diff --git a/nodes/image_nodes.py b/nodes/image_nodes.py
index cd858c4..8d201bc 100644
--- a/nodes/image_nodes.py
+++ b/nodes/image_nodes.py
@@ -3149,9 +3149,11 @@ class ImagePadKJ:
                     "extra_padding": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
                     "pad_mode": (["edge", "color"],),
                     "color": ("STRING", {"default": "0, 0, 0", "tooltip": "Color as RGB values in range 0-255, separated by commas."}),
-                  }
-                , "optional": {
+                  },
+                "optional": {
                     "mask": ("MASK", ),
+                    "target_width": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
+                    "target_height": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
                 }
                 }
     
@@ -3161,7 +3163,7 @@ class ImagePadKJ:
     CATEGORY = "KJNodes/image"
     DESCRIPTION = "Pad the input image and optionally mask with the specified padding."
         
-    def pad(self, image, left, right, top, bottom, extra_padding, color, pad_mode, mask=None):
+    def pad(self, image, left, right, top, bottom, extra_padding, color, pad_mode, mask=None, target_width=None, target_height=None):
         B, H, W, C = image.shape
         
         # Resize masks to image dimensions if necessary
@@ -3175,15 +3177,23 @@ class ImagePadKJ:
         if len(bg_color) == 1:
             bg_color = bg_color * 3  # Grayscale to RGB
         bg_color = torch.tensor(bg_color, dtype=image.dtype, device=image.device)
-
+        
         # Calculate padding sizes with extra padding
-        pad_left = left + extra_padding
-        pad_right = right + extra_padding
-        pad_top = top + extra_padding
-        pad_bottom = bottom + extra_padding
+        if target_width is not None and target_height is not None:
+            padded_width = target_width
+            padded_height = target_height
+            pad_left = (padded_width - W) // 2
+            pad_right = padded_width - W - pad_left
+            pad_top = (padded_height - H) // 2
+            pad_bottom = padded_height - H - pad_top
+        else:
+            pad_left = left + extra_padding
+            pad_right = right + extra_padding
+            pad_top = top + extra_padding
+            pad_bottom = bottom + extra_padding
 
-        padded_width = W + pad_left + pad_right
-        padded_height = H + pad_top + pad_bottom
+            padded_width = W + pad_left + pad_right
+            padded_height = H + pad_top + pad_bottom
         out_image = torch.zeros((B, padded_height, padded_width, C), dtype=image.dtype, device=image.device)
         
         # Fill padded areas