@@ -2,6 +2,7 @@
 import scipy.ndimage
 import torch
 import comfy.utils
+import node_helpers
 
 from nodes import MAX_RESOLUTION
 
@@ -87,11 +88,7 @@ def INPUT_TYPES(s):
     CATEGORY = "image"
 
     def composite(self, destination, source, x, y, resize_source, mask = None):
-        if destination.shape[-1] < source.shape[-1]:
-            source = source[...,:destination.shape[-1]]
-        elif destination.shape[-1] > source.shape[-1]:
-            destination = torch.nn.functional.pad(destination, (0, 1))
-            destination[..., -1] = source[..., -1]
+        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)
         return (output,)
 
@@ -6,7 +6,7 @@
 
 import comfy.utils
 import comfy.model_management
-
+import node_helpers
 
 class Blend:
     def __init__(self):
@@ -34,6 +34,7 @@ def INPUT_TYPES(s):
     CATEGORY = "image/postprocessing"
 
     def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str):
+        image1, image2 = node_helpers.image_alpha_fix(image1, image2)
         image2 = image2.to(image1.device)
         if image1.shape != image2.shape:
             image2 = image2.permute(0, 3, 1, 2)
 
@@ -44,3 +44,11 @@ def string_to_torch_dtype(string):
         return torch.float16
     if string == "bf16":
         return torch.bfloat16
+
+def image_alpha_fix(destination, source):
+    if destination.shape[-1] < source.shape[-1]:
+        source = source[...,:destination.shape[-1]]
+    elif destination.shape[-1] > source.shape[-1]:
+        destination = torch.nn.functional.pad(destination, (0, 1))
+        destination[..., -1] = source[..., -1]
+    return destination, source