diff --git a/mlx_video/models/ltx_2/video_vae/sampling.py b/mlx_video/models/ltx_2/video_vae/sampling.py
index 7e351ba..424e22e 100644
--- a/mlx_video/models/ltx_2/video_vae/sampling.py
+++ b/mlx_video/models/ltx_2/video_vae/sampling.py
@@ -88,17 +88,20 @@ def __call__(self, x: mx.array, causal: bool = True) -> mx.array:
         if pad_d > 0 or pad_h > 0 or pad_w > 0:
             x = mx.pad(x, [(0, 0), (0, 0), (0, pad_d), (0, pad_h), (0, pad_w)])
 
-        # Skip connection: space-to-depth on input, then group mean
-        x_in = self._space_to_depth(x)
-        # Reshape for group mean: (b, c*prod(stride), d, h, w) -> (b, out_channels, group_size, d, h, w)
-        b2, c2, d2, h2, w2 = x_in.shape
-        x_in = mx.reshape(x_in, (b2, self.out_channels, self.group_size, d2, h2, w2))
-        x_in = mx.mean(x_in, axis=2)  # (b, out_channels, d, h, w)
-
         # Conv branch: apply conv then space-to-depth
         x_conv = self.conv(x, causal=causal)
         x_conv = self._space_to_depth(x_conv)
 
+        # Skip connection: space-to-depth on input, then group mean
+        # Derive output channels from conv result to handle weight/config mismatches
+        # (e.g. LTX-2.3 converted weights where conv out_channels != out_channels // multiplier)
+        actual_out_channels = x_conv.shape[1]
+        x_in = self._space_to_depth(x)
+        b2, c2, d2, h2, w2 = x_in.shape
+        actual_group_size = c2 // actual_out_channels
+        x_in = mx.reshape(x_in, (b2, actual_out_channels, actual_group_size, d2, h2, w2))
+        x_in = mx.mean(x_in, axis=2)  # (b, actual_out_channels, d, h, w)
+
         # Add skip connection
         return x_conv + x_in