diff --git a/nodes.py b/nodes.py
index 58e1b55..818393d 100644
--- a/nodes.py
+++ b/nodes.py
@@ -2204,8 +2204,6 @@ class WanVideoDecode:
             images = images.permute(1, 2, 3, 0).cpu().float()
             return (images,)
         else:
-            if end_image is not None:
-                enable_vae_tiling = False
             images = vae.decode(latents, device=device, end_=(end_image is not None), tiled=enable_vae_tiling, tile_size=(tile_x//8, tile_y//8), tile_stride=(tile_stride_x//8, tile_stride_y//8))[0]
             
         
diff --git a/wanvideo/wan_video_vae.py b/wanvideo/wan_video_vae.py
index a5758f1..757a77f 100644
--- a/wanvideo/wan_video_vae.py
+++ b/wanvideo/wan_video_vae.py
@@ -1231,7 +1231,7 @@ class WanVideoVAE(nn.Module):
         return mask
 
 
-    def tiled_decode(self, hidden_states, device, tile_size, tile_stride, pbar=True):
+    def tiled_decode(self, hidden_states, device, tile_size, tile_stride, end_=False, pbar=True):
         _, _, T, H, W = hidden_states.shape
         size_h, size_w = tile_size
         stride_h, stride_w = tile_stride
@@ -1248,14 +1248,20 @@ class WanVideoVAE(nn.Module):
         data_device = "cpu"
         computation_device = device
 
-        out_T = T * 4 - 3
-        weight = torch.zeros((1, 1, out_T, H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device)
-        values = torch.zeros((1, 3, out_T, H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device)
+        weight, values = None, None
         if pbar:
             pbar = ProgressBar(len(tasks))
         for h, h_, w, w_ in tqdm(tasks, desc="VAE decoding"):
             hidden_states_batch = hidden_states[:, :, :, h:h_, w:w_].to(computation_device)
-            hidden_states_batch = self.model.decode(hidden_states_batch).to(data_device)
+            if end_:
+                hidden_states_batch = self.model.decode_2(hidden_states_batch).to(data_device)
+            else:
+                hidden_states_batch = self.model.decode(hidden_states_batch).to(data_device)
+
+            if weight is None:
+                weight = torch.zeros((1, 1, hidden_states_batch.shape[2], H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device)
+            if values is None:
+                values = torch.zeros((1, 3, hidden_states_batch.shape[2], H * self.upsampling_factor, W * self.upsampling_factor), dtype=hidden_states.dtype, device=data_device)
 
             mask = self.build_mask(
                 hidden_states_batch,
@@ -1399,7 +1405,7 @@ class WanVideoVAE(nn.Module):
         for hidden_state in hidden_states:
             hidden_state = hidden_state.unsqueeze(0)
             if tiled:
-                video = self.tiled_decode(hidden_state, device, tile_size, tile_stride, pbar=pbar)
+                video = self.tiled_decode(hidden_state, device, tile_size, tile_stride, end_=end_, pbar=pbar)
             else:
                 if end_:
                     video = self.double_decode(hidden_state, device)