[LoRA] add a test to ensure set_adapters() and attn kwargs outs match

tests/lora/utils.py

@@ -76,6 +76,9 @@ def initialize_dummy_state_dict(state_dict):
     return {k: torch.randn(v.shape, device=torch_device, dtype=v.dtype) for k, v in state_dict.items()}
 
 
+POSSIBLE_ATTENTION_KWARGS_NAMES = ["cross_attention_kwargs", "joint_attention_kwargs", "attention_kwargs"]
+
+
 @require_peft_backend
 class PeftLoraLoaderMixinTests:
     pipeline_class = None
@@ -429,7 +432,7 @@ def test_simple_inference_with_text_lora_and_scale(self):
         call_signature_keys = inspect.signature(self.pipeline_class.__call__).parameters.keys()
 
         # TODO(diffusers): Discuss a common naming convention across library for 1.0.0 release
-        for possible_attention_kwargs in ["cross_attention_kwargs", "joint_attention_kwargs", "attention_kwargs"]:
+        for possible_attention_kwargs in POSSIBLE_ATTENTION_KWARGS_NAMES:
             if possible_attention_kwargs in call_signature_keys:
                 attention_kwargs_name = possible_attention_kwargs
                 break
@@ -790,7 +793,7 @@ def test_simple_inference_with_text_denoiser_lora_and_scale(self):
         and makes sure it works as expected
         """
         call_signature_keys = inspect.signature(self.pipeline_class.__call__).parameters.keys()
-        for possible_attention_kwargs in ["cross_attention_kwargs", "joint_attention_kwargs", "attention_kwargs"]:
+        for possible_attention_kwargs in POSSIBLE_ATTENTION_KWARGS_NAMES:
             if possible_attention_kwargs in call_signature_keys:
                 attention_kwargs_name = possible_attention_kwargs
                 break
@@ -1885,3 +1888,88 @@ def set_pad_mode(network, mode="circular"):
 
             _, _, inputs = self.get_dummy_inputs()
             _ = pipe(**inputs)[0]
+
+    def test_set_adapters_match_attention_kwargs(self):
+        """Test to check if outputs after `set_adapters()` and attention kwargs match."""
+        call_signature_keys = inspect.signature(self.pipeline_class.__call__).parameters.keys()
+        for possible_attention_kwargs in POSSIBLE_ATTENTION_KWARGS_NAMES:
+            if possible_attention_kwargs in call_signature_keys:
+                attention_kwargs_name = possible_attention_kwargs
+                break
+        assert attention_kwargs_name is not None
+
+        for scheduler_cls in self.scheduler_classes:
+            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+            pipe = self.pipeline_class(**components)
+            pipe = pipe.to(torch_device)
+            pipe.set_progress_bar_config(disable=None)
+            _, _, inputs = self.get_dummy_inputs(with_generator=False)
+
+            output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
+            self.assertTrue(output_no_lora.shape == self.output_shape)
+
+            if "text_encoder" in self.pipeline_class._lora_loadable_modules:
+                pipe.text_encoder.add_adapter(text_lora_config)
+                self.assertTrue(
+                    check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
+                )
+
+            denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
+            denoiser.add_adapter(denoiser_lora_config)
+            self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
+
+            if self.has_two_text_encoders or self.has_three_text_encoders:
+                if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
+                    pipe.text_encoder_2.add_adapter(text_lora_config)
+                    self.assertTrue(
+                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
+                    )
+
+            lora_scale = 0.5
+            attention_kwargs = {attention_kwargs_name: {"scale": lora_scale}}
+            output_lora_scale = pipe(**inputs, generator=torch.manual_seed(0), **attention_kwargs)[0]
+            self.assertFalse(
+                np.allclose(output_no_lora, output_lora_scale, atol=1e-3, rtol=1e-3),
+                "Lora + scale should change the output",
+            )
+
+            pipe.set_adapters("default", lora_scale)
+            output_lora_scale_wo_kwargs = pipe(**inputs, generator=torch.manual_seed(0))[0]
+            self.assertTrue(
+                not np.allclose(output_no_lora, output_lora_scale_wo_kwargs, atol=1e-3, rtol=1e-3),
+                "Lora + scale should change the output",
+            )
+            self.assertTrue(
+                np.allclose(output_lora_scale, output_lora_scale_wo_kwargs, atol=1e-3, rtol=1e-3),
+                "Lora + scale should match the output of `set_adapters()`.",
+            )
+
+            with tempfile.TemporaryDirectory() as tmpdirname:
+                modules_to_save = self._get_modules_to_save(pipe, has_denoiser=True)
+                lora_state_dicts = self._get_lora_state_dicts(modules_to_save)
+                self.pipeline_class.save_lora_weights(
+                    save_directory=tmpdirname, safe_serialization=True, **lora_state_dicts
+                )
+
+                self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors")))
+                pipe = self.pipeline_class(**components)
+                pipe = pipe.to(torch_device)
+                pipe.set_progress_bar_config(disable=None)
+                pipe.load_lora_weights(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors"))
+
+                for module_name, module in modules_to_save.items():
+                    self.assertTrue(check_if_lora_correctly_set(module), f"Lora not correctly set in {module_name}")
+
+                output_lora_from_pretrained = pipe(**inputs, generator=torch.manual_seed(0), **attention_kwargs)[0]
+                self.assertTrue(
+                    not np.allclose(output_no_lora, output_lora_from_pretrained, atol=1e-3, rtol=1e-3),
+                    "Lora + scale should change the output",
+                )
+                self.assertTrue(
+                    np.allclose(output_lora_scale, output_lora_from_pretrained, atol=1e-3, rtol=1e-3),
+                    "Loading from saved checkpoints should give same results as attention_kwargs.",
+                )
+                self.assertTrue(
+                    np.allclose(output_lora_scale_wo_kwargs, output_lora_from_pretrained, atol=1e-3, rtol=1e-3),
+                    "Loading from saved checkpoints should give same results as set_adapters().",
+                )