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
d71287e068f527979052d687d3c2d44cdf0ad99e
sdnext/modules/shared.py
Vladimir Mandic 4823595dc9 minor fixes
2023-12-10 10:27:44 -05:00

1004 lines
60 KiB
Python
Raw Blame History

import io
import os
import sys
import time
import json
import contextlib
from types import SimpleNamespace
from urllib.parse import urlparse
from enum import Enum
import requests
import gradio as gr
import fasteners
from rich.console import Console
from modules import errors, shared_items, shared_state, cmd_args, ui_components, theme
from modules.paths 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
from modules.dml import memory_providers, default_memory_provider, directml_do_hijack
import modules.interrogate
import modules.memmon
import modules.styles
import modules.devices as devices # pylint: disable=R0402
import modules.paths as paths
from installer import print_dict
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 = []
extra_networks = []
options_templates = {}
hypernetworks = {}
loaded_hypernetworks = []
settings_components = None
latent_upscale_default_mode = "None"
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"
}
compatibility_opts = ['clip_skip', 'uni_pc_lower_order_final', 'uni_pc_order']
console = Console(log_time=True, log_time_format='%H:%M:%S-%f')
class Backend(Enum):
ORIGINAL = 1
DIFFUSERS = 2
state = shared_state.State()
if not hasattr(cmd_opts, "use_openvino"):
cmd_opts.use_openvino = False
if cmd_opts.use_openvino:
backend = Backend.DIFFUSERS
cmd_opts.backend = 'diffusers'
else:
backend = Backend.DIFFUSERS if (cmd_opts.backend is not None) and (cmd_opts.backend.lower() == 'diffusers') else Backend.ORIGINAL # initial since we don't have opts loaded yet
class OptionInfo:
def __init__(self, default=None, label="", component=None, component_args=None, onchange=None, section=None, refresh=None, folder=None, submit=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.folder = folder
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
self.submit = submit
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 html(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 is_url(string):
parsed_url = urlparse(string)
return all([parsed_url.scheme, parsed_url.netloc])
def reload_hypernetworks():
from modules.hypernetworks import hypernetwork
global hypernetworks # pylint: disable=W0603
hypernetworks = hypernetwork.list_hypernetworks(opts.hypernetwork_dir)
def refresh_checkpoints():
import modules.sd_models # pylint: disable=W0621
return modules.sd_models.list_models()
def refresh_vaes():
import modules.sd_vae # pylint: disable=W0621
modules.sd_vae.refresh_vae_list()
def list_samplers():
import modules.sd_samplers # pylint: disable=W0621
modules.sd_samplers.set_samplers()
return modules.sd_samplers.all_samplers
def temp_disable_extensions():
disable_safe = ['sd-webui-controlnet', 'multidiffusion-upscaler-for-automatic1111', 'a1111-sd-webui-lycoris', 'sd-webui-agent-scheduler', 'clip-interrogator-ext', 'stable-diffusion-webui-rembg', 'sd-extension-chainner', 'stable-diffusion-webui-images-browser']
disable_diffusers = ['sd-webui-controlnet', 'multidiffusion-upscaler-for-automatic1111', 'a1111-sd-webui-lycoris', 'sd-webui-animatediff']
disable_original = []
disabled = []
if cmd_opts.safe:
for ext in disable_safe:
if ext not in opts.disabled_extensions:
disabled.append(ext)
if backend == Backend.DIFFUSERS:
for ext in disable_diffusers:
if ext not in opts.disabled_extensions:
disabled.append(ext)
if backend == Backend.ORIGINAL:
for ext in disable_original:
if ext not in opts.disabled_extensions:
disabled.append(ext)
cmd_opts.controlnet_loglevel = 'WARNING'
return disabled
def readfile(filename, silent=False, lock=False):
data = {}
lock_file = None
locked = False
try:
if not os.path.exists(filename):
return {}
t0 = time.time()
if lock:
lock_file = fasteners.InterProcessReaderWriterLock(f"{filename}.lock", logger=log)
locked = lock_file.acquire_read_lock(blocking=True, timeout=3)
with open(filename, "r", encoding="utf8") as file:
data = json.load(file)
if type(data) is str:
data = json.loads(data)
t1 = time.time()
if not silent:
log.debug(f'Read: file="{filename}" json={len(data)} bytes={os.path.getsize(filename)} time={t1-t0:.3f}')
except Exception as e:
if not silent:
log.error(f'Reading failed: {filename} {e}')
return {}
finally:
if lock_file is not None:
lock_file.release_read_lock()
if locked and os.path.exists(f"{filename}.lock"):
os.remove(f"{filename}.lock")
return data
def writefile(data, filename, mode='w', silent=False):
lock = None
locked = False
def default(obj):
log.error(f"Saving: {filename} not a valid object: {obj}")
return str(obj)
try:
t0 = time.time()
# skipkeys=True, ensure_ascii=True, check_circular=True, allow_nan=True
if type(data) == dict:
output = json.dumps(data, indent=2, default=default)
elif type(data) == list:
output = json.dumps(data, indent=2, default=default)
elif isinstance(data, object):
simple = {}
for k in data.__dict__:
if data.__dict__[k] is not None:
simple[k] = data.__dict__[k]
output = json.dumps(simple, indent=2, default=default)
else:
raise ValueError('not a valid object')
lock = fasteners.InterProcessReaderWriterLock(f"{filename}.lock", logger=log)
locked = lock.acquire_write_lock(blocking=True, timeout=3)
with open(filename, mode, encoding="utf8") as file:
file.write(output)
t1 = time.time()
if not silent:
log.debug(f'Save: file="{filename}" json={len(data)} bytes={len(output)} time={t1-t0:.3f}')
except Exception as e:
log.error(f'Saving failed: {filename} {e}')
finally:
if lock is not None:
lock.release_read_lock()
if locked and os.path.exists(f"{filename}.lock"):
os.remove(f"{filename}.lock")
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 = "Scaled-Dot-Product"
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', "Execution & Models"), {
"sd_backend": OptionInfo("original" if not cmd_opts.use_openvino else "diffusers", "Execution backend", gr.Radio, {"choices": ["original", "diffusers"] }),
"sd_checkpoint_autoload": OptionInfo(True, "Model autoload on server start"),
"sd_model_checkpoint": OptionInfo(default_checkpoint, "Base model", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints),
"sd_model_refiner": OptionInfo('None', "Refiner model", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_tiles()}, refresh=refresh_checkpoints),
"sd_vae": OptionInfo("Automatic", "VAE model", gr.Dropdown, lambda: {"choices": shared_items.sd_vae_items()}, refresh=shared_items.refresh_vae_list),
"sd_model_dict": OptionInfo('None', "Use baseline data from a different model", gr.Dropdown, lambda: {"choices": ['None'] + list_checkpoint_tiles()}, refresh=refresh_checkpoints),
"stream_load": OptionInfo(False, "Load models using stream loading method"),
"model_reuse_dict": OptionInfo(False, "When loading models attempt to reuse previous model dictionary", gr.Checkbox, {"visible": False}),
"prompt_attention": OptionInfo("Full parser", "Prompt attention parser", gr.Radio, {"choices": ["Full parser", "Compel parser", "A1111 parser", "Fixed attention"] }),
"prompt_mean_norm": OptionInfo(True, "Prompt attention mean normalization"),
"comma_padding_backtrack": OptionInfo(20, "Prompt padding for long prompts", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }),
"sd_checkpoint_cache": OptionInfo(0, "Number of cached models", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_vae_checkpoint_cache": OptionInfo(0, "Number of cached VAEs", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}),
"sd_disable_ckpt": OptionInfo(False, "Disallow usage of models in ckpt format"),
}))
options_templates.update(options_section(('cuda', "Compute Settings"), {
"math_sep": OptionInfo("<h2>Execution precision</h2>", "", gr.HTML),
"precision": OptionInfo("Autocast", "Precision type", gr.Radio, {"choices": ["Autocast", "Full"]}),
"cuda_dtype": OptionInfo("FP32" if sys.platform == "darwin" or cmd_opts.use_openvino else "BF16" if devices.backend == "ipex" else "FP16", "Device precision type", gr.Radio, {"choices": ["FP32", "FP16", "BF16"]}),
"no_half": OptionInfo(False if not cmd_opts.use_openvino else True, "Use full precision for model (--no-half)", None, None, None),
"no_half_vae": OptionInfo(False if not cmd_opts.use_openvino else True, "Use full precision for VAE (--no-half-vae)"),
"upcast_sampling": OptionInfo(False if sys.platform != "darwin" else True, "Enable upcast sampling"),
"upcast_attn": OptionInfo(False, "Enable upcast cross attention layer"),
"cuda_cast_unet": OptionInfo(False, "Use fixed UNet precision"),
"disable_nan_check": OptionInfo(True, "Disable NaN check in produced images/latent spaces", gr.Checkbox, {"visible": False}),
"rollback_vae": OptionInfo(False, "Attempt VAE roll back when produced NaN values"),
"cross_attention_sep": OptionInfo("<h2>Cross-attention</h2>", "", gr.HTML),
"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, {"choices": ['xFormers enable flash Attention', 'SDP disable memory attention']}),
"sub_quad_sep": OptionInfo("<h3>Sub-quadratic options</h3>", "", gr.HTML),
"sub_quad_q_chunk_size": OptionInfo(512, "cross-attention query chunk size", gr.Slider, {"minimum": 16, "maximum": 8192, "step": 8}),
"sub_quad_kv_chunk_size": OptionInfo(512, "cross-attention kv chunk size", gr.Slider, {"minimum": 0, "maximum": 8192, "step": 8}),
"sub_quad_chunk_threshold": OptionInfo(80, "cross-attention chunking threshold", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}),
"other_sep": OptionInfo("<h2>Execution precision</h2>", "", gr.HTML),
"opt_channelslast": OptionInfo(False, "Use channels last as torch memory format "),
"cudnn_benchmark": OptionInfo(False, "Enable full-depth cuDNN benchmark feature"),
"torch_gc_threshold": OptionInfo(80 if devices.backend == "ipex" else 90, "VRAM usage threshold before running Torch GC to clear up VRAM", gr.Slider, {"minimum": 0, "maximum": 100, "step": 1}),
"cuda_compile_sep": OptionInfo("<h2>Model Compile</h2>", "", gr.HTML),
"cuda_compile": OptionInfo(False if not cmd_opts.use_openvino else True, "Compile UNet"),
"cuda_compile_vae": OptionInfo(False if not cmd_opts.use_openvino else True, "Compile VAE"),
"cuda_compile_upscaler": OptionInfo(False if not cmd_opts.use_openvino else True, "Compile upscaler"),
"cuda_compile_backend": OptionInfo("none" if not cmd_opts.use_openvino else "openvino_fx", "Model compile backend", gr.Radio, {"choices": ['none', 'inductor', 'cudagraphs', 'aot_ts_nvfuser', 'hidet', 'ipex', 'openvino_fx', 'stable-fast']}),
"cuda_compile_mode": OptionInfo("default", "Model compile mode", gr.Radio, {"choices": ['default', 'reduce-overhead', 'max-autotune', 'max-autotune-no-cudagraphs']}),
"cuda_compile_fullgraph": OptionInfo(False, "Model compile fullgraph"),
"cuda_compile_precompile": OptionInfo(False, "Model compile precompile"),
