fix loading sharded checkpoints from subfolder

src/diffusers/models/model_loading_utils.py

@@ -221,7 +221,7 @@ def _fetch_index_file(
                 local_files_only=local_files_only,
                 token=token,
                 revision=revision,
-                subfolder=subfolder,
+                subfolder=None,
                 user_agent=user_agent,
                 commit_hash=commit_hash,
             )

src/diffusers/utils/hub_utils.py

@@ -455,10 +455,13 @@ def _get_checkpoint_shard_files(
 
     # At this stage pretrained_model_name_or_path is a model identifier on the Hub
     allow_patterns = original_shard_filenames
+    if subfolder is not None:
+        allow_patterns = [os.path.join(subfolder, p) for p in allow_patterns]
+
     ignore_patterns = ["*.json", "*.md"]
     if not local_files_only:
         # `model_info` call must guarded with the above condition.
-        model_files_info = model_info(pretrained_model_name_or_path)
+        model_files_info = model_info(pretrained_model_name_or_path, revision=revision)
         for shard_file in original_shard_filenames:
             shard_file_present = any(shard_file in k.rfilename for k in model_files_info.siblings)
             if not shard_file_present:
@@ -481,6 +484,8 @@ def _get_checkpoint_shard_files(
             ignore_patterns=ignore_patterns,
             user_agent=user_agent,
         )
+        if subfolder is not None:
+            cached_folder = os.path.join(cached_folder, subfolder)
 
     # We have already dealt with RepositoryNotFoundError and RevisionNotFoundError when getting the index, so
     # we don't have to catch them here. We have also dealt with EntryNotFoundError.

tests/models/unets/test_models_unet_2d_condition.py

@@ -1045,6 +1045,18 @@ def test_load_sharded_checkpoint_from_hub(self):
         assert loaded_model
         assert new_output.sample.shape == (4, 4, 16, 16)
 
+    @require_torch_gpu
+    def test_load_sharded_checkpoint_from_hub_subfolder(self):
+        _, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        loaded_model = self.model_class.from_pretrained(
+            "hf-internal-testing/unet2d-sharded-dummy-subfolder", subfolder="unet"
+        )
+        loaded_model = loaded_model.to(torch_device)
+        new_output = loaded_model(**inputs_dict)
+
+        assert loaded_model
+        assert new_output.sample.shape == (4, 4, 16, 16)
+
     @require_torch_gpu
     def test_load_sharded_checkpoint_from_hub_local(self):
         _, inputs_dict = self.prepare_init_args_and_inputs_for_common()