def get_dummy_components_with_full_downscaling(self, adapter_type="full_adapter_xl"):
        """Get dummy components with x8 VAE downscaling and 3 UNet down blocks.
        These dummy components are intended to fully-exercise the T2I-Adapter
        downscaling behavior.
        """
        torch.manual_seed(0)
        unet = UNet2DConditionModel(
            block_out_channels=(32, 32, 64),
            layers_per_block=2,
            sample_size=32,
            in_channels=4,
            out_channels=4,
            down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D"),
            up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"),
            # SD2-specific config below
            attention_head_dim=2,
            use_linear_projection=True,
            addition_embed_type="text_time",
            addition_time_embed_dim=8,
            transformer_layers_per_block=1,
            projection_class_embeddings_input_dim=80,  # 6 * 8 + 32
            cross_attention_dim=64,
        )
        scheduler = EulerDiscreteScheduler(
            beta_start=0.00085,
            beta_end=0.012,
            steps_offset=1,
            beta_schedule="scaled_linear",
            timestep_spacing="leading",
        )
        torch.manual_seed(0)
        vae = AutoencoderKL(
            block_out_channels=[32, 32, 32, 64],
            in_channels=3,
            out_channels=3,
            down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D", "DownEncoderBlock2D"],
            up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            latent_channels=4,
            sample_size=128,
        )
        torch.manual_seed(0)
        text_encoder_config = CLIPTextConfig(
            bos_token_id=0,
            eos_token_id=2,
            hidden_size=32,
            intermediate_size=37,
            layer_norm_eps=1e-05,
            num_attention_heads=4,
            num_hidden_layers=5,
            pad_token_id=1,
            vocab_size=1000,
            # SD2-specific config below
            hidden_act="gelu",
            projection_dim=32,
        )
        text_encoder = CLIPTextModel(text_encoder_config)
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

        text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
        tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
        if adapter_type == "full_adapter_xl":
            adapter = T2IAdapter(
                in_channels=3,
                channels=[32, 32, 64],
                num_res_blocks=2,
                downscale_factor=16,
                adapter_type=adapter_type,
            )
        elif adapter_type == "multi_adapter":
            adapter = MultiAdapter(
                [
                    T2IAdapter(
                        in_channels=3,
                        channels=[32, 32, 64],
                        num_res_blocks=2,
                        downscale_factor=16,
                        adapter_type="full_adapter_xl",
                    ),
                    T2IAdapter(
                        in_channels=3,
                        channels=[32, 32, 64],
                        num_res_blocks=2,
                        downscale_factor=16,
                        adapter_type="full_adapter_xl",
                    ),
                ]
            )
        else:
            raise ValueError(
                f"Unknown adapter type: {adapter_type}, must be one of 'full_adapter_xl', or 'multi_adapter''"
            )

        components = {
            "adapter": adapter,
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "text_encoder_2": text_encoder_2,
            "tokenizer_2": tokenizer_2,
            # "safety_checker": None,
            "feature_extractor": None,
            "image_encoder": None,
        }
        return components
Get dummy components with x8 VAE downscaling and 3 UNet down blocks.
        These dummy components are intended to fully-exercise the T2I-Adapter
        downscaling behavior.
        
tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py