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import ldm.modules.encoders.modules
import open_clip
import torch
import transformers.utils.hub
class DisableInitialization:
"""
When an object of this class enters a `with` block, it starts:
- preventing torch's layer initialization functions from working
- changes CLIP and OpenCLIP to not download model weights
- changes CLIP to not make requests to check if there is a new version of a file you already have
When it leaves the block, it reverts everything to how it was before.
Use it like this:
```
with DisableInitialization():
do_things()
```
"""
def __enter__(self):
def do_nothing(*args, **kwargs):
pass
def create_model_and_transforms_without_pretrained(*args, pretrained=None, **kwargs):
return self.create_model_and_transforms(*args, pretrained=None, **kwargs)
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_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 original(url, *args, local_files_only=True, **kwargs)
except Exception as e:
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_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
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
torch.nn.init._no_grad_normal_ = self.init_no_grad_normal
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
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
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