def test_simple_inference_with_text_denoiser_block_scale(self):
        """
        Tests a simple inference with lora attached to text encoder and unet, attaches
        one adapter and set different weights for different blocks (i.e. block lora)
        """
        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]

            pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
            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, "adapter-1")
                    self.assertTrue(
                        check_if_lora_correctly_set(pipe.text_encoder_2), "Lora not correctly set in text encoder 2"
                    )

            weights_1 = {"text_encoder": 2, "unet": {"down": 5}}
            pipe.set_adapters("adapter-1", weights_1)
            output_weights_1 = pipe(**inputs, generator=torch.manual_seed(0))[0]

            weights_2 = {"unet": {"up": 5}}
            pipe.set_adapters("adapter-1", weights_2)
            output_weights_2 = pipe(**inputs, generator=torch.manual_seed(0))[0]

            self.assertFalse(
                np.allclose(output_weights_1, output_weights_2, atol=1e-3, rtol=1e-3),
                "LoRA weights 1 and 2 should give different results",
            )
            self.assertFalse(
                np.allclose(output_no_lora, output_weights_1, atol=1e-3, rtol=1e-3),
                "No adapter and LoRA weights 1 should give different results",
            )
            self.assertFalse(
                np.allclose(output_no_lora, output_weights_2, atol=1e-3, rtol=1e-3),
                "No adapter and LoRA weights 2 should give different results",
            )

            pipe.disable_lora()
            output_disabled = pipe(**inputs, generator=torch.manual_seed(0))[0]

            self.assertTrue(
                np.allclose(output_no_lora, output_disabled, atol=1e-3, rtol=1e-3),
                "output with no lora and output with lora disabled should give same results",
            )

        Tests a simple inference with lora attached to text encoder and unet, attaches
        one adapter and set different weights for different blocks (i.e. block lora)
        
tests/lora/utils.py