diff --git a/logic_management/image_list_sampler.py b/logic_management/image_list_sampler.py
index 81c5ad4..ec25db0 100644
--- a/logic_management/image_list_sampler.py
+++ b/logic_management/image_list_sampler.py
@@ -23,22 +23,30 @@ class ImageListSampler:
         if not images:
             return ([],)
             
-        total = len(images)
+        # Flatten list/batches into individual frames
+        import torch
+        all_frames = []
+        for item in images:
+            if isinstance(item, torch.Tensor):
+                if len(item.shape) == 4: # [B, H, W, C]
+                    for i in range(item.shape[0]):
+                        all_frames.append(item[i:i+1])
+                else: 
+                    all_frames.append(item.unsqueeze(0) if len(item.shape) == 3 else item)
+            elif isinstance(item, list):
+                all_frames.extend(item)
+        
+        total = len(all_frames)
         if cnt <= 0:
             return ([],)
         
         if cnt >= total:
-            return (images,)
+            return (all_frames,)
 
         # Calculate equally spaced indices
-        # Example: total=9, count=3
-        # step = 9 / 3 = 3.0
-        # i=0 -> 0
-        # i=1 -> 3
-        # i=2 -> 6
         step = total / cnt
         indices = [int(i * step) for i in range(cnt)]
         
-        sampled = [images[i] for i in indices]
+        sampled = [all_frames[i] for i in indices]
         
         return (sampled,)