diff --git a/model/iter.py b/model/iter.py
index 1d0933c..351a406 100644
--- a/model/iter.py
+++ b/model/iter.py
@@ -1,4 +1,4 @@
-from typing import List, Callable
+from typing import List, Callable, Any, Optional
 import torch
 import tqdm
 from comfy.sd import ModelPatcher, CLIP, VAE
@@ -13,14 +13,14 @@ class CondForModels(torch.Tensor):
         super().__init__()
         self.ex = ex
 
+ATTR_NAME = 'iter_fn'
+
 def iterize_model(model: ModelPatcher) -> List[Callable[[],ModelPatcher]]:
-    ATTR_NAME = 'iter_fn'
     if not hasattr(model, ATTR_NAME):
         setattr(model, ATTR_NAME, [lambda: model])
     return getattr(model, ATTR_NAME)
 
 def iterize_clip(clip: CLIP) -> List[Callable[[],CLIP]]:
-    ATTR_NAME = 'iter_fn'
     if hasattr(clip, ATTR_NAME):
         return getattr(clip, ATTR_NAME)
     
@@ -47,7 +47,6 @@ def iterize_clip(clip: CLIP) -> List[Callable[[],CLIP]]:
     return getattr(clip, ATTR_NAME)
 
 def iterize_vae(vae: VAE) -> List[Callable[[],VAE]]:
-    ATTR_NAME = 'iter_fn'
     if hasattr(vae, ATTR_NAME):
         return getattr(vae, ATTR_NAME)
     
@@ -73,6 +72,8 @@ def iterize_vae(vae: VAE) -> List[Callable[[],VAE]]:
     
     return getattr(vae, ATTR_NAME)
 
+def try_get_iter(obj) -> Optional[List[Callable[[],Any]]]:
+    return getattr(obj, ATTR_NAME, None)
 
 class ModelIter:
     
diff --git a/vae.py b/vae.py
index e7ce6f7..e18eedd 100644
--- a/vae.py
+++ b/vae.py
@@ -1,5 +1,8 @@
+from collections import defaultdict
+from typing import Dict, List
 import torch
 from tqdm import trange
+from .model.iter import try_get_iter
 
 class VAEDecodeBatched:
     def __init__(self, device="cpu"):
@@ -29,14 +32,30 @@ class VAEDecodeBatched:
         s = samples['samples']
         n = s.shape[0]
         
-        results = []
+        iters = try_get_iter(vae)
+        if iters is None:
+            vae_num = 1
+        else:
+            vae_num = len(iters)
+        
+        vae_results: Dict[int,List[torch.Tensor]] = defaultdict(lambda: [])
+        
         for i in trange(0, n, batch_size):
             e = min([i+batch_size, n])
             t = s[i:e, ...]
             v = vae.decode(t)
-            results.append(v)
+            
+            vaes = torch.chunk(v, vae_num)
+            
+            for vn, vv in enumerate(vaes):
+                vae_results[vn].append(vv)
         
-        vs = torch.cat(results)
+        results = []
+        for k in sorted(vae_results.keys()):
+            v = vae_results[k]
+            results.extend(v)
+            
+        vs = torch.cat(results).contiguous()
         return (vs,)