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
89ffca0c8848255eddeb6246ac3f30cccfb4e5cc
sdnext/modules/shared.py

884 lines
48 KiB
Python
Raw Blame History

import os
import sys
import time
import json
import datetime
import urllib.request
from enum import Enum
import gradio as gr
import tqdm
import requests
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}
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",
]
class Backend(Enum):
ORIGINAL = 1
DIFFUSERS = 2
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
paused = 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
oom = False
def skip(self):
log.debug('Requested skip')
self.skipped = True
def interrupt(self):
log.debug('Requested interrupt')
self.interrupted = True
def pause(self):
self.paused = not self.paused
log.debug(f'Requested {"pause" if self.paused else "continue"}')
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.paused = False
self.textinfo = None
self.time_start = time.time()
devices.torch_gc()
def end(self):
self.job = ""
self.job_count = 0
self.paused = False
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, comment_before='', comment_after=''):
self.default = default
self.label = label
self.component = component
self.component_args = component_args
self.onchange = onchange
self.section = section
self.refresh = refresh
self.comment_before = comment_before # HTML text that will be added after label in UI
self.comment_after = comment_after # HTML text that will be added before label in UI
def link(self, label, uri):
self.comment_before += f"[<a href='{uri}' target='_blank'>{label}</a>]"
return self
def js(self, label, js_func):
self.comment_before += f"[<a onclick='{js_func}(); return false'>{label}</a>]"
return self
def info(self, info):
self.comment_after += f"<span class='info'>({info})</span>"
return self
def needs_restart(self):
self.comment_after += " <span class='info'>(requires restart)</span>"
return self
def options_section(section_identifier, options_dict):
for v in options_dict.values():
v.section = section_identifier
return options_dict
def list_checkpoint_tiles():
import modules.sd_models # 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():
fn = os.path.join('html', 'themes.json')
if not os.path.exists(fn):
refresh_themes()
if os.path.exists(fn):
with open(fn, 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 lora_disable():
if opts.lora_disable:
if 'Lora' not in opts.disabled_extensions:
opts.data['disabled_extensions'].append('Lora')
else:
opts.data['disabled_extensions'] = [x for x in opts.disabled_extensions if x != 'Lora']
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()
fn = os.path.join('html', 'themes.json')
with open(fn, 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')
if devices.backend == "cpu":
cross_attention_optimization_default = "Doggettx's"
elif devices.backend == "mps":
cross_attention_optimization_default = "Doggettx's"
elif devices.backend == "ipex":
cross_attention_optimization_default = "InvokeAI's"
elif devices.backend == "directml":
cross_attention_optimization_default = "Sub-quadratic"
elif devices.backend == "rocm":
cross_attention_optimization_default = "Sub-quadratic"
else: # cuda
cross_attention_optimization_default ="Scaled-Dot-Product"
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, "Number of cached model checkpoints", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae_checkpoint_cache": OptionInfo(0, "Number of cached VAE checkpoints", 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),
"sd_model_dict": OptionInfo('None', "Stable Diffusion checkpoint dict", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_tiles()}, refresh=refresh_checkpoints),
"sd_vae_sliced_encode": OptionInfo(False, "Enable splitting of hires batch processing"),
"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"),
"cross_attention_optimization": OptionInfo(cross_attention_optimization_default, "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", gr.Slider, {"minimum": 16, "maximum": 8192, "step": 8}),
"sub_quad_kv_chunk_size": OptionInfo(512, "Sub-quadratic cross-attention kv chunk size", gr.Slider, {"minimum": 0, "maximum": 8192, "step": 8}),
"sub_quad_chunk_threshold": OptionInfo(80, "Sub-quadratic cross-attention chunking threshold", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}),
"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"),
"always_batch_cond_uncond": OptionInfo(False, "Disable conditional batching enabled on low memory systems"),
"enable_quantization": OptionInfo(True, "Enable samplers quantization 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 }),
"sd_backend": OptionInfo("Original", "Stable Diffusion backend (experimental)", gr.Radio, lambda: {"choices": ["Original", "Diffusers"] }),
}))
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(False, "Use full precision for model (--no-half)", None, None, None),
"no_half_vae": OptionInfo(False, "Use full precision for VAE (--no-half-vae)"),
"upcast_sampling": OptionInfo(True if sys.platform == "darwin" else False, "Enable upcast sampling"),
"upcast_attn": OptionInfo(False, "Enable upcast cross attention layer"),
"disable_nan_check": OptionInfo(True, "Disable NaN check in produced images/latent spaces"),
"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 full-depth 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(('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"),
