def test_set_adapters_match_attention_kwargs(self):
        """Test to check if outputs after `set_adapters()` and attention kwargs match."""
        attention_kwargs_name = determine_attention_kwargs_name(self.pipeline_class)

        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)

            pipe, _ = self.check_if_adapters_added_correctly(pipe, text_lora_config, denoiser_lora_config)

            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().",
                )
Test to check if outputs after `set_adapters()` and attention kwargs match.
tests/lora/utils.py