diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py
index c9b2a84d9..96ee1a0f8 100644
--- a/comfy_extras/nodes_mask.py
+++ b/comfy_extras/nodes_mask.py
@@ -40,23 +40,13 @@ def composite(destination, source, x, y, mask = None, multiplier = 8, resize_sou
 
     inverse_mask = torch.ones_like(mask) - mask
 
-    source_rgb = source[:, :3, :visible_height, :visible_width]
-    dest_slice = destination[..., top:bottom, left:right]
-
-    if destination.shape[1] == 4:
-        if torch.max(dest_slice) == 0:
-            destination[:, :3, top:bottom, left:right] = source_rgb
-            destination[:, 3:4, top:bottom, left:right] = mask
-        else:
-            destination[:, :3, top:bottom, left:right] = (mask * source_rgb) + (inverse_mask * dest_slice[:, :3])
-            destination[:, 3:4, top:bottom, left:right] = torch.max(mask, dest_slice[:, 3:4])
-    else:
-        source_portion = mask * source_rgb
-        destination_portion = inverse_mask * dest_slice
-        destination[..., top:bottom, left:right] = source_portion + destination_portion
+    source_portion = mask * source[..., :visible_height, :visible_width]
+    destination_portion = inverse_mask  * destination[..., top:bottom, left:right]
 
+    destination[..., top:bottom, left:right] = source_portion + destination_portion
     return destination
 
+
 class LatentCompositeMasked(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -95,23 +85,18 @@ class ImageCompositeMasked(IO.ComfyNode):
             display_name="Image Composite Masked",
             category="image",
             inputs=[
+                IO.Image.Input("destination"),
                 IO.Image.Input("source"),
                 IO.Int.Input("x", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
                 IO.Int.Input("y", default=0, min=0, max=nodes.MAX_RESOLUTION, step=1),
                 IO.Boolean.Input("resize_source", default=False),
-                IO.Image.Input("destination", optional=True),
                 IO.Mask.Input("mask", optional=True),
             ],
             outputs=[IO.Image.Output()],
         )
 
     @classmethod
-    def execute(cls, source, x, y, resize_source, destination = None, mask = None) -> IO.NodeOutput:
-        if destination is None: # transparent rgba
-            B, H, W, C = source.shape
-            destination = torch.zeros((B, H, W, 4), dtype=source.dtype, device=source.device)
-            if C == 3:
-                source = torch.nn.functional.pad(source, (0, 1), value=1.0)
+    def execute(cls, destination, source, x, y, resize_source, mask = None) -> IO.NodeOutput:
         destination, source = node_helpers.image_alpha_fix(destination, source)
         destination = destination.clone().movedim(-1, 1)
         output = composite(destination, source.movedim(-1, 1), x, y, mask, 1, resize_source).movedim(1, -1)