Merge branch 'master' into tensorboard

1 files changed, 88 insertions, 0 deletions

diff --git a/modules/sd_disable_initialization.py b/modules/sd_disable_initialization.py
new file mode 100644
index 00000000..c72d8efc
--- /dev/null
+++ b/modules/sd_disable_initialization.py
@@ -0,0 +1,88 @@
+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):

+        self.replaced = []

+

+    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):

+            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' 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)

+        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()

+