from suggestions

Author: SunMarc

src/diffusers/configuration_utils.py

@@ -361,7 +361,12 @@ def load_config(
             )
         # Custom path for now
         if dduf_entries:
-            config_file = cls._get_config_file_from_dduf(pretrained_model_name_or_path, subfolder, dduf_entries)
+            if subfolder is not None:
+                raise ValueError(
+                    "DDUF file only allow for 1 level of directory (e.g transformer/model1/model.safetentors is not allowed). "
+                    "Please check the DDUF structure"
+                )
+            config_file = cls._get_config_file_from_dduf(pretrained_model_name_or_path, dduf_entries)
         elif os.path.isfile(pretrained_model_name_or_path):
             config_file = pretrained_model_name_or_path
         elif os.path.isdir(pretrained_model_name_or_path):
@@ -623,14 +628,7 @@ def to_json_file(self, json_file_path: Union[str, os.PathLike]):
             writer.write(self.to_json_string())
 
     @classmethod
-    def _get_config_file_from_dduf(
-        cls, pretrained_model_name_or_path: str, subfolder: str, dduf_entries: Dict[str, DDUFEntry]
-    ):
-        if subfolder is not None:
-            raise ValueError(
-                "DDUF file only allow for 1 level of directory (e.g transformer/model1/model.safetentors is not allowed). "
-                "Please check the DDUF structure"
-            )
+    def _get_config_file_from_dduf(cls, pretrained_model_name_or_path: str, dduf_entries: Dict[str, DDUFEntry]):
         # paths inside a DDUF file must always be "/"
         config_file = (
             cls.config_name

src/diffusers/models/model_loading_utils.py

@@ -28,6 +28,7 @@
 from huggingface_hub.utils import EntryNotFoundError
 
 from ..utils import (
+    GGUF_FILE_EXTENSION,
     SAFE_WEIGHTS_INDEX_NAME,
     SAFETENSORS_FILE_EXTENSION,
     WEIGHTS_INDEX_NAME,
@@ -152,7 +153,8 @@ def load_state_dict(
                     return safetensors.torch.load(mm)
             else:
                 return safetensors.torch.load_file(checkpoint_file, device="cpu")
-
+        elif file_extension == GGUF_FILE_EXTENSION:
+            return load_gguf_checkpoint(checkpoint_file)
         else:
             weights_only_kwarg = {"weights_only": True} if is_torch_version(">=", "1.13") else {}
             return torch.load(