def METHOD_NAME(self):
    torch.manual_seed(0)
    unet = UNet3DConditionModel(
        block_out_channels=(32, 64, 64, 64),
        layers_per_block=2,
        sample_size=32,
        in_channels=4,
        out_channels=4,
        down_block_types=("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D"),
        up_block_types=("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D"),
        cross_attention_dim=32,
        attention_head_dim=4,
    )
    scheduler = DDIMScheduler(
        beta_start=0.00085,
        beta_end=0.012,
        beta_schedule="scaled_linear",
        clip_sample=False,
        set_alpha_to_one=False,
    )
    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,
        hidden_act="gelu",
        projection_dim=512,
    )
    text_encoder = CLIPTextModel(text_encoder_config)
    tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
    components = {
        "unet": unet,
        "scheduler": scheduler,
        "vae": vae,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
    }
    return components