"cuda_compile_verbose": OptionInfo(False, "Model compile verbose mode"),
"cuda_compile_errors": OptionInfo(True, "Model compile suppress errors"),
"ipex_sep": OptionInfo("<h2>IPEX, DirectML and OpenVINO</h2>", "", gr.HTML),
"ipex_optimize": OptionInfo(False if not devices.backend == "ipex" else True, "Enable IPEX Optimize for Intel GPUs"),
"ipex_optimize_upscaler": OptionInfo(False if not devices.backend == "ipex" else True, "Enable IPEX Optimize for Intel GPUs with Upscalers"),
"directml_memory_provider": OptionInfo(default_memory_provider, 'DirectML memory stats provider', gr.Radio, {"choices": memory_providers}),
"directml_catch_nan": OptionInfo(False, "DirectML retry specific operation when NaN is produced if possible. (makes generation slower)"),
"openvino_disable_model_caching": OptionInfo(False, "OpenVINO disable model caching"),
"openvino_hetero_gpu": OptionInfo(False, "OpenVINO use Hetero Device for single inference with multiple devices"),
"openvino_remove_cpu_from_hetero": OptionInfo(False, "OpenVINO remove CPU from Hetero Device"),
"openvino_remove_igpu_from_hetero": OptionInfo(False, "OpenVINO remove iGPU from Hetero Device"),
}))
options_templates.update(options_section(('advanced', "Inference Settings"), {
"token_merging_sep": OptionInfo("<h2>Token merging</h2>", "", gr.HTML),
"token_merging_ratio": OptionInfo(0.0, "Token merging ratio (txt2img)", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
"token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio (img2img)", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
"token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio (hires)", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}),
"freeu_sep": OptionInfo("<h2>FreeU</h2>", "", gr.HTML),
"freeu_enabled": OptionInfo(False, "FreeU enabled"),
"freeu_b1": OptionInfo(1.2, "1st stage backbone factor", gr.Slider, {"minimum": 1.0, "maximum": 2.0, "step": 0.01}),
"freeu_b2": OptionInfo(1.4, "2nd stage backbone factor", gr.Slider, {"minimum": 1.0, "maximum": 2.0, "step": 0.01}),
"freeu_s1": OptionInfo(0.9, "1st stage skip factor", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"freeu_s2": OptionInfo(0.2, "2nd stage skip factor", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"hypertile_sep": OptionInfo("<h2>HyperTile</h2>", "", gr.HTML),
"hypertile_vae_enabled": OptionInfo(False, "HyperTile for VAE enabled", gr.Checkbox, {"visible": False}),
"hypertile_vae_tile": OptionInfo(128, "HyperTile for VAE tile size", gr.Slider, {"minimum": 128, "maximum": 512, "step": 8, "visible": False}),
"hypertile_unet_enabled": OptionInfo(False, "HyperTile for UNet enabled"),
"hypertile_unet_tile": OptionInfo(256, "HyperTile for UNet tile size", gr.Slider, {"minimum": 256, "maximum": 1024, "step": 8}),
"inference_other_sep": OptionInfo("<h2>Other</h2>", "", gr.HTML),
"batch_frame_mode": OptionInfo(False, "Process multiple images in batch in parallel"),
"inference_mode": OptionInfo("no-grad", "Torch inference mode", gr.Radio, {"choices": ["no-grad", "inference-mode", "none"]}),
"sd_vae_sliced_encode": OptionInfo(False, "VAE Slicing (original)"),
}))
options_templates.update(options_section(('diffusers', "Diffusers Settings"), {
"diffusers_pipeline": OptionInfo('Autodetect', 'Diffusers pipeline', gr.Dropdown, lambda: {"choices": list(shared_items.get_pipelines()) }),
"diffusers_move_base": OptionInfo(True, "Move base model to CPU when using refiner"),
"diffusers_move_unet": OptionInfo(True, "Move base model to CPU when using VAE"),
"diffusers_move_refiner": OptionInfo(True, "Move refiner model to CPU when not in use"),
"diffusers_extract_ema": OptionInfo(True, "Use model EMA weights when possible"),
"diffusers_generator_device": OptionInfo("default", "Generator device", gr.Radio, {"choices": ["default", "cpu"]}),
"diffusers_model_cpu_offload": OptionInfo(False, "Enable model CPU offload (--medvram)"),
"diffusers_seq_cpu_offload": OptionInfo(False, "Enable sequential CPU offload (--lowvram)"),
"diffusers_vae_upcast": OptionInfo("default", "VAE upcasting", gr.Radio, {"choices": ['default', 'true', 'false']}),
"diffusers_vae_slicing": OptionInfo(True, "Enable VAE slicing"),
"diffusers_vae_tiling": OptionInfo(True if not cmd_opts.use_openvino else False, "Enable VAE tiling"),
"diffusers_attention_slicing": OptionInfo(False, "Enable attention slicing"),
"diffusers_model_load_variant": OptionInfo("default", "Diffusers model loading variant", gr.Radio, {"choices": ['default', 'fp32', 'fp16']}),
"diffusers_vae_load_variant": OptionInfo("default", "Diffusers VAE loading variant", gr.Radio, {"choices": ['default', 'fp32', 'fp16']}),
"custom_diffusers_pipeline": OptionInfo('', 'Load custom Diffusers pipeline'),
"diffusers_eval": OptionInfo(True, "Force model eval"),
"diffusers_force_zeros": OptionInfo(True, "Force zeros for prompts when empty"),
"diffusers_aesthetics_score": OptionInfo(False, "Require aesthetics score"),
"diffusers_pooled": OptionInfo("default", "Diffusers SDXL pooled embeds (experimental)", gr.Radio, {"choices": ['default', 'weighted']}),
}))
options_templates.update(options_section(('system-paths', "System Paths"), {
"models_paths_sep_options": OptionInfo("<h2>Models paths</h2>", "", gr.HTML),
"models_dir": OptionInfo('models', "Base path where all models are stored", folder=True),
"ckpt_dir": OptionInfo(os.path.join(paths.models_path, 'Stable-diffusion'), "Folder with stable diffusion models", folder=True),
