Fix regression introduced in #2448 (#2551)

* Fix regression introduced in #2448

* Style.

src/diffusers/loaders.py

@@ -150,7 +150,7 @@ class UNet2DConditionLoadersMixin:
 
         model_file = None
         if not isinstance(pretrained_model_name_or_path_or_dict, dict):
-            if is_safetensors_available():
+            if (is_safetensors_available() and weight_name is None) or weight_name.endswith(".safetensors"):
                 if weight_name is None:
                     weight_name = LORA_WEIGHT_NAME_SAFE
                 try:

tests/models/test_models_unet_2d_condition.py

@@ -445,6 +445,43 @@ class UNet2DConditionModelTests(ModelTesterMixin, unittest.TestCase):
         # LoRA and no LoRA should NOT be the same
         assert (sample - old_sample).abs().max() > 1e-4
 
+    def test_lora_save_load_safetensors_load_torch(self):
+        # enable deterministic behavior for gradient checkpointing
+        init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
+
+        init_dict["attention_head_dim"] = (8, 16)
+
+        torch.manual_seed(0)
+        model = self.model_class(**init_dict)
+        model.to(torch_device)
+
+        lora_attn_procs = {}
+        for name in model.attn_processors.keys():
+            cross_attention_dim = None if name.endswith("attn1.processor") else model.config.cross_attention_dim
+            if name.startswith("mid_block"):
+                hidden_size = model.config.block_out_channels[-1]
+            elif name.startswith("up_blocks"):
+                block_id = int(name[len("up_blocks.")])
+                hidden_size = list(reversed(model.config.block_out_channels))[block_id]
+            elif name.startswith("down_blocks"):
+                block_id = int(name[len("down_blocks.")])
+                hidden_size = model.config.block_out_channels[block_id]
+
+            lora_attn_procs[name] = LoRACrossAttnProcessor(
+                hidden_size=hidden_size, cross_attention_dim=cross_attention_dim
+            )
+            lora_attn_procs[name] = lora_attn_procs[name].to(model.device)
+
+        model.set_attn_processor(lora_attn_procs)
+        # Saving as torch, properly reloads with directly filename
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            model.save_attn_procs(tmpdirname)
+            self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.bin")))
+            torch.manual_seed(0)
+            new_model = self.model_class(**init_dict)
+            new_model.to(torch_device)
+            new_model.load_attn_procs(tmpdirname, weight_name="pytorch_lora_weights.bin")
+
     def test_lora_on_off(self):
         # enable deterministic behavior for gradient checkpointing
         init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()