diff --git a/__init__.py b/__init__.py
index 2c23dd4..80a5386 100644
--- a/__init__.py
+++ b/__init__.py
@@ -41,7 +41,7 @@ NODE_CONFIG = {
     "ResizeMask": {"class": ResizeMask, "name": "Resize Mask"},
     "RoundMask": {"class": RoundMask, "name": "Round Mask"},
     "SeparateMasks": {"class": SeparateMasks, "name": "Separate Masks"},
-    "KJConsolidateMasks": {"class": KJConsolidateMasks, "name": "Consolidate Masks"},
+    "ConsolidateMasksKJ": {"class": ConsolidateMasksKJ, "name": "Consolidate Masks"},
     #images
     "AddLabel": {"class": AddLabel, "name": "Add Label"},
     "ColorMatch": {"class": ColorMatch, "name": "Color Match"},
diff --git a/nodes/mask_nodes.py b/nodes/mask_nodes.py
index 8268f18..c519ee0 100644
--- a/nodes/mask_nodes.py
+++ b/nodes/mask_nodes.py
@@ -1426,12 +1426,12 @@ class SeparateMasks:
             return torch.empty((1, 64, 64), device=mask.device),
 
 
-class KJConsolidateMasks:
+class ConsolidateMasksKJ:
     @classmethod
     def INPUT_TYPES(s):
         return {
             "required": {
-                "mask": ("MASK",),
+                "masks": ("MASK",),
                 "width": ("INT", {"default": 512, "min": 0, "max": 4096, "step": 64}),
                 "height": ("INT", {"default": 512, "min": 0, "max": 4096, "step": 64}),
                 "padding": ("INT", {"default": 0, "min": 0, "max": 4096, "step": 1}),
@@ -1444,8 +1444,8 @@ class KJConsolidateMasks:
     CATEGORY = "KJNodes/masking"
     DESCRIPTION = "Consolidates a batch of separate masks by finding the largest group of masks that fit inside a tile of the given width and height (including the padding), and repeating until no more masks can be combined."
 
-    def consolidate(self, mask, width=512, height=512, padding=0):
-        B, H, W = mask.shape
+    def consolidate(self, masks, width=512, height=512, padding=0):
+        B, H, W = masks.shape
 
         def mask_fits(coords, candidate_coords):
             x_min, y_min, x_max, y_max = coords
@@ -1459,7 +1459,7 @@ class KJConsolidateMasks:
         separated = []
         final_masks = []
         for b in range(B):
-            m = mask[b]
+            m = masks[b]
             rows, cols = m.any(dim=1), m.any(dim=0)
             y_min, y_max = torch.where(rows)[0][[0, -1]]
             x_min, x_max = torch.where(cols)[0][[0, -1]]
@@ -1469,8 +1469,8 @@ class KJConsolidateMasks:
 
         separated.sort(key=lambda x: x[0])
         fits = []
-        for i, mask in enumerate(separated):
-            coord = mask[0]
+        for i, masks in enumerate(separated):
+            coord = masks[0]
             fits_in_box = []
             for j, cand_mask in enumerate(separated):
                 if i == j: