fix OOM for test_vae_tiling (#7510)

use float16 and add torch.no_grad()

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)]
+        with torch.no_grad():
             for shape in shapes:
                 image = torch.zeros(shape, device=torch_device)
                 pipe.vae.decode(image)

tests/pipelines/test_pipelines_common.py

@@ -124,6 +124,7 @@ 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)]
+        with torch.no_grad():
             for shape in shapes:
                 zeros = torch.zeros(shape).to(torch_device)
                 pipe.vae.decode(zeros)