| | 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 __init__(self, disable_clip=True): |
| | self.replaced = [] |
| | self.disable_clip = disable_clip |
| |
|
| | def replace(self, obj, field, func): |
| | original = getattr(obj, field, None) |
| | if original is None: |
| | return None |
| |
|
| | self.replaced.append((obj, field, original)) |
| | setattr(obj, field, func) |
| |
|
| | return original |
| |
|
| | 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): |
| | res = self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs) |
| | res.name_or_path = pretrained_model_name_or_path |
| | return res |
| |
|
| | def transformers_modeling_utils_load_pretrained_model(*args, **kwargs): |
| | args = args[0:3] + ('/', ) + args[4:] |
| | return self.transformers_modeling_utils_load_pretrained_model(*args, **kwargs) |
| |
|
| | def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs): |
| |
|
| | |
| | if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json' or url == 'openai/clip-vit-large-patch14' and args[0] == 'added_tokens.json': |
| | return None |
| |
|
| | try: |
| | res = original(url, *args, local_files_only=True, **kwargs) |
| | if res is None: |
| | res = original(url, *args, local_files_only=False, **kwargs) |
| | return res |
| | 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.replace(torch.nn.init, 'kaiming_uniform_', do_nothing) |
| | self.replace(torch.nn.init, '_no_grad_normal_', do_nothing) |
| | self.replace(torch.nn.init, '_no_grad_uniform_', do_nothing) |
| |
|
| | if self.disable_clip: |
| | self.create_model_and_transforms = self.replace(open_clip, 'create_model_and_transforms', create_model_and_transforms_without_pretrained) |
| | self.CLIPTextModel_from_pretrained = self.replace(ldm.modules.encoders.modules.CLIPTextModel, 'from_pretrained', CLIPTextModel_from_pretrained) |
| | self.transformers_modeling_utils_load_pretrained_model = self.replace(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', transformers_modeling_utils_load_pretrained_model) |
| | self.transformers_tokenization_utils_base_cached_file = self.replace(transformers.tokenization_utils_base, 'cached_file', transformers_tokenization_utils_base_cached_file) |
| | self.transformers_configuration_utils_cached_file = self.replace(transformers.configuration_utils, 'cached_file', transformers_configuration_utils_cached_file) |
| | self.transformers_utils_hub_get_from_cache = self.replace(transformers.utils.hub, 'get_from_cache', transformers_utils_hub_get_from_cache) |
| |
|
| | def __exit__(self, exc_type, exc_val, exc_tb): |
| | for obj, field, original in self.replaced: |
| | setattr(obj, field, original) |
| |
|
| | self.replaced.clear() |
| |
|
| |
|
