remove ddpm test_full_inference (#2291)

* remove ddpm test_full_inference

* style

tests/pipelines/ddpm/test_ddpm.py

@@ -66,32 +66,6 @@ class DDPMPipelineFastTests(unittest.TestCase):
         assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
 
-    def test_full_inference(self):
-        device = "cpu"
-        unet = self.dummy_uncond_unet
-        scheduler = DDPMScheduler()
-
-        ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
-        ddpm.to(device)
-        ddpm.set_progress_bar_config(disable=None)
-
-        generator = torch.Generator(device=device).manual_seed(0)
-        image = ddpm(generator=generator, output_type="numpy").images
-
-        generator = torch.Generator(device=device).manual_seed(0)
-        image_from_tuple = ddpm(generator=generator, output_type="numpy", return_dict=False)[0]
-
-        image_slice = image[0, -3:, -3:, -1]
-        image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
-
-        assert image.shape == (1, 32, 32, 3)
-        expected_slice = np.array(
-            [1.0, 3.495e-02, 2.939e-01, 9.821e-01, 9.448e-01, 6.261e-03, 7.998e-01, 8.9e-01, 1.122e-02]
-        )
-
-        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
-        assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
-
     def test_inference_predict_sample(self):
         unet = self.dummy_uncond_unet
         scheduler = DDPMScheduler(prediction_type="sample")