def METHOD_NAME(self):
    pipe = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion", torch_dtype=torch.float16)
    pipe.to(torch_device)
    pipe.set_progress_bar_config(disable=None)
    prompt = "cyberpunk 2077"
    init_image = load_image(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"
    )
    generator = torch.manual_seed(0)
    image = pipe.dual_guided(
        prompt=prompt,
        image=init_image,
        text_to_image_strength=0.75,
        generator=generator,
        guidance_scale=7.5,
        num_inference_steps=50,
        output_type="numpy",
    ).images
    image_slice = image[0, 253:256, 253:256, -1]
    assert image.shape == (1, 512, 512, 3)
    expected_slice = np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001])
    assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
    prompt = "A painting of a squirrel eating a burger "
    generator = torch.manual_seed(0)
    image = pipe.text_to_image(
        prompt=prompt, generator=generator, guidance_scale=7.5, num_inference_steps=50, output_type="numpy"
    ).images
    image_slice = image[0, 253:256, 253:256, -1]
    assert image.shape == (1, 512, 512, 3)
    expected_slice = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778])
    assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1
    image = pipe.image_variation(init_image, generator=generator, output_type="numpy").images
    image_slice = image[0, 253:256, 253:256, -1]
    assert image.shape == (1, 512, 512, 3)
    expected_slice = np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456])
    assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-1