def test_stable_diffusion_v_pred_dpm(self):
        """
        TODO: update this test after making DPM compatible with V-prediction!
        """
        scheduler = DPMSolverMultistepScheduler.from_pretrained(
            "stabilityai/stable-diffusion-2",
            subfolder="scheduler",
            final_sigmas_type="sigma_min",
        )
        sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", scheduler=scheduler)
        sd_pipe = sd_pipe.to(torch_device)
        sd_pipe.enable_attention_slicing()
        sd_pipe.set_progress_bar_config(disable=None)

        prompt = "a photograph of an astronaut riding a horse"
        generator = torch.manual_seed(0)
        image = sd_pipe(
            [prompt], generator=generator, guidance_scale=7.5, num_inference_steps=5, output_type="np"
        ).images

        image_slice = image[0, 253:256, 253:256, -1]
        assert image.shape == (1, 768, 768, 3)
        expected_slice = np.array([0.3303, 0.3184, 0.3291, 0.3300, 0.3256, 0.3113, 0.2965, 0.3134, 0.3192])

        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

        TODO: update this test after making DPM compatible with V-prediction!
        
tests/pipelines/stable_diffusion_2/test_stable_diffusion_v_pred.py