Update checkpoint_merger pipeline to pass the "variant" argument (#6670)

* make checkpoint_merger pipeline pass the "variant" argument to from_pretrained()

* make style

---------

Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: YiYi Xu <yixu310@gmail.com>

examples/community/checkpoint_merger.py

@@ -81,6 +81,8 @@ def merge(self, pretrained_model_name_or_path_list: List[Union[str, os.PathLike]
 
                 force - Whether to ignore mismatch in model_config.json for the current models. Defaults to False.
 
+                variant - which variant of a pretrained model to load, e.g. "fp16" (None)
+
         """
         # Default kwargs from DiffusionPipeline
         cache_dir = kwargs.pop("cache_dir", None)
@@ -89,6 +91,7 @@ def merge(self, pretrained_model_name_or_path_list: List[Union[str, os.PathLike]
         proxies = kwargs.pop("proxies", None)
         local_files_only = kwargs.pop("local_files_only", False)
         token = kwargs.pop("token", None)
+        variant = kwargs.pop("variant", None)
         revision = kwargs.pop("revision", None)
         torch_dtype = kwargs.pop("torch_dtype", None)
         device_map = kwargs.pop("device_map", None)
@@ -173,7 +176,10 @@ def merge(self, pretrained_model_name_or_path_list: List[Union[str, os.PathLike]
         # Step 3:-
         # Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
         final_pipe = DiffusionPipeline.from_pretrained(
-            cached_folders[0], torch_dtype=torch_dtype, device_map=device_map
+            cached_folders[0],
+            torch_dtype=torch_dtype,
+            device_map=device_map,
+            variant=variant,
         )
         final_pipe.to(self.device)