"diffusers_dir": OptionInfo(os.path.join(paths.models_path, 'Diffusers'), "Folder with Hugggingface models", folder=True),
"hfcache_dir": OptionInfo(os.path.join(os.path.expanduser('~'), '.cache', 'huggingface', 'hub'), "Folder for Hugggingface cache", folder=True),
"vae_dir": OptionInfo(os.path.join(paths.models_path, 'VAE'), "Folder with VAE files", folder=True),
"sd_lora": OptionInfo("", "Add LoRA to prompt", gr.Textbox, {"visible": False}),
"lora_dir": OptionInfo(os.path.join(paths.models_path, 'Lora'), "Folder with LoRA network(s)", folder=True),
"lyco_dir": OptionInfo(os.path.join(paths.models_path, 'LyCORIS'), "Folder with LyCORIS network(s)", gr.Text, {"visible": False}),
"styles_dir": OptionInfo(os.path.join(paths.data_path, 'styles.csv'), "File or Folder with user-defined styles", folder=True),
"embeddings_dir": OptionInfo(os.path.join(paths.models_path, 'embeddings'), "Folder with textual inversion embeddings", folder=True),
"hypernetwork_dir": OptionInfo(os.path.join(paths.models_path, 'hypernetworks'), "Folder with Hypernetwork models", folder=True),
"codeformer_models_path": OptionInfo(os.path.join(paths.models_path, 'Codeformer'), "Folder with codeformer models", folder=True),
"gfpgan_models_path": OptionInfo(os.path.join(paths.models_path, 'GFPGAN'), "Folder with GFPGAN models", folder=True),
"esrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'ESRGAN'), "Folder with ESRGAN models", folder=True),
"bsrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'BSRGAN'), "Folder with BSRGAN models", folder=True),
"realesrgan_models_path": OptionInfo(os.path.join(paths.models_path, 'RealESRGAN'), "Folder with RealESRGAN models", folder=True),
"scunet_models_path": OptionInfo(os.path.join(paths.models_path, 'SCUNet'), "Folder with SCUNet models", folder=True),
"swinir_models_path": OptionInfo(os.path.join(paths.models_path, 'SwinIR'), "Folder with SwinIR models", folder=True),
"ldsr_models_path": OptionInfo(os.path.join(paths.models_path, 'LDSR'), "Folder with LDSR models", folder=True),
"clip_models_path": OptionInfo(os.path.join(paths.models_path, 'CLIP'), "Folder with CLIP models", folder=True),
"other_paths_sep_options": OptionInfo("<h2>Other paths</h2>", "", gr.HTML),
"temp_dir": OptionInfo("", "Directory for temporary images; leave empty for default", folder=True),
"clean_temp_dir_at_start": OptionInfo(True, "Cleanup non-default temporary directory when starting webui"),
}))
options_templates.update(options_section(('saving-images', "Image Options"), {
"keep_incomplete": OptionInfo(True, "Keep incomplete images"),
"samples_save": OptionInfo(True, "Always save all generated images"),
"samples_format": OptionInfo('jpg', 'File format for generated images', gr.Dropdown, {"choices": ["jpg", "png", "webp", "tiff", "jp2"]}),
"jpeg_quality": OptionInfo(90, "Quality for saved images", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
"img_max_size_mp": OptionInfo(250, "Maximum image size (MP)", gr.Slider, {"minimum": 100, "maximum": 2000, "step": 1}),
"webp_lossless": OptionInfo(False, "Use lossless compression for webp images"),
"save_selected_only": OptionInfo(True, "When using 'Save' button, only save a single selected image"),
"samples_save_zip": OptionInfo(True, "Create zip archive when downloading multiple images"),
"image_sep_metadata": OptionInfo("<h2>Metadata/Logging</h2>", "", gr.HTML),
"image_metadata": OptionInfo(True, "Include metadata in saved images"),
"save_txt": OptionInfo(False, "Create info file for each every image"),
"save_log_fn": OptionInfo("", "Create JSON log file for each saved image", component_args=hide_dirs),
"image_watermark_enabled": OptionInfo(False, "Include watermark in saved images"),
"image_watermark": OptionInfo('', "Image watermark string"),
"image_sep_grid": OptionInfo("<h2>Grid Options</h2>", "", gr.HTML),
"grid_save": OptionInfo(True, "Always save all generated image grids"),
"grid_format": OptionInfo('jpg', 'File format for grids', gr.Dropdown, {"choices": ["jpg", "png", "webp", "tiff", "jp2"]}),
"n_rows": OptionInfo(-1, "Grid row count", gr.Slider, {"minimum": -1, "maximum": 16, "step": 1}),
"grid_background": OptionInfo("#000000", "Grid background color", ui_components.FormColorPicker, {}),
"font": OptionInfo("", "Font file"),
"font_color": OptionInfo("#FFFFFF", "Font color", ui_components.FormColorPicker, {}),
"save_sep_options": OptionInfo("<h2>Intermediate Image Saving</h2>", "", gr.HTML),
"save_init_img": OptionInfo(False, "Save copy of img2img init images"),
"save_images_before_highres_fix": OptionInfo(False, "Save copy of image before applying hires"),
"save_images_before_refiner": OptionInfo(False, "Save copy of image before running refiner"),
"save_images_before_face_restoration": OptionInfo(False, "Save copy of image before doing face restoration"),
"save_images_before_color_correction": OptionInfo(False, "Save copy of image before applying color correction"),
"save_mask": OptionInfo(False, "Save copy of the inpainting greyscale mask"),
"save_mask_composite": OptionInfo(False, "Save copy of inpainting masked composite"),
}))
options_templates.update(options_section(('saving-paths', "Image Naming & Paths"), {
"saving_sep_images": OptionInfo("<h2>Save options</h2>", "", gr.HTML),
"save_images_add_number": OptionInfo(True, "Add number to filename when saving", component_args=hide_dirs),
"use_original_name_batch": OptionInfo(True, "Use original name during batch process"),
