mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-12-21 07:30:02 +08:00
Merge branch 'disable_initialization'
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commit
50fb20cedc
@ -10,7 +10,7 @@ from modules.upscaler import Upscaler
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from modules.paths import script_path, models_path
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def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None) -> list:
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def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None) -> list:
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"""
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A one-and done loader to try finding the desired models in specified directories.
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@ -45,6 +45,8 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
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full_path = file
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if os.path.isdir(full_path):
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continue
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if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]):
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continue
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if len(ext_filter) != 0:
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model_name, extension = os.path.splitext(file)
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if extension not in ext_filter:
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95
modules/sd_disable_initialization.py
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95
modules/sd_disable_initialization.py
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@ -0,0 +1,95 @@
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import ldm.modules.encoders.modules
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import open_clip
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import torch
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import transformers.utils.hub
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class DisableInitialization:
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"""
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When an object of this class enters a `with` block, it starts:
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- preventing torch's layer initialization functions from working
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- changes CLIP and OpenCLIP to not download model weights
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- changes CLIP to not make requests to check if there is a new version of a file you already have
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When it leaves the block, it reverts everything to how it was before.
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Use it like this:
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```
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with DisableInitialization():
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do_things()
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```
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"""
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def __enter__(self):
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def do_nothing(*args, **kwargs):
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pass
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def create_model_and_transforms_without_pretrained(*args, pretrained=None, **kwargs):
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return self.create_model_and_transforms(*args, pretrained=None, **kwargs)
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def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):
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return self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
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def transformers_modeling_utils_load_pretrained_model(*args, **kwargs):
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args = args[0:3] + ('/', ) + args[4:] # resolved_archive_file; must set it to something to prevent what seems to be a bug
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return self.transformers_modeling_utils_load_pretrained_model(*args, **kwargs)
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def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
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# this file is always 404, prevent making request
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if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json':
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raise transformers.utils.hub.EntryNotFoundError
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try:
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return original(url, *args, local_files_only=True, **kwargs)
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except Exception as e:
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return original(url, *args, local_files_only=False, **kwargs)
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def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):
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return transformers_utils_hub_get_file_from_cache(self.transformers_utils_hub_get_from_cache, url, *args, **kwargs)
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def transformers_tokenization_utils_base_cached_file(url, *args, local_files_only=False, **kwargs):
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return transformers_utils_hub_get_file_from_cache(self.transformers_tokenization_utils_base_cached_file, url, *args, **kwargs)
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def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs):
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return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **kwargs)
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self.init_kaiming_uniform = torch.nn.init.kaiming_uniform_
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self.init_no_grad_normal = torch.nn.init._no_grad_normal_
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self.init_no_grad_uniform_ = torch.nn.init._no_grad_uniform_
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self.create_model_and_transforms = open_clip.create_model_and_transforms
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self.CLIPTextModel_from_pretrained = ldm.modules.encoders.modules.CLIPTextModel.from_pretrained
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self.transformers_modeling_utils_load_pretrained_model = getattr(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', None)
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self.transformers_tokenization_utils_base_cached_file = getattr(transformers.tokenization_utils_base, 'cached_file', None)
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self.transformers_configuration_utils_cached_file = getattr(transformers.configuration_utils, 'cached_file', None)
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self.transformers_utils_hub_get_from_cache = getattr(transformers.utils.hub, 'get_from_cache', None)
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torch.nn.init.kaiming_uniform_ = do_nothing
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torch.nn.init._no_grad_normal_ = do_nothing
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torch.nn.init._no_grad_uniform_ = do_nothing
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open_clip.create_model_and_transforms = create_model_and_transforms_without_pretrained
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ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = CLIPTextModel_from_pretrained
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if self.transformers_modeling_utils_load_pretrained_model is not None:
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transformers.modeling_utils.PreTrainedModel._load_pretrained_model = transformers_modeling_utils_load_pretrained_model
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if self.transformers_tokenization_utils_base_cached_file is not None:
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transformers.tokenization_utils_base.cached_file = transformers_tokenization_utils_base_cached_file
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if self.transformers_configuration_utils_cached_file is not None:
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transformers.configuration_utils.cached_file = transformers_configuration_utils_cached_file
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if self.transformers_utils_hub_get_from_cache is not None:
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transformers.utils.hub.get_from_cache = transformers_utils_hub_get_from_cache
