[Transformer2DModel] Handle `norm_type` safely while remapping (#8370)

* handle norm_type of transformer2d_model safely.

* log an info when old model class is being returned.

* Apply suggestions from code review
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

* remove extra stuff

---------
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>

src/diffusers/models/model_loading_utils.py

@@ -71,18 +71,22 @@ def _determine_device_map(model: torch.nn.Module, device_map, max_memory, torch_
 
 def _fetch_remapped_cls_from_config(config, old_class):
     previous_class_name = old_class.__name__
-    remapped_class_name = _CLASS_REMAPPING_DICT.get(previous_class_name).get(config["norm_type"])
-
-    # load diffusers library to import compatible and original scheduler
-    diffusers_library = importlib.import_module(__name__.split(".")[0])
-    remapped_class = getattr(diffusers_library, remapped_class_name)
-    logger.info(
-        f"Changing class object to be of `{remapped_class_name}` type from `{previous_class_name}` type."
-        "This is because `previous_class_name` is scheduled to be deprecated in a future version. Note that this"
-        " DOESN'T affect the final results."
-    )
-
-    return remapped_class
+    remapped_class_name = _CLASS_REMAPPING_DICT.get(previous_class_name).get(config["norm_type"], None)
+
+    # Details:
+    # https://github.com/huggingface/diffusers/pull/7647#discussion_r1621344818
+    if remapped_class_name:
+        # load diffusers library to import compatible and original scheduler
+        diffusers_library = importlib.import_module(__name__.split(".")[0])
+        remapped_class = getattr(diffusers_library, remapped_class_name)
+        logger.info(
+            f"Changing class object to be of `{remapped_class_name}` type from `{previous_class_name}` type."
+            f"This is because `{previous_class_name}` is scheduled to be deprecated in a future version. Note that this"
+            " DOESN'T affect the final results."
+        )
+        return remapped_class
+    else:
+        return old_class
 
 
 def load_state_dict(checkpoint_file: Union[str, os.PathLike], variant: Optional[str] = None):