diff --git a/src/diffusers/models/autoencoders/autoencoder_kl_wan.py b/src/diffusers/models/autoencoders/autoencoder_kl_wan.py
index d84a0861e984..e6e58c1cce85 100644
--- a/src/diffusers/models/autoencoders/autoencoder_kl_wan.py
+++ b/src/diffusers/models/autoencoders/autoencoder_kl_wan.py
@@ -1052,7 +1052,7 @@ def __init__(
             is_residual=is_residual,
         )
 
-        self.spatial_compression_ratio = 2 ** len(self.temperal_downsample)
+        self.spatial_compression_ratio = scale_factor_spatial
 
         # When decoding a batch of video latents at a time, one can save memory by slicing across the batch dimension
         # to perform decoding of a single video latent at a time.
@@ -1145,12 +1145,13 @@ def clear_cache(self):
     def _encode(self, x: torch.Tensor):
         _, _, num_frame, height, width = x.shape
 
-        if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):
-            return self.tiled_encode(x)
-
         self.clear_cache()
         if self.config.patch_size is not None:
             x = patchify(x, patch_size=self.config.patch_size)
+
+        if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):
+            return self.tiled_encode(x)
+
         iter_ = 1 + (num_frame - 1) // 4
         for i in range(iter_):
             self._enc_conv_idx = [0]