"use_upscaler_name_as_suffix": OptionInfo(True, "Use upscaler as suffix", gr.Checkbox, {"visible": False}),
"samples_filename_pattern": OptionInfo("[seq]-[model_name]-[prompt_words]", "Images filename pattern", component_args=hide_dirs),
"directories_max_prompt_words": OptionInfo(8, "Max words for [prompt_words] pattern", gr.Slider, {"minimum": 1, "maximum": 99, "step": 1, **hide_dirs}),
"use_save_to_dirs_for_ui": OptionInfo(False, "Save images to a subdirectory when using Save button", gr.Checkbox, {"visible": False}),
"save_to_dirs": OptionInfo(False, "Save images to a subdirectory"),
"directories_filename_pattern": OptionInfo("[date]", "Directory name pattern", component_args=hide_dirs),
"outdir_sep_dirs": OptionInfo("<h2>Directories</h2>", "", gr.HTML),
"outdir_samples": OptionInfo("", "Output directory for images", component_args=hide_dirs, folder=True),
"outdir_txt2img_samples": OptionInfo("outputs/text", 'Directory for text generate', component_args=hide_dirs, folder=True),
"outdir_img2img_samples": OptionInfo("outputs/image", 'Directory for image generate', component_args=hide_dirs, folder=True),
"outdir_extras_samples": OptionInfo("outputs/extras", 'Directory for processed images', component_args=hide_dirs, folder=True),
"outdir_save": OptionInfo("outputs/save", "Directory for manually saved images", component_args=hide_dirs, folder=True),
"outdir_video": OptionInfo("outputs/video", "Directory for videos", component_args=hide_dirs, folder=True),
"outdir_init_images": OptionInfo("outputs/init-images", "Directory for init images", component_args=hide_dirs, folder=True),
"outdir_sep_grids": OptionInfo("<h2>Grids</h2>", "", gr.HTML),
"grid_extended_filename": OptionInfo(True, "Add extended info (seed, prompt) to filename when saving grid", gr.Checkbox, {"visible": False}),
"grid_save_to_dirs": OptionInfo(False, "Save grids to a subdirectory", gr.Checkbox, {"visible": False}),
"outdir_grids": OptionInfo("", "Output directory for grids", component_args=hide_dirs, folder=True),
"outdir_txt2img_grids": OptionInfo("outputs/grids", 'Output directory for txt2img grids', component_args=hide_dirs, folder=True),
"outdir_img2img_grids": OptionInfo("outputs/grids", 'Output directory for img2img grids', component_args=hide_dirs, folder=True),
}))
options_templates.update(options_section(('ui', "User Interface"), {
"motd": OptionInfo(True, "Show MOTD"),
"gradio_theme": OptionInfo("black-teal", "UI theme", gr.Dropdown, lambda: {"choices": theme.list_themes()}, refresh=theme.refresh_themes),
"theme_style": OptionInfo("Auto", "Theme mode", gr.Radio, {"choices": ["Auto", "Dark", "Light"]}),
"tooltips": OptionInfo("UI Tooltips", "UI tooltips", gr.Radio, {"choices": ["None", "Browser default", "UI tooltips"], "visible": False}),
"gallery_height": OptionInfo("", "Gallery height", gr.Textbox),
"compact_view": OptionInfo(False, "Compact view"),
"return_grid": OptionInfo(True, "Show grid in results"),
"return_mask": OptionInfo(False, "Inpainting include greyscale mask in results"),
"return_mask_composite": OptionInfo(False, "Inpainting include masked composite in results"),
"disable_weights_auto_swap": OptionInfo(True, "Do not change 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"),
"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, "visible": False}),
"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, "visible": False}),
"keyedit_delimiters": OptionInfo(".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters", gr.Textbox, { "visible": False }), # pylint: disable=anomalous-backslash-in-string
"quicksettings_list": OptionInfo(["sd_model_checkpoint"] if backend == Backend.ORIGINAL else ["sd_model_checkpoint", "sd_model_refiner"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}),
"ui_scripts_reorder": OptionInfo("", "UI scripts order", gr.Textbox, { "visible": False }),
}))
options_templates.update(options_section(('live-preview', "Live Previews"), {
"show_progressbar": OptionInfo(True, "Show progressbar", gr.Checkbox, {"visible": False}),
"live_previews_enable": OptionInfo(True, "Show live previews of the created image", gr.Checkbox, {"visible": False}),
"show_progress_grid": OptionInfo(True, "Show previews of all images generated in a batch as a grid", gr.Checkbox, {"visible": False}),
"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, folder=True),
"show_progress_every_n_steps": OptionInfo(1, "Live preview display period", gr.Slider, {"minimum": -1, "maximum": 32, "step": 1}),
"show_progress_type": OptionInfo("Approximate", "Live preview method", gr.Radio, {"choices": ["Simple", "Approximate", "TAESD", "Full VAE"]}),
"live_preview_content": OptionInfo("Combined", "Live preview subject", gr.Radio, {"choices": ["Combined", "Prompt", "Negative prompt"], "visible": False}),
"live_preview_refresh_period": OptionInfo(500, "Progress update period", gr.Slider, {"minimum": 0, "maximum": 5000, "step": 25}),
"logmonitor_show": OptionInfo(True, "Show log view"),
"logmonitor_refresh_period": OptionInfo(5000, "Log view update period", gr.Slider, {"minimum": 0, "maximum": 30000, "step": 25}),
}))
options_templates.update(options_section(('sampler-params', "Sampler Settings"), {
"show_samplers": OptionInfo([], "Show samplers in user interface", gr.CheckboxGroup, lambda: {"choices": [x.name for x in list_samplers()]}),
'eta_noise_seed_delta': OptionInfo(0, "Noise seed delta (eta)", gr.Number, {"precision": 0}),
