feat(wan): Add tiled VAE decoding and fix TI2V quality

mlx_video/models/wan/model.py

@@ -48,9 +48,8 @@ class Head(nn.Module):
         """
         if e.ndim == 2:
             e = e[:, None, :]  # [B, 1, dim]
-        # Compute modulation in float32 for precision, cast to working dtype
-        w_dtype = _linear_dtype(self.head)
-        mod = (self.modulation[:, None, :, :] + e[:, :, None, :]).astype(w_dtype)
+        # Compute modulation in float32 (matching reference's autocast(float32))
+        mod = self.modulation[:, None, :, :] + e[:, :, None, :]  # float32
         e0 = mod[:, :, 0, :]  # [B, L_e, dim] shift
         e1 = mod[:, :, 1, :]  # [B, L_e, dim] scale
         x_norm = self.norm(x)
@@ -120,10 +119,13 @@ class WanModel(nn.Module):
         ], axis=1)
 
         # Precompute sinusoidal inv_freq for time embedding
+        # Use numpy float64 for precision (matches reference torch.float64),
+        # then store as float32 since MLX GPU doesn't support float64.
         half = config.freq_dim // 2
-        self._inv_freq = mx.power(
-            10000.0, -mx.arange(half).astype(mx.float32) / half
+        inv_freq_np = np.power(
+            10000.0, -np.arange(half, dtype=np.float64) / half
         )
+        self._inv_freq = mx.array(inv_freq_np.astype(np.float32))
 
 
     def _patchify(self, x: mx.array) -> tuple: