| | import importlib |
| | import logging |
| | import sys |
| | import warnings |
| | from threading import Thread |
| |
|
| | from modules.timer import startup_timer |
| |
|
| |
|
| | def imports(): |
| | logging.getLogger("torch.distributed.nn").setLevel(logging.ERROR) |
| | logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage()) |
| |
|
| | import torch |
| | startup_timer.record("import torch") |
| | import pytorch_lightning |
| | startup_timer.record("import torch") |
| | warnings.filterwarnings(action="ignore", category=DeprecationWarning, module="pytorch_lightning") |
| | warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision") |
| |
|
| | import gradio |
| | startup_timer.record("import gradio") |
| |
|
| | from modules import paths, timer, import_hook, errors |
| | startup_timer.record("setup paths") |
| |
|
| | import ldm.modules.encoders.modules |
| | startup_timer.record("import ldm") |
| |
|
| | import sgm.modules.encoders.modules |
| | startup_timer.record("import sgm") |
| |
|
| | from modules import shared_init |
| | shared_init.initialize() |
| | startup_timer.record("initialize shared") |
| |
|
| | from modules import processing, gradio_extensons, ui |
| | startup_timer.record("other imports") |
| |
|
| |
|
| | def check_versions(): |
| | from modules.shared_cmd_options import cmd_opts |
| |
|
| | if not cmd_opts.skip_version_check: |
| | from modules import errors |
| | errors.check_versions() |
| |
|
| |
|
| | def initialize(): |
| | from modules import initialize_util |
| | initialize_util.fix_torch_version() |
| | initialize_util.fix_asyncio_event_loop_policy() |
| | initialize_util.validate_tls_options() |
| | initialize_util.configure_sigint_handler() |
| | initialize_util.configure_opts_onchange() |
| |
|
| | from modules import modelloader |
| | modelloader.cleanup_models() |
| |
|
| | from modules import sd_models |
| | sd_models.setup_model() |
| | startup_timer.record("setup SD model") |
| |
|
| | from modules.shared_cmd_options import cmd_opts |
| |
|
| | from modules import codeformer_model |
| | warnings.filterwarnings(action="ignore", category=UserWarning, module="torchvision.transforms.functional_tensor") |
| | codeformer_model.setup_model(cmd_opts.codeformer_models_path) |
| | startup_timer.record("setup codeformer") |
| |
|
| | from modules import gfpgan_model |
| | gfpgan_model.setup_model(cmd_opts.gfpgan_models_path) |
| | startup_timer.record("setup gfpgan") |
| |
|
| | initialize_rest(reload_script_modules=False) |
| |
|
| |
|
| | def initialize_rest(*, reload_script_modules=False): |
| | """ |
| | Called both from initialize() and when reloading the webui. |
| | """ |
| | from modules.shared_cmd_options import cmd_opts |
| |
|
| | from modules import sd_samplers |
| | sd_samplers.set_samplers() |
| | startup_timer.record("set samplers") |
| |
|
| | from modules import extensions |
| | extensions.list_extensions() |
| | startup_timer.record("list extensions") |
| |
|
| | from modules import initialize_util |
| | initialize_util.restore_config_state_file() |
| | startup_timer.record("restore config state file") |
| |
|
| | from modules import shared, upscaler, scripts |
| | if cmd_opts.ui_debug_mode: |
| | shared.sd_upscalers = upscaler.UpscalerLanczos().scalers |
| | scripts.load_scripts() |
| | return |
| |
|
| | from modules import sd_models |
| | sd_models.list_models() |
| | startup_timer.record("list SD models") |
| |
|
| | from modules import localization |
| | localization.list_localizations(cmd_opts.localizations_dir) |
| | startup_timer.record("list localizations") |
| |
|
| | with startup_timer.subcategory("load scripts"): |
| | scripts.load_scripts() |
| |
|
| | if reload_script_modules: |
| | for module in [module for name, module in sys.modules.items() if name.startswith("modules.ui")]: |
| | importlib.reload(module) |
| | startup_timer.record("reload script modules") |
| |
|
| | from modules import modelloader |
| | modelloader.load_upscalers() |
| | startup_timer.record("load upscalers") |
| |
|
| | from modules import sd_vae |
| | sd_vae.refresh_vae_list() |
| | startup_timer.record("refresh VAE") |
| |
|
| | from modules import textual_inversion |
| | textual_inversion.textual_inversion.list_textual_inversion_templates() |
| | startup_timer.record("refresh textual inversion templates") |
| |
|
| | from modules import script_callbacks, sd_hijack_optimizations, sd_hijack |
| | script_callbacks.on_list_optimizers(sd_hijack_optimizations.list_optimizers) |
| | sd_hijack.list_optimizers() |
| | startup_timer.record("scripts list_optimizers") |
| |
|
| | from modules import sd_unet |
| | sd_unet.list_unets() |
| | startup_timer.record("scripts list_unets") |
| |
|
| | def load_model(): |
| | """ |
| | Accesses shared.sd_model property to load model. |
| | After it's available, if it has been loaded before this access by some extension, |
| | its optimization may be None because the list of optimizaers has neet been filled |
| | by that time, so we apply optimization again. |
| | """ |
| |
|
| | shared.sd_model |
| |
|
| | if sd_hijack.current_optimizer is None: |
| | sd_hijack.apply_optimizations() |
| |
|
| | from modules import devices |
| | devices.first_time_calculation() |
| |
|
| | Thread(target=load_model).start() |
| |
|
| | from modules import shared_items |
| | shared_items.reload_hypernetworks() |
| | startup_timer.record("reload hypernetworks") |
| |
|
| | from modules import ui_extra_networks |
| | ui_extra_networks.initialize() |
| | ui_extra_networks.register_default_pages() |
| |
|
| | from modules import extra_networks |
| | extra_networks.initialize() |
| | extra_networks.register_default_extra_networks() |
| | startup_timer.record("initialize extra networks") |
| |
|