"diffusers_dir": OptionInfo(os.path.join(paths.models_path, 'Diffusers'), "Path to directory with stable diffusion diffusers"),
"vae_dir": OptionInfo(os.path.join(paths.models_path, 'VAE'), "Path to directory with VAE files"),
"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"),
"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)"),
}))
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 generated images', gr.Dropdown, lambda: {"choices": ["jpg", "png", "webp", "tiff", "jp2"]}),
"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', gr.Dropdown, lambda: {"choices": ["jpg", "png", "webp", "tiff", "jp2"]}),
"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 JSON 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, "Save a copy of the inpainting greyscale mask"),
"save_mask_composite": OptionInfo(False, "Save a copy of inpainting masked composite"),
"save_init_img": OptionInfo(False, "Save a copy of processing init images"),
"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(250, "Maximum allowed 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, "Save images to a subdirectory when using Save button"),
"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(('image-processing', "Image Processing"), {
"img2img_color_correction": OptionInfo(False, "Apply color correction to match original colors"),
"img2img_fix_steps": OptionInfo(False, "For image processing do exact number of steps as specified"),
"img2img_background_color": OptionInfo("#ffffff", "Image transparent color fill", ui_components.FormColorPicker, {}),
"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 image processing", gr.Slider, {"minimum": 0.1, "maximum": 1.5, "step": 0.01}),
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 8, "step": 1, "visible": False}),
}))
options_templates.update(options_section(('saving-paths', "Output 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(('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("Checkpoints, Lora, LyCORIS, Textual Inversion, Hypernetworks", "Extra networks tab order"),
}))
options_templates.update(options_section(('live-preview', "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, "Live preview display period", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
"show_progress_type": OptionInfo("TAESD", "Live preview method", gr.Radio, {"choices": ["Full VAE", "Approximate NN", "Approximate simple", "TAESD"]}),
"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 Settings"), {
"show_samplers": OptionInfo(["Euler a", "UniPC", "DDIM", "DPM++ 2M SDE", "DPM++ 2M SDE Karras", "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()]}),
"force_latent_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(('postprocessing', "Postprocessing"), {
'postprocessing_enable_in_main_ui': OptionInfo([], "Enable addtional postprocessing operations", 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(('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"), {
"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(('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(('lora', "Lora"), {
"lyco_patch_lora": OptionInfo(False, "Use LyCoris handler for all Lora types", gr.Checkbox, { "visible": True }),
"lora_disable": OptionInfo(False, "Disable built-in Lora handler", gr.Checkbox, { "visible": True }, onchange=lora_disable),
"lora_functional": OptionInfo(False, "Use Kohya method for handling multiple Loras", gr.Checkbox, { "visible": True }),
}))
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(('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(('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 for high-res pass", 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((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()}
metadata = {
k: {
"is_stored": k in self.data,
"tab_name": v.section[0]
} for k, v in self.data_labels.items()
}
return json.dumps({"values": d, "metadata": metadata})
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)
if cmd_opts.backend == 'diffusers':
log.info('Overriding backend to Diffusers')
opts.data['sd_backend'] = 'Diffusers'
if cmd_opts.backend == 'original':
log.info('Overriding backend to Diffusers')
opts.data['sd_backend'] = 'Original'
backend = Backend.DIFFUSERS if opts.sd_backend == 'Diffusers' else Backend.ORIGINAL
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
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 ""
def get_version():
version = None
if version is None:
try:
import subprocess
res = subprocess.run('git log --pretty=format:"%h %ad" -1 --date=short', stdout = subprocess.PIPE, stderr = subprocess.PIPE, shell=True, check=True)
ver = res.stdout.decode(encoding = 'utf8', errors='ignore') if len(res.stdout) > 0 else ' '
githash, updated = ver.split(' ')
res = subprocess.run('git remote get-url origin', stdout = subprocess.PIPE, stderr = subprocess.PIPE, shell=True, check=True)
origin = res.stdout.decode(encoding = 'utf8', errors='ignore') if len(res.stdout) > 0 else ''
res = subprocess.run('git branch --show-current', stdout = subprocess.PIPE, stderr = subprocess.PIPE, shell=True, check=True)
branch = res.stdout.decode(encoding = 'utf8', errors='ignore') if len(res.stdout) > 0 else ''
version = {
'app': 'sd.next',
'updated': updated,
'hash': githash,
'url': origin.replace('\n', '') + '/tree/' + branch.replace('\n', '')
}
except:
version = { 'app': 'sd.next' }
return version
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