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def __exit__(self, exc_type, exc_val, exc_tb):
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torch.nn.init.kaiming_uniform_ = self.init_kaiming_uniform
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torch.nn.init._no_grad_normal_ = self.init_no_grad_normal
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torch.nn.init._no_grad_uniform_ = self.init_no_grad_uniform_
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open_clip.create_model_and_transforms = self.create_model_and_transforms
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ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = self.CLIPTextModel_from_pretrained
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if self.transformers_modeling_utils_load_pretrained_model is not None:
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transformers.modeling_utils.PreTrainedModel._load_pretrained_model = self.transformers_modeling_utils_load_pretrained_model
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if self.transformers_tokenization_utils_base_cached_file is not None:
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transformers.utils.hub.cached_file = self.transformers_tokenization_utils_base_cached_file
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if self.transformers_configuration_utils_cached_file is not None:
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transformers.utils.hub.cached_file = self.transformers_configuration_utils_cached_file
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if self.transformers_utils_hub_get_from_cache is not None:
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transformers.utils.hub.get_from_cache = self.transformers_utils_hub_get_from_cache
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@ -2,6 +2,7 @@ import collections
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import os.path
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import sys
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import gc
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import time
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from collections import namedtuple
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import torch
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import re
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@ -13,7 +14,7 @@ import ldm.modules.midas as midas
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from ldm.util import instantiate_from_config
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from modules import shared, modelloader, devices, script_callbacks, sd_vae
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from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors
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from modules.paths import models_path
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from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
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@ -61,7 +62,7 @@ def find_checkpoint_config(info):
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def list_models():
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checkpoints_list.clear()
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model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"])
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model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], ext_blacklist=[".vae.safetensors"])
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def modeltitle(path, shorthash):
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abspath = os.path.abspath(path)
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@ -288,6 +289,17 @@ def enable_midas_autodownload():
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midas.api.load_model = load_model_wrapper
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class Timer:
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def __init__(self):
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self.start = time.time()
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def elapsed(self):
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end = time.time()
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res = end - self.start
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self.start = end
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return res
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def load_model(checkpoint_info=None):
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from modules import lowvram, sd_hijack
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checkpoint_info = checkpoint_info or select_checkpoint()
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@ -319,10 +331,21 @@ def load_model(checkpoint_info=None):
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if shared.cmd_opts.no_half:
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sd_config.model.params.unet_config.params.use_fp16 = False
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sd_model = instantiate_from_config(sd_config.model)
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timer = Timer()
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try:
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with sd_disable_initialization.DisableInitialization():
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sd_model = instantiate_from_config(sd_config.model)
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except Exception as e:
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print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
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sd_model = instantiate_from_config(sd_config.model)
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elapsed_create = timer.elapsed()
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load_model_weights(sd_model, checkpoint_info)
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elapsed_load_weights = timer.elapsed()
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if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
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lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
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else:
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@ -337,7 +360,9 @@ def load_model(checkpoint_info=None):
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script_callbacks.model_loaded_callback(sd_model)
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print("Model loaded.")
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elapsed_the_rest = timer.elapsed()
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print(f"Model loaded in {elapsed_create + elapsed_load_weights + elapsed_the_rest:.1f}s ({elapsed_create:.1f}s create model, {elapsed_load_weights:.1f}s load weights).")
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return sd_model
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@ -348,7 +373,7 @@ def reload_model_weights(sd_model=None, info=None):
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if not sd_model:
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sd_model = shared.sd_model
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if sd_model is None: # previous model load failed
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if sd_model is None: # previous model load failed
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current_checkpoint_info = None
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else:
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current_checkpoint_info = sd_model.sd_checkpoint_info
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@ -370,6 +395,8 @@ def reload_model_weights(sd_model=None, info=None):
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sd_hijack.model_hijack.undo_hijack(sd_model)
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timer = Timer()
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try:
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load_model_weights(sd_model, checkpoint_info)
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except Exception as e:
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@ -383,6 +410,8 @@ def reload_model_weights(sd_model=None, info=None):
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if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
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sd_model.to(devices.device)
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print("Weights loaded.")
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elapsed = timer.elapsed()
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print(f"Weights loaded in {elapsed:.1f}s.")
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return sd_model
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