diff --git a/comfy/ldm/wan/vae.py b/comfy/ldm/wan/vae.py
index 9a3db212f..65296afa8 100644
--- a/comfy/ldm/wan/vae.py
+++ b/comfy/ldm/wan/vae.py
@@ -378,24 +378,43 @@ class Decoder3d(nn.Module):
             else:
                 x = layer(x)
 
-        ## upsamples
-        for layer in self.upsamples:
+        out_chunks = []
+
+        def run_head(x, feat_idx):
+            for layer in self.head:
+                if isinstance(layer, CausalConv3d) and feat_cache is not None:
+                    cache_x = x[:, :, -CACHE_T:, :, :].clone()
+                    x = layer(x, feat_cache[feat_idx[0]])
+                    feat_cache[feat_idx[0]] = cache_x
+                    feat_idx[0] += 1
+                else:
+                    x = layer(x)
+            out_chunks.append(x)
+
+        def run_upsamples(layer_idx, x, feat_idx):
+            if layer_idx >= len(self.upsamples):
+                run_head(x, feat_idx)
+                return
+
+            layer = self.upsamples[layer_idx]
+            if isinstance(layer, Resample) and layer.mode == 'upsample3d' and x.shape[2] > 1:
+                for frame_idx in range(x.shape[2]):
+                    run_upsamples(
+                        layer_idx,
+                        x[:, :, frame_idx:frame_idx + 1, :, :],
+                        feat_idx.copy(),
+                    )
+                return
+
             if feat_cache is not None:
                 x = layer(x, feat_cache, feat_idx)
             else:
                 x = layer(x)
 
-        ## head
-        for layer in self.head:
-            if isinstance(layer, CausalConv3d) and feat_cache is not None:
-                idx = feat_idx[0]
-                cache_x = x[:, :, -CACHE_T:, :, :].clone()
-                x = layer(x, feat_cache[idx])
-                feat_cache[idx] = cache_x
-                feat_idx[0] += 1
-            else:
-                x = layer(x)
-        return x
+            run_upsamples(layer_idx + 1, x, feat_idx)
+
+        run_upsamples(0, x, feat_idx)
+        return out_chunks
 
 
 def count_cache_layers(model):
@@ -483,5 +502,5 @@ class WanVAE(nn.Module):
                     x[:, :, 1 + 2 * (i - 1):1 + 2 * i, :, :],
                     feat_cache=feat_map,
                     feat_idx=conv_idx)
-                out = torch.cat([out, out_], 2)
-        return out
+                out += out_
+        return torch.cat(out, 2)