def METHOD_NAME(self):
    torch.manual_seed(0)
    transformer = Transformer2DModel(
        sample_size=16,
        num_layers=2,
        patch_size=4,
        attention_head_dim=8,
        num_attention_heads=2,
        in_channels=4,
        out_channels=8,
        attention_bias=True,
        activation_fn="gelu-approximate",
        num_embeds_ada_norm=1000,
        norm_type="ada_norm_zero",
        norm_elementwise_affine=False,
    )
    vae = AutoencoderKL()
    scheduler = DDIMScheduler()
    components = {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
    return components