@@ -315,6 +315,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         use_auth_token = kwargs.pop("use_auth_token", None)
         revision = kwargs.pop("revision", None)
         from_auto_class = kwargs.pop("_from_auto", False)
+        torch_dtype = kwargs.pop("torch_dtype", None)
         subfolder = kwargs.pop("subfolder", None)
 
         user_agent = {"file_type": "model", "framework": "pytorch", "from_auto_class": from_auto_class}
@@ -334,6 +335,12 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
             subfolder=subfolder,
             **kwargs,
         )
+
+        if torch_dtype is not None and not isinstance(torch_dtype, torch.dtype):
+            raise ValueError(f"{torch_dtype} needs to be of type `torch.dtype`, e.g. `torch.float16`, but is {type(torch_dtype)}.")
+        elif torch_dtype is not None:
+            model = model.to(torch_dtype)
+
         model.register_to_config(_name_or_path=pretrained_model_name_or_path)
         # This variable will flag if we're loading a sharded checkpoint. In this case the archive file is just the
         # Load model
 
@@ -146,6 +146,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
         local_files_only = kwargs.pop("local_files_only", False)
         use_auth_token = kwargs.pop("use_auth_token", None)
         revision = kwargs.pop("revision", None)
+        torch_dtype = kwargs.pop("torch_dtype", None)
 
         # 1. Download the checkpoints and configs
         # use snapshot download here to get it working from from_pretrained
@@ -237,12 +238,16 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
 
                 load_method = getattr(class_obj, load_method_name)
 
+                loading_kwargs = {}
+                if issubclass(class_obj, torch.nn.Module):
+                    loading_kwargs["torch_dtype"] = torch_dtype
+
                 # check if the module is in a subdirectory
                 if os.path.isdir(os.path.join(cached_folder, name)):
-                    loaded_sub_model = load_method(os.path.join(cached_folder, name))
+                    loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)
                 else:
                     # else load from the root directory
-                    loaded_sub_model = load_method(cached_folder)
+                    loaded_sub_model = load_method(cached_folder, **loading_kwargs)
 
             init_kwargs[name] = loaded_sub_model  # UNet(...), # DiffusionSchedule(...)