def METHOD_NAME(self):
    pipe = DiTPipeline.from_pretrained("facebook/DiT-XL-2-512")
    pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
    pipe.to("cuda")
    words = ["vase", "umbrella"]
    ids = pipe.get_label_ids(words)
    generator = torch.manual_seed(0)
    images = pipe(ids, generator=generator, num_inference_steps=25, output_type="np").images
    for word, image in zip(words, images):
        expected_image = load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            f"/dit/{word}_512.npy"
        )
        assert np.abs((expected_image - image).max()) < 1e-1