Fix loading sharded checkpoints when we have variants (huggingface#9061)

* Fix loading sharded checkpoint when we have variant

* add test

* remote print

---------

Co-authored-by: Sayak Paul <[email protected]>

Author: SunMarc

src/diffusers/models/modeling_utils.py

@@ -773,7 +773,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                     try:
                         accelerate.load_checkpoint_and_dispatch(
                             model,
-                            model_file if not is_sharded else sharded_ckpt_cached_folder,
+                            model_file if not is_sharded else index_file,
                             device_map,
                             max_memory=max_memory,
                             offload_folder=offload_folder,
@@ -803,7 +803,7 @@ def from_pretrained(cls, pretrained_model_name_or_path: Optional[Union[str, os.P
                             model._temp_convert_self_to_deprecated_attention_blocks()
                             accelerate.load_checkpoint_and_dispatch(
                                 model,
-                                model_file if not is_sharded else sharded_ckpt_cached_folder,
+                                model_file if not is_sharded else index_file,
                                 device_map,
                                 max_memory=max_memory,
                                 offload_folder=offload_folder,

tests/models/unets/test_models_unet_2d_condition.py

@@ -1121,6 +1121,18 @@ def test_load_sharded_checkpoint_device_map_from_hub_local_subfolder(self):
         assert loaded_model
         assert new_output.sample.shape == (4, 4, 16, 16)
 
+    @require_torch_gpu
+    def test_load_sharded_checkpoint_with_variant_from_hub(self):
+        _, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+        loaded_model = self.model_class.from_pretrained(
+            "hf-internal-testing/unet2d-sharded-with-variant-dummy", variant="fp16"
+        )
+        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_peft_backend
     def test_lora(self):
         init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()