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

mlx_video/models/wan/vae.py

@@ -534,3 +534,56 @@ class WanVAE(nn.Module):
         x = self.conv2(z)
         out = self.decoder(x)
         return mx.clip(out, -1, 1)
+
+    def decode_tiled(self, z: mx.array, tiling_config=None) -> mx.array:
+        """Decode latent to video using tiling to reduce memory usage.
+
+        Splits the latent tensor into overlapping spatial/temporal tiles,
+        decodes each tile independently, and blends them with trapezoidal
+        masks. Reuses the LTX-2 tiling infrastructure.
+
+        Args:
+            z: Normalized latent [B, z_dim, T, H, W]
+            tiling_config: Optional TilingConfig. If None, uses default.
+
+        Returns:
+            Video [B, 3, T_out, H_out, W_out] clamped to [-1, 1]
+        """
+        from mlx_video.models.ltx.video_vae.tiling import TilingConfig, decode_with_tiling
+
+        if tiling_config is None:
+            tiling_config = TilingConfig.default()
+
+        # Check if tiling is actually needed
+        _, _, f, h, w = z.shape
+        needs_tiling = False
+        if tiling_config.spatial_config is not None:
+            s_tile = tiling_config.spatial_config.tile_size_in_pixels // 8
+            if h > s_tile or w > s_tile:
+                needs_tiling = True
+        if tiling_config.temporal_config is not None:
+            t_tile = tiling_config.temporal_config.tile_size_in_frames // 4
+            if f > t_tile:
+                needs_tiling = True
+
+        if not needs_tiling:
+            return self.decode(z)
+
+        # Denormalize once (small tensor), then tile the denormalized latents
+        mean = self.mean.reshape(1, -1, 1, 1, 1)
+        inv_std = self.inv_std.reshape(1, -1, 1, 1, 1)
+        z_denorm = z / inv_std + mean
+
+        def tile_decode(tile_latents, **kwargs):
+            x = self.conv2(tile_latents)
+            out = self.decoder(x)
+            return mx.clip(out, -1, 1)
+
+        return decode_with_tiling(
+            decoder_fn=tile_decode,
+            latents=z_denorm,
+            tiling_config=tiling_config,
+            spatial_scale=8,    # 3× spatial 2× upsamples = 8×
+            temporal_scale=4,   # 2× temporal upsamples × 2 = 4×
+            causal_temporal=False,  # Wan2.1 uses non-causal temporal (T → 4T)
+        )