"scheduler_eta": OptionInfo(1.0, "Noise multiplier (eta)", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"schedulers_solver_order": OptionInfo(2, "Solver order (where applicable)", gr.Slider, {"minimum": 1, "maximum": 5, "step": 1}),
# managed from ui.py for backend original
"schedulers_brownian_noise": OptionInfo(True, "Use Brownian noise", gr.Checkbox, {"visible": False}),
"schedulers_discard_penultimate": OptionInfo(True, "Discard penultimate sigma", gr.Checkbox, {"visible": False}),
"schedulers_sigma": OptionInfo("default", "Sigma algorithm", gr.Radio, {"choices": ['default', 'karras', 'exponential', 'polyexponential'], "visible": False}),
"schedulers_use_karras": OptionInfo(True, "Use Karras sigmas", gr.Checkbox, {"visible": False}),
"schedulers_use_thresholding": OptionInfo(False, "Use dynamic thresholding", gr.Checkbox, {"visible": False}),
"schedulers_use_loworder": OptionInfo(True, "Use simplified solvers in final steps", gr.Checkbox, {"visible": False}),
"schedulers_prediction_type": OptionInfo("default", "Override model prediction type", gr.Radio, {"choices": ['default', 'epsilon', 'sample', 'v_prediction']}),
# managed from ui.py for backend diffusers
"schedulers_sep_diffusers": OptionInfo("<h2>Diffusers specific config</h2>", "", gr.HTML),
"schedulers_dpm_solver": OptionInfo("sde-dpmsolver++", "DPM solver algorithm", gr.Radio, {"choices": ['dpmsolver', 'dpmsolver++', 'sde-dpmsolver', 'sde-dpmsolver++']}),
"schedulers_beta_schedule": OptionInfo("default", "Beta schedule", gr.Radio, {"choices": ['default', 'linear', 'scaled_linear', 'squaredcos_cap_v2']}),
'schedulers_beta_start': OptionInfo(0, "Beta start", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.00001}),
'schedulers_beta_end': OptionInfo(0, "Beta end", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.00001}),
'schedulers_timesteps_range': OptionInfo(1000, "Timesteps range", gr.Slider, {"minimum": 250, "maximum": 4000, "step": 1}),
"schedulers_rescale_betas": OptionInfo(False, "Rescale betas with zero terminal SNR", gr.Checkbox),
# managed from ui.py for backend original k-diffusion
"schedulers_sep_kdiffusers": OptionInfo("<h2>K-Diffusion specific config</h2>", "", gr.HTML),
"always_batch_cond_uncond": OptionInfo(False, "Disable conditional batching enabled on low memory systems"),
"enable_quantization": OptionInfo(True, "Enable quantization for sharper and cleaner results"),
's_churn': OptionInfo(0.0, "Sigma churn", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_min_uncond': OptionInfo(0.0, "Sigma negative guidance minimum ", 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}),
's_min': OptionInfo(0.0, "Sigma min", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
's_max': OptionInfo(0.0, "Sigma max", gr.Slider, {"minimum": 0.0, "maximum": 100.0, "step": 1.0}),
"schedulers_sep_compvis": OptionInfo("<h2>CompVis specific config</h2>", "", gr.HTML),
'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"]}),
"ddim_discretize": OptionInfo('uniform', "DDIM discretize img2img", gr.Radio, {"choices": ['uniform', 'quad']}),
# TODO pad_cond_uncond implementation missing for original backend
"pad_cond_uncond": OptionInfo(True, "Pad prompt and negative prompt to be same length", gr.Checkbox, {"visible": False}),
# TODO batch_cond-uncond implementation missing for original backend
"batch_cond_uncond": OptionInfo(True, "Do conditional and unconditional denoising in one batch", gr.Checkbox, {"visible": False}),
}))
options_templates.update(options_section(('postprocessing', "Postprocessing"), {
'postprocessing_enable_in_main_ui': OptionInfo([], "Enable additional 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()]}),
"postprocessing_sep_img2img": OptionInfo("<h2>Img2Img & Inpainting</h2>", "", gr.HTML),
"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}),
"postprocessing_sep_face_restoration": OptionInfo("<h2>Face Restoration</h2>", "", gr.HTML),
"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", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
"face_restoration_unload": OptionInfo(False, "Move face restoration model from VRAM into RAM after processing"),
"postprocessing_sep_upscalers": OptionInfo("<h2>Upscaling</h2>", "", gr.HTML),
"upscaler_unload": OptionInfo(False, "Unload upscaler after processing"),
# 'upscaling_max_images_in_cache': OptionInfo(5, "Maximum number of images in upscaling cache", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1, "visible": False}),
"upscaler_for_img2img": OptionInfo("None", "Default upscaler for image resize operations", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
"upscaler_tile_size": OptionInfo(192, "Upscaler tile size", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
"upscaler_tile_overlap": OptionInfo(8, "Upscaler tile overlap", gr.Slider, {"minimum": 0, "maximum": 64, "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, "Save resumable optimizer state when training"),
"save_training_settings_to_txt": OptionInfo(True, "Save training settings to a text file on training start"),
"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", folder=True),
"training_image_repeats_per_epoch": OptionInfo(1, "Image repeats per epoch", gr.Slider, {"minimum": 1, "maximum": 100, "step": 1}),
"training_write_csv_every": OptionInfo(0, "Save loss CSV file every n steps"),
"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, "Tensorboard flush period"),
}))
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", 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", gr.Slider, { "visible": False }),
"interrogate_clip_skip_categories": OptionInfo(["artists", "movements", "flavors"], "Interrogate: skip 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"),
"deepbooru_filter_tags": OptionInfo("", "Filter out tags from deepbooru output"),
}))
options_templates.update(options_section(('extra_networks', "Extra Networks"), {
"extra_networks_sep1": OptionInfo("<h2>Extra networks UI</h2>", "", gr.HTML),
"extra_networks": OptionInfo(["All"], "Extra networks", ui_components.DropdownMulti, lambda: {"choices": ['All'] + [en.title for en in extra_networks]}),
"extra_networks_view": OptionInfo("gallery", "UI view", gr.Radio, {"choices": ["gallery", "list"]}),
"extra_networks_card_cover": OptionInfo("sidebar", "UI position", gr.Radio, {"choices": ["cover", "inline", "sidebar"]}),
"extra_networks_height": OptionInfo(53, "UI height (%)", gr.Slider, {"minimum": 10, "maximum": 100, "step": 1}),
"extra_networks_sidebar_width": OptionInfo(35, "UI sidebar width (%)", gr.Slider, {"minimum": 10, "maximum": 80, "step": 1}),
"extra_networks_card_size": OptionInfo(160, "UI card size (px)", gr.Slider, {"minimum": 20, "maximum": 2000, "step": 1}),
"extra_networks_card_square": OptionInfo(True, "UI disable variable aspect ratio"),
"extra_networks_card_fit": OptionInfo("cover", "UI image contain method", gr.Radio, {"choices": ["contain", "cover", "fill"], "visible": False}),
"extra_networks_sep2": OptionInfo("<h2>Extra networks general</h2>", "", gr.HTML),
"extra_network_skip_indexing": OptionInfo(False, "Build info on first access", gr.Checkbox),
"extra_networks_default_multiplier": OptionInfo(1.0, "Default multiplier for extra networks", gr.Slider, {"minimum": 0.0, "maximum": 1.0, "step": 0.01}),
"extra_networks_sep3": OptionInfo("<h2>Extra networks settings</h2>", "", gr.HTML),
"extra_networks_styles": OptionInfo(True, "Show built-in styles"),
"lora_preferred_name": OptionInfo("filename", "LoRA preffered name", gr.Radio, {"choices": ["filename", "alias"]}),
"lora_add_hashes_to_infotext": OptionInfo(True, "LoRA add hash info"),
"lora_force_diffusers": OptionInfo(False if not cmd_opts.use_openvino else True, "LoRA use alternative loading method"),
"lora_fuse_diffusers": OptionInfo(False if not cmd_opts.use_openvino else True, "LoRA use merge when using alternative method"),
"lora_in_memory_limit": OptionInfo(0, "LoRA memory cache", gr.Slider, {"minimum": 0, "maximum": 24, "step": 1}),
"lora_functional": OptionInfo(False, "Use Kohya method for handling multiple LoRA", gr.Checkbox, { "visible": False }),
"sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, { "choices": ["None"], "visible": False }),
}))
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
filename = None
typemap = {int: float}
def __init__(self):
self.data = {k: v.default for k, v in self.data_labels.items()}
def __setattr__(self, key, value): # pylint: disable=inconsistent-return-statements
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
self.data[key] = value
return
return super(Options, self).__setattr__(key, value) # pylint: disable=super-with-arguments
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) # pylint: disable=super-with-arguments
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 is None:
oldval = self.data_labels[key].default
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:
log.error(f'Error in onchange callback: {key} {value} {e}')
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)
return data_label.default if data_label is not None else None
def save(self, filename=None, silent=False):
if filename is None:
filename = self.filename
if cmd_opts.freeze:
log.warning(f'Settings saving is disabled: {filename}')
return
try:
# output = json.dumps(self.data, indent=2)
diff = {}
unused_settings = []
for k, v in self.data.items():
if k in self.data_labels:
if type(v) is list:
diff[k] = v
if self.data_labels[k].default != v:
diff[k] = v
else:
if k not in compatibility_opts:
unused_settings.append(k)
diff[k] = v
writefile(diff, filename, silent=silent)
if len(unused_settings) > 0:
log.debug(f"Unused settings: {unused_settings}")
except Exception as e:
log.error(f'Saving settings failed: {filename} {e}')
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=None):
if filename is None:
filename = self.filename
if not os.path.isfile(filename):
log.debug(f'Created default config: {filename}')
self.save(filename)
return
self.data = readfile(filename, lock=True)
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(',')]
unknown_settings = []
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"Error: bad setting value: {k}: {v} ({type(v).__name__}; expected {type(info.default).__name__})")
if info is None and k not in compatibility_opts:
unknown_settings.append(k)
if len(unknown_settings) > 0:
log.debug(f"Unknown settings: {unknown_settings}")
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 and self.data[k] != self.data_labels[k].default, # pylint: disable=unnecessary-dict-index-lookup
"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 = dict(sorted(settings_items, key=lambda x: section_ids[x[1].section]))
def cast_value(self, key, value):
"""casts an arbitrary to the same type as this setting's value with key
Example: cast_value("eta_noise_seed_delta", "12") -> returns 12 (an int rather than str)
"""
if value is None:
return None
default_value = self.data_labels[key].default
if default_value is None:
default_value = getattr(self, key, None)
if default_value is None:
return None
expected_type = type(default_value)
if expected_type == bool and value == "False":
value = False
elif expected_type == type(value):
pass
else:
value = expected_type(value)
return value
profiler = None
opts = Options()
config_filename = cmd_opts.config
opts.load(config_filename)
cmd_opts = cmd_args.compatibility_args(opts, cmd_opts)
if cmd_opts.backend is None:
backend = Backend.DIFFUSERS if cmd_opts.use_openvino or opts.data.get('sd_backend', 'original') == 'diffusers' else Backend.ORIGINAL
else:
backend = Backend.DIFFUSERS if cmd_opts.use_openvino or cmd_opts.backend.lower() == 'diffusers' else Backend.ORIGINAL
opts.data['sd_backend'] = 'diffusers' if backend == Backend.DIFFUSERS else 'original'
if cmd_opts.use_xformers:
opts.data['cross_attention_optimization'] = 'xFormers'
opts.data['uni_pc_lower_order_final'] = opts.schedulers_use_loworder # compatibility
opts.data['uni_pc_order'] = opts.schedulers_solver_order # compatibility
log.info(f'Engine: backend={backend} compute={devices.backend} mode={devices.inference_context.__name__} device={devices.get_optimal_device_name()} cross-optimization="{opts.cross_attention_optimization}"')
log.info(f'Device: {print_dict(devices.get_gpu_info())}')
prompt_styles = modules.styles.StyleDatabase(opts)
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
mem_mon = modules.memmon.MemUsageMonitor("MemMon", devices.device)
max_workers = 2
if devices.backend == "directml":
directml_do_hijack()
class TotalTQDM: # compatibility with previous global-tqdm
# import tqdm
def __init__(self):
pass
def reset(self):
pass
def update(self):
pass
def updateTotal(self, new_total):
pass
def clear(self):
pass
total_tqdm = TotalTQDM()
def restart_server(restart=True):
if demo is None:
return
log.warning('Server shutdown requested')
try:
sys.tracebacklimit = 0
stdout = io.StringIO()
stderr = io.StringIO()
with contextlib.redirect_stdout(stdout), contextlib.redirect_stdout(stderr):
demo.server.wants_restart = restart
demo.server.should_exit = True
demo.server.force_exit = True
demo.close(verbose=False)
demo.server.close()
demo.fns = []
time.sleep(1)
sys.tracebacklimit = 100
# os._exit(0)
except (Exception, BaseException) as e:
log.error(f'Server shutdown error: {e}')
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)
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 rev-parse --abbrev-ref HEAD', 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 Exception:
version = { 'app': 'sd.next' }
return version
def req(url_addr, headers = None, **kwargs):
if headers is None:
headers = { 'Content-type': 'application/json' }
try:
res = requests.get(url_addr, timeout=30, headers=headers, verify=False, allow_redirects=True, **kwargs)
except Exception as e:
log.error(f'HTTP request error: url={url_addr} {e}')
res = { 'status_code': 500, 'text': f'HTTP request error: url={url_addr} {e}' }
res = SimpleNamespace(**res)
return res
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.
@property
def sd_model(self):
import modules.sd_models # pylint: disable=W0621
if modules.sd_models.model_data.sd_model is None:
log.debug(f'Model requested: fn={sys._getframe().f_back.f_code.co_name}') # pylint: disable=protected-access
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)
@property
def sd_refiner(self):
import modules.sd_models # pylint: disable=W0621
return modules.sd_models.model_data.get_sd_refiner()
@sd_refiner.setter
def sd_refiner(self, value):
import modules.sd_models # pylint: disable=W0621
modules.sd_models.model_data.set_sd_refiner(value)
@property
def backend(self):
return Backend.ORIGINAL if not cmd_opts.use_openvino and opts.data['sd_backend'] == 'original' else Backend.DIFFUSERS
@property
def sd_model_type(self):
try:
import modules.sd_models # pylint: disable=W0621
if modules.sd_models.model_data.sd_model is None:
model_type = 'none'
return model_type
if backend == Backend.ORIGINAL:
model_type = 'ldm'
elif "StableDiffusionXL" in self.sd_model.__class__.__name__:
model_type = 'sdxl'
elif "StableDiffusion" in self.sd_model.__class__.__name__:
model_type = 'sd'
elif "LatentConsistencyModel" in self.sd_model.__class__.__name__:
model_type = 'sd' # lcm is compatible with sd
elif "AnimateDiffPipeline" in self.sd_model.__class__.__name__:
model_type = 'sd' # ad is compatible with sd
elif "Kandinsky" in self.sd_model.__class__.__name__:
model_type = 'kandinsky'
else:
model_type = self.sd_model.__class__.__name__
except Exception:
model_type = 'unknown'
return model_type
@property
def sd_refiner_type(self):
try:
import modules.sd_models # pylint: disable=W0621
if modules.sd_models.model_data.sd_refiner is None:
model_type = 'none'
return model_type
if backend == Backend.ORIGINAL:
model_type = 'ldm'
elif "StableDiffusionXL" in self.sd_refiner.__class__.__name__:
model_type = 'sdxl'
elif "StableDiffusion" in self.sd_refiner.__class__.__name__:
model_type = 'sd'
elif "Kandinsky" in self.sd_refiner.__class__.__name__:
model_type = 'kandinsky'
else:
model_type = self.sd_refiner.__class__.__name__
except Exception:
model_type = 'unknown'
return model_type
sd_model = None
sd_refiner = None
sd_model_type = ''
sd_refiner_type = ''
compiled_model_state = None
sys.modules[__name__].__class__ = Shared
View Git Blame Copy Permalink