diff options
Diffstat (limited to 'modules/sd_disable_initialization.py')
-rw-r--r-- | modules/sd_disable_initialization.py | 95 |
1 files changed, 95 insertions, 0 deletions
diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py new file mode 100644 index 00000000..088ac24b --- /dev/null +++ b/modules/sd_disable_initialization.py @@ -0,0 +1,95 @@ +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
+
|