diff --git a/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py b/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py
index e2236a7f20ad..9be79cfe7dc9 100644
--- a/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py
+++ b/src/diffusers/models/autoencoders/autoencoder_kl_hunyuan_video.py
@@ -786,7 +786,7 @@ def __init__(
         self.use_tiling = False
 
         # When decoding temporally long video latents, the memory requirement is very high. By decoding latent frames
-        # at a fixed frame batch size (based on `self.num_latent_frames_batch_sizes`), the memory requirement can be lowered.
+        # at a fixed frame batch size (based on `self.tile_sample_min_num_frames`), the memory requirement can be lowered.
         self.use_framewise_encoding = True
         self.use_framewise_decoding = True
 
@@ -868,7 +868,7 @@ def disable_slicing(self) -> None:
     def _encode(self, x: torch.Tensor) -> torch.Tensor:
         batch_size, num_channels, num_frames, height, width = x.shape
 
-        if self.use_framewise_decoding and num_frames > self.tile_sample_min_num_frames:
+        if self.use_framewise_encoding and num_frames > self.tile_sample_min_num_frames:
             return self._temporal_tiled_encode(x)
 
         if self.use_tiling and (width > self.tile_sample_min_width or height > self.tile_sample_min_height):