diff --git a/src/diffusers/models/autoencoders/autoencoder_kl_wan.py b/src/diffusers/models/autoencoders/autoencoder_kl_wan.py
index 49cefcd8a142..49b6330574a7 100644
--- a/src/diffusers/models/autoencoders/autoencoder_kl_wan.py
+++ b/src/diffusers/models/autoencoders/autoencoder_kl_wan.py
@@ -619,6 +619,13 @@ def __init__(
         self.gradient_checkpointing = False
 
     def forward(self, x, feat_cache=None, feat_idx=[0]):
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            return self._gradient_checkpointing_func(self._decode, x, feat_cache, feat_idx)
+        else:
+            return self._decode(x, feat_cache, feat_idx)
+
+    def _decode(self, x, in_cache=None, feat_idx=[0]):
+        feat_cache = in_cache.copy()
         ## conv1
         if feat_cache is not None:
             idx = feat_idx[0]
@@ -653,7 +660,8 @@ def forward(self, x, feat_cache=None, feat_idx=[0]):
             feat_idx[0] += 1
         else:
             x = self.conv_out(x)
-        return x
+        feat_idx[0] = 0
+        return x, feat_cache
 
 
 class AutoencoderKLWan(ModelMixin, ConfigMixin, FromOriginalModelMixin):
@@ -665,7 +673,7 @@ class AutoencoderKLWan(ModelMixin, ConfigMixin, FromOriginalModelMixin):
     for all models (such as downloading or saving).
     """
 
-    _supports_gradient_checkpointing = False
+    _supports_gradient_checkpointing = True
 
     @register_to_config
     def __init__(
@@ -884,9 +892,13 @@ def _decode(self, z: torch.Tensor, return_dict: bool = True):
         for i in range(num_frame):
             self._conv_idx = [0]
             if i == 0:
-                out = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx)
+                out, self._feat_map = self.decoder(
+                    x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx
+                )
             else:
-                out_ = self.decoder(x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx)
+                out_, self._feat_map = self.decoder(
+                    x[:, :, i : i + 1, :, :], feat_cache=self._feat_map, feat_idx=self._conv_idx
+                )
                 out = torch.cat([out, out_], 2)
 
         out = torch.clamp(out, min=-1.0, max=1.0)
diff --git a/tests/models/autoencoders/test_models_autoencoder_wan.py b/tests/models/autoencoders/test_models_autoencoder_wan.py
index c0af4f5834b7..43f710e9d012 100644
--- a/tests/models/autoencoders/test_models_autoencoder_wan.py
+++ b/tests/models/autoencoders/test_models_autoencoder_wan.py
@@ -140,7 +140,13 @@ def test_enable_disable_slicing(self):
 
     @unittest.skip("Gradient checkpointing has not been implemented yet")
     def test_gradient_checkpointing_is_applied(self):
-        pass
+        expected_set = {
+            "WanDecoder3d",
+            "WanEncoder3d",
+            "WanMidBlock",
+            "WanUpBlock",
+        }
+        super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
 
     @unittest.skip("Test not supported")
     def test_forward_with_norm_groups(self):