def test_simple_inference_with_text_lora_fused(self):
        """
        Tests a simple inference with lora attached into text encoder + fuses the lora weights into base model
        and makes sure it works as expected
        """
        for scheduler_cls in self.scheduler_classes:
            components, text_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=None)

            pipe.fuse_lora()
            # Fusing should still keep the LoRA layers
            self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")

            if self.has_two_text_encoders or self.has_three_text_encoders:
                if "text_encoder_2" in self.pipeline_class._lora_loadable_modules:
                    self.assertTrue(
                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                    )

            ouput_fused = pipe(**inputs, generator=torch.manual_seed(0))[0]
            self.assertFalse(
                np.allclose(ouput_fused, output_no_lora, atol=1e-3, rtol=1e-3), "Fused lora should change the output"
            )

        Tests a simple inference with lora attached into text encoder + fuses the lora weights into base model
        and makes sure it works as expected
        
tests/lora/utils.py