}),
"token_merging_hr_only": OptionInfo(True, "Apply only to high-res fix pass. Disabling can yield a ~20-35% speedup on contemporary resolutions.", gr.Checkbox),
"token_merging_ratio_hr": OptionInfo(0.5, "Merging Ratio (high-res pass) - If 'Apply only to high-res' is enabled, this will always be the ratio used.", gr.Slider, {"minimum": 0, "maximum": 0.9, "step": 0.1}),
"token_merging_random": OptionInfo(False, "Use random perturbations - Can improve outputs for certain samplers. For others, it may cause visual artifacting.", gr.Checkbox),
"token_merging_merge_attention": OptionInfo(True, "Merge attention (Recommend on)", gr.Checkbox),
"token_merging_merge_cross_attention": OptionInfo(False, "Merge cross attention (Recommend off)", gr.Checkbox),
"token_merging_merge_mlp": OptionInfo(False, "Merge mlp (Strongly recommend off)", gr.Checkbox),
"token_merging_maximum_down_sampling": OptionInfo(1, "Maximum down sampling", gr.Radio, lambda: {"choices": [1, 2, 4, 8]}),
"token_merging_stride_x": OptionInfo(2, "Stride - X", gr.Slider, {"minimum": 2, "maximum": 8, "step": 2}),
"token_merging_stride_y": OptionInfo(2, "Stride - Y", gr.Slider, {"minimum": 2, "maximum": 8, "step": 2})
}))
options_templates.update(options_section(('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", "user", "all"]}),
"sd_checkpoint_hash": OptionInfo("", "SHA256 hash of the current checkpoint"),
}))
options_templates.update()
class Options:
data = None
data_labels = options_templates
typemap = {int: float}
def __init__(self):
self.data = {k: v.default for k, v in self.data_labels.items()}
def __setattr__(self, key, value):
if self.data is not None:
if key in self.data or key in self.data_labels:
if cmd_opts.freeze:
log.warning(f'Settings are frozen: {key}')
return
if cmd_opts.hide_ui_dir_config and key in restricted_opts:
log.warning(f'Settings key is restricted: {key}')
return
else:
self.data[key] = value
return
return super(Options, self).__setattr__(key, value)
def __getattr__(self, item):
if self.data is not None:
if item in self.data:
return self.data[item]
if item in self.data_labels:
return self.data_labels[item].default
return super(Options, self).__getattribute__(item)
def set(self, key, value):
"""sets an option and calls its onchange callback, returning True if the option changed and False otherwise"""
oldval = self.data.get(key, None)
if oldval == value:
return False
try:
setattr(self, key, value)
except RuntimeError:
return False
if self.data_labels[key].onchange is not None:
try:
self.data_labels[key].onchange()
except Exception as e:
errors.display(e, f"changing setting {key} to {value}")
setattr(self, key, oldval)
return False
return True
def get_default(self, key):
"""returns the default value for the key"""
data_label = self.data_labels.get(key)
if data_label is None:
return None
return data_label.default
def save(self, filename):
if cmd_opts.freeze:
log.warning(f'Settings saving is disabled: {filename}')
return
with open(filename, "w", encoding="utf8") as file:
json.dump(self.data, file, indent=4)
def same_type(self, x, y):
if x is None or y is None:
return True
type_x = self.typemap.get(type(x), type(x))
type_y = self.typemap.get(type(y), type(y))
return type_x == type_y
def load(self, filename):
if not os.path.isfile(filename):
log.debug(f'Created default config: {filename}')
self.save(filename)
return
with open(filename, "r", encoding="utf8") as file:
self.data = json.load(file)
if self.data.get('quicksettings') is not None and self.data.get('quicksettings_list') is None:
self.data['quicksettings_list'] = [i.strip() for i in self.data.get('quicksettings').split(',')]
bad_settings = 0
for k, v in self.data.items():
info = self.data_labels.get(k, None)
if info is not None and not self.same_type(info.default, v):
log.error(f"Warning: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})")
bad_settings += 1
if bad_settings > 0:
log.error(f"Error: Bad settings found in {filename}")
def onchange(self, key, func, call=True):
item = self.data_labels.get(key)
item.onchange = func
if call:
func()
def dumpjson(self):
d = {k: self.data.get(k, self.data_labels.get(k).default) for k in self.data_labels.keys()}
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
elif expected_type == type(value):
pass
else:
value = expected_type(value)
return value
opts = Options()
config_filename = cmd_opts.config
opts.load(config_filename)
cmd_opts = cmd_args.compatibility_args(opts, cmd_opts)
prompt_styles = modules.styles.StyleDatabase(opts.styles_dir)
cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or (cmd_opts.server_name or False)) and not cmd_opts.insecure
devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = (devices.cpu if any(y in cmd_opts.use_cpu for y in [x, 'all']) else devices.get_optimal_device() for x in ['sd', 'interrogate', 'gfpgan', 'esrgan', 'codeformer'])
device = devices.device
batch_cond_uncond = opts.always_batch_cond_uncond or not (cmd_opts.lowvram or cmd_opts.medvram)
parallel_processing_allowed = not cmd_opts.lowvram and not cmd_opts.medvram
mem_mon = modules.memmon.MemUsageMonitor("MemMon", device, opts)
mem_mon.start()
if device.type == 'privateuseone':
import modules.dml # pylint: disable=ungrouped-imports
is_device_dml = True
def reload_gradio_theme(theme_name=None):
global gradio_theme # pylint: disable=global-statement
if not theme_name:
theme_name = opts.gradio_theme
default_font_params = {}
res = 0
try:
req = urllib.request.Request("https://fonts.googleapis.com/css2?family=IBM+Plex+Mono", method="HEAD")
res = urllib.request.urlopen(req, timeout=3.0).status
except:
res = 0
if res != 200:
log.info('No internet access detected, using default fonts')
default_font_params = {
'font':['Helvetica', 'ui-sans-serif', 'system-ui', 'sans-serif'],
'font_mono':['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace']
}
if theme_name == "black-orange":
gradio_theme = gr.themes.Default(**default_font_params)
elif theme_name.startswith("gradio/"):
if theme_name == "gradio/default":
gradio_theme = gr.themes.Default(**default_font_params)
if theme_name == "gradio/base":
gradio_theme = gr.themes.Base(**default_font_params)
if theme_name == "gradio/glass":
gradio_theme = gr.themes.Glass(**default_font_params)
if theme_name == "gradio/monochrome":
gradio_theme = gr.themes.Monochrome(**default_font_params)
if theme_name == "gradio/soft":
gradio_theme = gr.themes.Soft(**default_font_params)
else:
try:
gradio_theme = gr.themes.ThemeClass.from_hub(theme_name)
except:
log.error("Theme download error accessing HuggingFace")
gradio_theme = gr.themes.Default(**default_font_params)
log.info(f'Loading UI theme: name={theme_name} style={opts.theme_style}')
class TotalTQDM:
def __init__(self):
self._tqdm = None
def reset(self):
self._tqdm = tqdm.tqdm(
desc="Total",
total=state.job_count * state.sampling_steps,
position=1,
)
def update(self):
if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
return
if self._tqdm is None:
self.reset()
self._tqdm.update()
def updateTotal(self, new_total):
if not opts.multiple_tqdm or cmd_opts.disable_console_progressbars:
return
if self._tqdm is None:
self.reset()
self._tqdm.total = new_total
def clear(self):
if self._tqdm is not None:
self._tqdm.refresh()
self._tqdm.close()
self._tqdm = None
total_tqdm = TotalTQDM()
def restart_server(restart=True):
if demo is None:
return
log.info('Server shutdown requested')
try:
demo.server.wants_restart = restart
demo.server.should_exit = True
demo.server.force_exit = True
demo.close(verbose=False)
demo.server.close()
demo.fns = []
except:
pass
if restart:
log.info('Server will restart')
def restore_defaults(restart=True):
if os.path.exists(cmd_opts.config):
log.info('Restoring server defaults')
os.remove(cmd_opts.config)
if os.path.exists(cmd_opts.ui_config):
log.info('Restoring UI defaults')
os.remove(cmd_opts.ui_config)
restart_server(restart)
def listfiles(dirname):
filenames = [os.path.join(dirname, x) for x in sorted(os.listdir(dirname), key=str.lower) if not x.startswith(".")]
return [file for file in filenames if os.path.isfile(file)]
def walk_files(path, allowed_extensions=None):
if not os.path.exists(path):
return
if allowed_extensions is not None:
allowed_extensions = set(allowed_extensions)
for root, _dirs, files in os.walk(path, followlinks=True):
for filename in files:
if allowed_extensions is not None:
_, ext = os.path.splitext(filename)
if ext not in allowed_extensions:
continue
yield os.path.join(root, filename)
def html_path(filename):
return os.path.join(paths.script_path, "html", filename)
def html(filename):
path = html_path(filename)
if os.path.exists(path):
with open(path, encoding="utf8") as file:
return file.read()
return ""
class Shared(sys.modules[__name__].__class__):
# this class is here to provide sd_model field as a property, so that it can be created and loaded on demand rather than at program startup.
sd_model_val = None
@property
def sd_model(self):
import modules.sd_models # pylint: disable=W0621
# return modules.sd_models.model_data.sd_model
return modules.sd_models.model_data.get_sd_model()
@sd_model.setter
def sd_model(self, value):
import modules.sd_models # pylint: disable=W0621
modules.sd_models.model_data.set_sd_model(value)
# sd_model: LatentDiffusion = None # this var is here just for IDE's type checking; it cannot be accessed because the class field above will be accessed instead
sd_model = None
sys.modules[__name__].__class__ = Shared
View Git Blame Copy Permalink