diff --git a/tests/models/autoencoders/test_models_autoencoder_vidtok.py b/tests/models/autoencoders/test_models_autoencoder_vidtok.py
index 5d810141ee8b..d200fe8fa60e 100644
--- a/tests/models/autoencoders/test_models_autoencoder_vidtok.py
+++ b/tests/models/autoencoders/test_models_autoencoder_vidtok.py
@@ -113,60 +113,6 @@ def test_layerwise_casting_training(self):
 class TestAutoencoderVidTokSlicingTiling(AutoencoderVidTokTesterConfig, AutoencoderTesterMixin):
     """Slicing and tiling tests for AutoencoderVidTok."""
 
-    def test_enable_disable_tiling(self):
-        init_dict = self.get_init_dict()
-        inputs_dict = self.get_dummy_inputs()
-
-        torch.manual_seed(0)
-        model = self.model_class(**init_dict).to(torch_device)
-
-        torch.manual_seed(0)
-        output_without_tiling = model(**inputs_dict, generator=torch.manual_seed(0))[0]
-
-        torch.manual_seed(0)
-        model.enable_tiling()
-        output_with_tiling = model(**inputs_dict, generator=torch.manual_seed(0))[0]
-
-        assert (
-            output_without_tiling.detach().cpu().numpy() - output_with_tiling.detach().cpu().numpy()
-        ).max() < 0.5, "VAE tiling should not affect the inference results"
-
-        torch.manual_seed(0)
-        model.disable_tiling()
-        output_without_tiling_2 = model(**inputs_dict, generator=torch.manual_seed(0))[0]
-
-        assert (
-            output_without_tiling.detach().cpu().numpy().all() == output_without_tiling_2.detach().cpu().numpy().all()
-        ), "Without tiling outputs should match with the outputs when tiling is manually disabled."
-
-    def test_enable_disable_slicing(self):
-        init_dict = self.get_init_dict()
-        inputs_dict = self.get_dummy_inputs()
-
-        torch.manual_seed(0)
-        model = self.model_class(**init_dict).to(torch_device)
-        inputs_dict.update({"return_dict": False})
-
-        torch.manual_seed(0)
-        output_without_slicing = model(**inputs_dict, generator=torch.manual_seed(0))[0]
-
-        torch.manual_seed(0)
-        model.enable_slicing()
-        output_with_slicing = model(**inputs_dict, generator=torch.manual_seed(0))[0]
-
-        assert (
-            output_without_slicing.detach().cpu().numpy() - output_with_slicing.detach().cpu().numpy()
-        ).max() < 0.5, "VAE slicing should not affect the inference results"
-
-        torch.manual_seed(0)
-        model.disable_slicing()
-        output_without_slicing_2 = model(**inputs_dict, generator=torch.manual_seed(0))[0]
-
-        assert (
-            output_without_slicing.detach().cpu().numpy().all()
-            == output_without_slicing_2.detach().cpu().numpy().all()
-        ), "Without slicing outputs should match when slicing is manually disabled."
-
     def test_forward_with_norm_groups(self):
         """VidTok uses layernorm instead of groupnorm."""
         init_dict = self.get_init_dict()