diff --git a/nodes.py b/nodes.py
index 89e6532..e824bd5 100644
--- a/nodes.py
+++ b/nodes.py
@@ -1797,6 +1797,131 @@ class BatchCropFromMaskAdvanced:
 
         return (original_images, cropped_out, cropped_masks_out, combined_crop_out, combined_crop_mask_out, bounding_boxes, combined_bounding_box, self.max_bbox_size, self.max_bbox_size)
 
+class FilterZeroMasksAndCorrespondingImages:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "masks": ("MASK",),
+            },
+            "optional": {
+                "original_images": ("IMAGE",), 
+            },
+        }
+
+    RETURN_TYPES = (
+        "MASK",
+        "IMAGE",
+        "IMAGE",
+        "INDEXES"
+    )
+    RETURN_NAMES = (
+        "non_zero_masks_out",
+        "non_zero_mask_images_out",
+        "zero_mask_images_out",
+        "zero_mask_images_out_indexes"
+    )
+    FUNCTION = "filter"
+    CATEGORY = "KJNodes/masking"
+    
+    def filter(self, masks, original_images=None):
+        """
+        Filter out all the empty (i.e. all zero) mask in masks
+        Also filter out all the corresponding images in original_images by indexes if provide
+
+        Args:
+            original_images (optional): If provide, it need have same length as masks.
+        """
+        non_zero_masks = []
+        non_zero_mask_images = []
+        zero_mask_images = []
+        zero_mask_images_indexes = []
+        
+        masks_num = len(masks)
+        also_process_images = False
+        if original_images is not None:
+            imgs_num = len(original_images)
+            if len(original_images) == masks_num:
+                also_process_images = True
+            else:
+                print(f"[WARNING] ignore input: original_images, due to number of original_images ({imgs_num}) is not equal to number of masks ({masks_num})")
+        
+        for i in range(masks_num):
+            non_zero_num = np.count_nonzero(np.array(masks[i]))
+            if non_zero_num > 0:
+                non_zero_masks.append(masks[i])
+                if also_process_images:
+                    non_zero_mask_images.append(original_images[i])
+            else:
+                zero_mask_images.append(original_images[i])
+                zero_mask_images_indexes.append(i)
+
+        non_zero_masks_out = torch.stack(non_zero_masks, dim=0)
+        non_zero_mask_images_out = zero_mask_images_out = zero_mask_images_out_indexes = None
+        
+        if also_process_images:
+            non_zero_mask_images_out = torch.stack(non_zero_mask_images, dim=0)
+            if len(zero_mask_images) > 0:
+                zero_mask_images_out = torch.stack(zero_mask_images, dim=0)
+                zero_mask_images_out_indexes = zero_mask_images_indexes
+
+        return (non_zero_masks_out, non_zero_mask_images_out, zero_mask_images_out, zero_mask_images_out_indexes)
+
+class InsertImageBatchByIndexes:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "images": ("IMAGE",), 
+                "images_to_insert": ("IMAGE",), 
+                "insert_indexes": ("INDEXES",),
+            },
+        }
+
+    RETURN_TYPES = (
+        "IMAGE",
+    )
+    RETURN_NAMES = (
+        "images_after_insert",
+    )
+    FUNCTION = "insert"
+    CATEGORY = "KJNodes"
+    
+    def insert(self, images, images_to_insert, insert_indexes):
+        """
+        This node is designed to be use with node FilterZeroMasksAndCorrespondingImages
+        It inserts the images_to_insert into images according to insert_indexes
+
+        Returns:
+            images_after_insert: updated original images with origonal sequence order 
+        """
+        
+        images_after_insert = images
+        
+        if images_to_insert is not None and insert_indexes is not None:
+            images_to_insert_num = len(images_to_insert)
+            insert_indexes_num = len(insert_indexes)
+            if images_to_insert_num == insert_indexes_num:
+                images_after_insert = []
+
+                i_images = 0
+                for i in range(len(images) + images_to_insert_num):
+                    if i in insert_indexes:
+                        images_after_insert.append(images_to_insert[insert_indexes.index(i)])
+                    else:
+                        images_after_insert.append(images[i_images])
+                        i_images += 1
+                        
+                images_after_insert = torch.stack(images_after_insert, dim=0)
+                
+            else:
+                print(f"[WARNING] skip this node, due to number of images_to_insert ({images_to_insert_num}) is not equal to number of insert_indexes ({insert_indexes_num})")
+
+
+        return (images_after_insert, )
+
 def bbox_to_region(bbox, target_size=None):
     bbox = bbox_check(bbox, target_size)
     return (bbox[0], bbox[1], bbox[0] + bbox[2], bbox[1] + bbox[3])
@@ -2973,6 +3098,8 @@ NODE_CLASS_MAPPINGS = {
     "ReplaceImagesInBatch": ReplaceImagesInBatch,
     "BatchCropFromMask": BatchCropFromMask,
     "BatchCropFromMaskAdvanced": BatchCropFromMaskAdvanced,
+    "FilterZeroMasksAndCorrespondingImages": FilterZeroMasksAndCorrespondingImages,
+    "InsertImageBatchByIndexes": InsertImageBatchByIndexes,
     "BatchUncrop": BatchUncrop,
     "BatchUncropAdvanced": BatchUncropAdvanced,
     "BatchCLIPSeg": BatchCLIPSeg,
@@ -3028,6 +3155,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ReplaceImagesInBatch": "ReplaceImagesInBatch",
     "BatchCropFromMask": "BatchCropFromMask",
     "BatchCropFromMaskAdvanced": "BatchCropFromMaskAdvanced",
+    "FilterZeroMasksAndCorrespondingImages": "FilterZeroMasksAndCorrespondingImages",
+    "InsertImageBatchByIndexes": "InsertImageBatchByIndexes",
     "BatchUncrop": "BatchUncrop",
     "BatchUncropAdvanced": "BatchUncropAdvanced",
     "BatchCLIPSeg": "BatchCLIPSeg",