[LoRA] depcrecate save_attn_procs(). (#10126)

depcrecate save_attn_procs().

src/diffusers/loaders/unet.py

@@ -492,6 +492,9 @@ class UNet2DConditionLoadersMixin:
                     )
                 state_dict = {k: v for k, v in state_dict.items() if isinstance(v, torch.Tensor)}
         else:
+            deprecation_message = "Using the `save_attn_procs()` method has been deprecated and will be removed in a future version. Please use `save_lora_adapter()`."
+            deprecate("save_attn_procs", "0.40.0", deprecation_message)
+
             if not USE_PEFT_BACKEND:
                 raise ValueError("PEFT backend is required for saving LoRAs using the `save_attn_procs()` method.")
 

tests/models/unets/test_models_unet_2d_condition.py

@@ -1119,6 +1119,24 @@ class UNet2DConditionModelTests(ModelTesterMixin, UNetTesterMixin, unittest.Test
             lora_sample_1, lora_sample_2, atol=1e-4, rtol=1e-4
         ), "Loading from a saved checkpoint should produce identical results."
 
+    @require_peft_backend
+    def test_save_attn_procs_raise_warning(self):
+        init_dict, _ = self.prepare_init_args_and_inputs_for_common()
+        model = self.model_class(**init_dict)
+        model.to(torch_device)
+
+        unet_lora_config = get_unet_lora_config()
+        model.add_adapter(unet_lora_config)
+
+        assert check_if_lora_correctly_set(model), "Lora not correctly set in UNet."
+
+        with tempfile.TemporaryDirectory() as tmpdirname:
+            with self.assertWarns(FutureWarning) as warning:
+                model.save_attn_procs(tmpdirname)
+
+        warning_message = str(warning.warnings[0].message)
+        assert "Using the `save_attn_procs()` method has been deprecated" in warning_message
+
 
 @slow
 class UNet2DConditionModelIntegrationTests(unittest.TestCase):