diff --git a/src/diffusers/schedulers/scheduling_ddim.py b/src/diffusers/schedulers/scheduling_ddim.py
index 75cef635d063..10494ca9ab26 100644
--- a/src/diffusers/schedulers/scheduling_ddim.py
+++ b/src/diffusers/schedulers/scheduling_ddim.py
@@ -300,10 +300,14 @@ def step(
                     # randn does not work reproducibly on mps
                     variance_noise = torch.randn(model_output.shape, dtype=model_output.dtype, generator=generator)
                     variance_noise = variance_noise.to(device)
-                else:
+                elif generator is not None and generator.device.type == device.type:
                     variance_noise = torch.randn(
                         model_output.shape, generator=generator, device=device, dtype=model_output.dtype
                     )
+                else:
+                    variance_noise = torch.randn(model_output.shape, generator=generator, dtype=model_output.dtype)
+                        variance_noise = variance_noise.to(device)
+
             variance = self._get_variance(timestep, prev_timestep) ** (0.5) * eta * variance_noise
 
             prev_sample = prev_sample + variance
diff --git a/tests/pipelines/ddim/test_ddim.py b/tests/pipelines/ddim/test_ddim.py
index 4445fe7feecf..288ae844e17c 100644
--- a/tests/pipelines/ddim/test_ddim.py
+++ b/tests/pipelines/ddim/test_ddim.py
@@ -71,6 +71,30 @@ def test_inference(self):
         assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance
 
+    def test_inference_eta(self):
+        unet = self.dummy_uncond_unet
+        scheduler = DDIMScheduler()
+
+        ddim = DDIMPipeline(unet=unet, scheduler=scheduler)
+        ddim.to(torch_device)
+        ddim.set_progress_bar_config(disable=None)
+
+        # Warmup pass when using mps (see #372)
+        if torch_device == "mps":
+            _ = ddim(num_inference_steps=1)
+
+        generator = torch.manual_seed(0)
+        # set eta > 0 to test the variance noise generation
+        image = ddim(generator=generator, eta=1.0, num_inference_steps=2, output_type="numpy").images
+        image_slice = image[0, -3:, -3:, -1]
+
+        assert image.shape == (1, 32, 32, 3)
+        expected_slice = np.array(
+            [[9.955e-01, 5.785e-01, 4.674e-01, 9.93e-01, 0.0e00, 1.0e00, 1.2e-03, 2.73e-04, 5.19e-04]]
+        )
+        tolerance = 1e-2 if torch_device != "mps" else 3e-2
+        assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
+
 
 @slow
 @require_torch
diff --git a/tests/test_pipelines.py b/tests/test_pipelines.py
index 753c821dd315..34f5acccc316 100644
--- a/tests/test_pipelines.py
+++ b/tests/test_pipelines.py
@@ -42,7 +42,6 @@
 from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
 from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, slow, torch_device
 from diffusers.utils.testing_utils import CaptureLogger, get_tests_dir, require_torch_gpu
-from parameterized import parameterized
 from PIL import Image
 from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
 
@@ -462,40 +461,9 @@ def test_output_format(self):
         assert isinstance(images, list)
         assert isinstance(images[0], PIL.Image.Image)
 
-    # Make sure the test passes for different values of random seed
-    @parameterized.expand([(0,), (4,)])
-    def test_ddpm_ddim_equality(self, seed):
-        model_id = "google/ddpm-cifar10-32"
-
-        unet = UNet2DModel.from_pretrained(model_id)
-        ddpm_scheduler = DDPMScheduler()
-        ddim_scheduler = DDIMScheduler()
-
-        ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
-        ddpm.to(torch_device)
-        ddpm.set_progress_bar_config(disable=None)
-        ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
-        ddim.to(torch_device)
-        ddim.set_progress_bar_config(disable=None)
-
-        generator = torch.manual_seed(seed)
-        ddpm_image = ddpm(generator=generator, output_type="numpy").images
-
-        generator = torch.manual_seed(seed)
-        ddim_image = ddim(
-            generator=generator,
-            num_inference_steps=1000,
-            eta=1.0,
-            output_type="numpy",
-            use_clipped_model_output=True,  # Need this to make DDIM match DDPM
-        ).images
-
-        # the values aren't exactly equal, but the images look the same visually
-        assert np.abs(ddpm_image - ddim_image).max() < 1e-1
-
-    # Make sure the test passes for different values of random seed
-    @parameterized.expand([(0,), (4,)])
-    def test_ddpm_ddim_equality_batched(self, seed):
+    def test_ddpm_ddim_equality_batched(self):
+        seed = 0
+        batch_size = 2
         model_id = "google/ddpm-cifar10-32"
 
         unet = UNet2DModel.from_pretrained(model_id)
@@ -511,11 +479,11 @@ def test_ddpm_ddim_equality_batched(self, seed):
         ddim.set_progress_bar_config(disable=None)
 
         generator = torch.manual_seed(seed)
-        ddpm_images = ddpm(batch_size=4, generator=generator, output_type="numpy").images
+        ddpm_images = ddpm(batch_size=batch_size, generator=generator, output_type="numpy").images
 
         generator = torch.manual_seed(seed)
         ddim_images = ddim(
-            batch_size=4,
+            batch_size=batch_size,
             generator=generator,
             num_inference_steps=1000,
             eta=1.0,
@@ -523,5 +491,4 @@ def test_ddpm_ddim_equality_batched(self, seed):
             use_clipped_model_output=True,  # Need this to make DDIM match DDPM
         ).images
 
-        # the values aren't exactly equal, but the images look the same visually
         assert np.abs(ddpm_images - ddim_images).max() < 1e-1