def METHOD_NAME(self):
    unet = self.dummy_uncond_unet
    scheduler = DDPMScheduler(prediction_type="sample")
    ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
    ddpm.to(torch_device)
    ddpm.set_progress_bar_config(disable=None)
    generator = torch.manual_seed(0)
    image = ddpm(generator=generator, num_inference_steps=2, output_type="numpy").images
    generator = torch.manual_seed(0)
    image_eps = ddpm(generator=generator, num_inference_steps=2, output_type="numpy")[0]
    image_slice = image[0, -3:, -3:, -1]
    image_eps_slice = image_eps[0, -3:, -3:, -1]
    assert image.shape == (1, 32, 32, 3)
    tolerance = 1e-2 if torch_device != "mps" else 3e-2
    assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance