diff --git a/tests/models/autoencoders/test_models_vae.py b/tests/models/autoencoders/test_models_vae.py
index ef9dca9fef..e70874f23b 100644
--- a/tests/models/autoencoders/test_models_vae.py
+++ b/tests/models/autoencoders/test_models_vae.py
@@ -1118,8 +1118,10 @@ class ConsistencyDecoderVAEIntegrationTests(unittest.TestCase):
         assert torch_all_close(actual_output, expected_output, atol=5e-3)
 
     def test_vae_tiling(self):
-        vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder")
-        pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", vae=vae, safety_checker=None)
+        vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder", torch_dtype=torch.float16)
+        pipe = StableDiffusionPipeline.from_pretrained(
+            "runwayml/stable-diffusion-v1-5", vae=vae, safety_checker=None, torch_dtype=torch.float16
+        )
         pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
 
@@ -1143,6 +1145,7 @@ class ConsistencyDecoderVAEIntegrationTests(unittest.TestCase):
 
         # test that tiled decode works with various shapes
         shapes = [(1, 4, 73, 97), (1, 4, 97, 73), (1, 4, 49, 65), (1, 4, 65, 49)]
-        for shape in shapes:
-            image = torch.zeros(shape, device=torch_device)
-            pipe.vae.decode(image)
+        with torch.no_grad():
+            for shape in shapes:
+                image = torch.zeros(shape, device=torch_device)
+                pipe.vae.decode(image)
diff --git a/tests/pipelines/test_pipelines_common.py b/tests/pipelines/test_pipelines_common.py
index 41292ec963..67433705d2 100644
--- a/tests/pipelines/test_pipelines_common.py
+++ b/tests/pipelines/test_pipelines_common.py
@@ -124,9 +124,10 @@ class SDFunctionTesterMixin:
 
         # test that tiled decode works with various shapes
         shapes = [(1, 4, 73, 97), (1, 4, 97, 73), (1, 4, 49, 65), (1, 4, 65, 49)]
-        for shape in shapes:
-            zeros = torch.zeros(shape).to(torch_device)
-            pipe.vae.decode(zeros)
+        with torch.no_grad():
+            for shape in shapes:
+                zeros = torch.zeros(shape).to(torch_device)
+                pipe.vae.decode(zeros)
 
     def test_freeu_enabled(self):
         components = self.get_dummy_components()