diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py
index 32b60afda..cd8111683 100644
--- a/comfy_extras/nodes_mask.py
+++ b/comfy_extras/nodes_mask.py
@@ -39,10 +39,21 @@ def composite(destination, source, x, y, mask = None, multiplier = 8, resize_sou
 
     inverse_mask = torch.ones_like(mask) - mask
 
-    source_portion = mask * source[..., :visible_height, :visible_width]
-    destination_portion = inverse_mask  * destination[..., top:bottom, left:right]
+    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
 
-    destination[..., top:bottom, left:right] = source_portion + destination_portion
     return destination
 
 class LatentCompositeMasked(IO.ComfyNode):
@@ -82,18 +93,23 @@ class ImageCompositeMasked(IO.ComfyNode):
             search_aliases=["paste image", "overlay", "layer"],
             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, destination, source, x, y, resize_source, mask = None) -> IO.NodeOutput:
+    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)
         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)
@@ -397,33 +413,6 @@ class ClipVisionToMask(IO.ComfyNode):
 
     clip_vision_to_mask = execute
 
-class ConcatMask(IO.ComfyNode):
-    @classmethod
-    def define_schema(cls):
-        return IO.Schema(
-            node_id="ConcatMask",
-            search_aliases=["add mask", "concat mask", "merge mask"],
-            category="mask",
-            inputs=[
-                IO.Mask.Input("mask"),
-                IO.Image.Input("image"),
-            ],
-            outputs=[IO.Image.Output("rgba_image"), IO.Mask.Output("input_mask")],
-        )
-    @classmethod
-    def execute(cls, mask, image):
-        if image.shape[-1] == 3:
-            image = image.movedim(-1, 1)
-        target_h, target_w = image.shape[2], image.shape[3]
-        if mask.shape[-2:] !=  (target_h, target_w):
-            mask = torch.nn.functional.interpolate(
-                mask, size=(target_h, target_w), mode='bicubic', align_corners=False
-            )
-        rgba = torch.cat([image, mask], dim = 1)
-        return IO.NodeOutput(rgba.movedim(1, -1), mask)
-
-    concat_mask = execute
-
 class ThresholdMask(IO.ComfyNode):
     @classmethod
     def define_schema(cls):
@@ -487,7 +476,6 @@ class MaskExtension(ComfyExtension):
             GrowMask,
             ThresholdMask,
             MaskPreview,
-            ConcatMask,
             ClipVisionToMask
         ]