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add support for transformers==4.25.1 

add fallback for when quick model creation fails
This commit is contained in:
AUTOMATIC 2023-01-10 17:46:59 +03:00
parent ce3f639ec8
commit 0f8603a559
2 changed files with 42 additions and 8 deletions
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42
modules/sd_disable_initialization.py
View File

@ -30,30 +30,53 @@ class DisableInitialization:
def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):
return self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):
def transformers_modeling_utils_load_pretrained_model(*args, **kwargs):
args = args[0:3] + ('/', ) + args[4:] # resolved_archive_file; must set it to something to prevent what seems to be a bug
return self.transformers_modeling_utils_load_pretrained_model(*args, **kwargs)
def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
# this file is always 404, prevent making request
if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json':
raise transformers.utils.hub.EntryNotFoundError
try:
return self.transformers_utils_hub_get_from_cache(url, *args, local_files_only=True, **kwargs)
return original(url, *args, local_files_only=True, **kwargs)
except Exception as e:
return self.transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs)
return original(url, *args, local_files_only=False, **kwargs)
def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):
return transformers_utils_hub_get_file_from_cache(self.transformers_utils_hub_get_from_cache, url, *args, **kwargs)
def transformers_tokenization_utils_base_cached_file(url, *args, local_files_only=False, **kwargs):
return transformers_utils_hub_get_file_from_cache(self.transformers_tokenization_utils_base_cached_file, url, *args, **kwargs)
def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs):
return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **kwargs)
self.init_kaiming_uniform = torch.nn.init.kaiming_uniform_
self.init_no_grad_normal = torch.nn.init._no_grad_normal_
self.init_no_grad_uniform_ = torch.nn.init._no_grad_uniform_
self.create_model_and_transforms = open_clip.create_model_and_transforms
self.CLIPTextModel_from_pretrained = ldm.modules.encoders.modules.CLIPTextModel.from_pretrained
self.transformers_utils_hub_get_from_cache = transformers.utils.hub.get_from_cache
self.transformers_modeling_utils_load_pretrained_model = getattr(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', None)
self.transformers_tokenization_utils_base_cached_file = getattr(transformers.tokenization_utils_base, 'cached_file', None)
self.transformers_configuration_utils_cached_file = getattr(transformers.configuration_utils, 'cached_file', None)
self.transformers_utils_hub_get_from_cache = getattr(transformers.utils.hub, 'get_from_cache', None)
torch.nn.init.kaiming_uniform_ = do_nothing
torch.nn.init._no_grad_normal_ = do_nothing
torch.nn.init._no_grad_uniform_ = do_nothing
open_clip.create_model_and_transforms = create_model_and_transforms_without_pretrained
ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = CLIPTextModel_from_pretrained
transformers.utils.hub.get_from_cache = transformers_utils_hub_get_from_cache
if self.transformers_modeling_utils_load_pretrained_model is not None:
transformers.modeling_utils.PreTrainedModel._load_pretrained_model = transformers_modeling_utils_load_pretrained_model
if self.transformers_tokenization_utils_base_cached_file is not None:
transformers.tokenization_utils_base.cached_file = transformers_tokenization_utils_base_cached_file
if self.transformers_configuration_utils_cached_file is not None:
transformers.configuration_utils.cached_file = transformers_configuration_utils_cached_file
if self.transformers_utils_hub_get_from_cache is not None:
transformers.utils.hub.get_from_cache = transformers_utils_hub_get_from_cache
def __exit__(self, exc_type, exc_val, exc_tb):
torch.nn.init.kaiming_uniform_ = self.init_kaiming_uniform
@ -61,5 +84,12 @@ class DisableInitialization:
torch.nn.init._no_grad_uniform_ = self.init_no_grad_uniform_
open_clip.create_model_and_transforms = self.create_model_and_transforms
ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = self.CLIPTextModel_from_pretrained
transformers.utils.hub.get_from_cache = self.transformers_utils_hub_get_from_cache
if self.transformers_modeling_utils_load_pretrained_model is not None:
transformers.modeling_utils.PreTrainedModel._load_pretrained_model = self.transformers_modeling_utils_load_pretrained_model
if self.transformers_tokenization_utils_base_cached_file is not None:
transformers.utils.hub.cached_file = self.transformers_tokenization_utils_base_cached_file
if self.transformers_configuration_utils_cached_file is not None:
transformers.utils.hub.cached_file = self.transformers_configuration_utils_cached_file
if self.transformers_utils_hub_get_from_cache is not None:
transformers.utils.hub.get_from_cache = self.transformers_utils_hub_get_from_cache

8
modules/sd_models.py
View File

@ -14,7 +14,7 @@ import ldm.modules.midas as midas
from ldm.util import instantiate_from_config
from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization
from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors
from modules.paths import models_path
from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
@ -333,7 +333,11 @@ def load_model(checkpoint_info=None):
timer = Timer()
with sd_disable_initialization.DisableInitialization():
try:
with sd_disable_initialization.DisableInitialization():
sd_model = instantiate_from_config(sd_config.model)
except Exception as e:
print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
sd_model = instantiate_from_config(sd_config.model)
elapsed_create = timer.elapsed()