fixed for bacth inputs

__init__.py

@@ -2391,7 +2391,7 @@ class TRI3DCompositeImageSplitter:
         """
         return {
             "required": {
-                "image": ("IMAGE",)
+                "images": ("IMAGE",)
             },
         }
 
@@ -2401,16 +2401,30 @@ class TRI3DCompositeImageSplitter:
 
     CATEGORY = "TRI3D"
 
-    def main(self, image):
-        image = from_torch_image(image)
-        h,w,_ = image.shape
-        image1 = image[:,:w//2,:]
-        image2 = image[:,w//2:,:]
+    def main(self, images):
+        
+        def tensor_to_cv2_img(tensor, remove_alpha=False):
+            i = 255. * tensor.cpu().numpy()  # This will give us (H, W, C)
+            img = np.clip(i, 0, 255).astype(np.uint8)
+            return img
 
-        image1 = torch.from_numpy(image1.astype(np.float32)/255.0)[None,]
-        image2 = torch.from_numpy(image2.astype(np.float32)/255.0)[None,]
+        def cv2_img_to_tensor(img):
+            img = img.astype(np.float32) / 255.0
+            img = torch.from_numpy(img)[
+                None,
+            ]
+            return img
+        images1 = []
+        images2 = []
+        for image in images:
+            image = tensor_to_cv2_img(image)
+            h,w,_ = image.shape
+            image1 = image[:,:w//2,:]
+            image2 = image[:,w//2:,:]
+            images1.append(cv2_img_to_tensor(image1).squeeze(0))
+            images2.append(cv2_img_to_tensor(image2).squeeze(0))
 
-        return (image1, image2)
+        return (torch.stack(images1), torch.stack(images2))
     
 # A dictionary that contains all nodes you want to export with their names
 # NOTE: names should be globally unique