def METHOD_NAME(self):
    torch.manual_seed(0)
    unet = UNet2DConditionModel(
        block_out_channels=(32, 64),
        layers_per_block=2,
        sample_size=32,
        in_channels=4,
        out_channels=4,
        down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
        up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
        # SD2-specific config below
        attention_head_dim=(2, 4),
        use_linear_projection=True,
        addition_embed_type="text_time",
        addition_time_embed_dim=8,
        transformer_layers_per_block=(1, 2),
        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, 64],
        in_channels=3,
        out_channels=3,
        down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
        up_block_types=["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")
    adapter = T2IAdapter(
        in_channels=3,
        channels=[32, 64],
        num_res_blocks=2,
        downscale_factor=4,
        adapter_type="full_adapter_xl",
    )
    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,
    }
    return components