diff --git a/sam2/utils/visualization.py b/sam2/utils/visualization.py
index 6c4c10e9..c17f9e1e 100644
--- a/sam2/utils/visualization.py
+++ b/sam2/utils/visualization.py
@@ -11,25 +11,41 @@ def show_masks(
     alpha: Optional[float] = 0.5,
     display_image: Optional[bool] = False,
     only_best: Optional[bool] = True,
+    autogenerated_mask: Optional[bool] = False,
 ) -> Image.Image:
     if scores is not None:
         # sort masks by their scores
         sorted_ind = np.argsort(scores)[::-1]
         masks = masks[sorted_ind]
 
-    # get mask dimensions
-    h, w = masks.shape[-2:]
+    if autogenerated_mask:
+        masks = sorted(masks, key=(lambda x: x["area"]), reverse=True)
+    else:
+        # get mask dimensions
+        h, w = masks.shape[-2:]
 
     if display_image:
         output_image = Image.fromarray(image)
     else:
         # create a new blank image to superimpose masks
-        output_image = Image.new(mode="RGBA", size=(w, h), color=(0, 0, 0))
+        if autogenerated_mask:
+            output_image = Image.new(
+                mode="RGBA",
+                size=(
+                    masks[0]["segmentation"].shape[0],
+                    masks[0]["segmentation"].shape[1],
+                ),
+                color=(0, 0, 0),
+            )
+        else:
+            output_image = Image.new(mode="RGBA", size=(w, h), color=(0, 0, 0))
 
     for i, mask in enumerate(masks):
-        if mask.ndim > 2:  # type: ignore
-            mask = mask.squeeze()  # type: ignore
-
+        if not autogenerated_mask:
+            if mask.ndim > 2:  # type: ignore
+                mask = mask.squeeze()  # type: ignore
+        else:
+            mask = mask["segmentation"]
         # Generate a random color with specified alpha value
         color = np.concatenate(
             (np.random.randint(0, 256, size=3), [int(alpha * 255)]), axis=0