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_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="first prompt",
        image=prompt_image,
        text_to_image_strength=0.75,
        generator=generator,
        guidance_scale=7.5,
        num_inference_steps=2,
        output_type="numpy",
    ).images
    with tempfile.TemporaryDirectory() as tmpdirname:
        pipe.save_pretrained(tmpdirname)
        pipe = VersatileDiffusionPipeline.from_pretrained(tmpdirname, torch_dtype=torch.float16)
    pipe.to(torch_device)
    pipe.set_progress_bar_config(disable=None)
    generator = generator.manual_seed(0)
    new_image = pipe.dual_guided(
        prompt="first prompt",
        image=prompt_image,
        text_to_image_strength=0.75,
        generator=generator,
        guidance_scale=7.5,
        num_inference_steps=2,
        output_type="numpy",
    ).images
    assert np.abs(image - new_image).max() < 1e-5, "Models don't have the same forward pass"