diff --git a/comfy_extras/nodes_compositing.py b/comfy_extras/nodes_compositing.py
index b0ae2dfa..f39daa00 100644
--- a/comfy_extras/nodes_compositing.py
+++ b/comfy_extras/nodes_compositing.py
@@ -113,19 +113,21 @@ class PorterDuffImageComposite:
             src_image = source[i]
             dst_image = destination[i]
 
+            assert src_image.shape[2] == dst_image.shape[2] # inputs need to have same number of channels
+
             src_alpha = source_alpha[i].unsqueeze(2)
             dst_alpha = destination_alpha[i].unsqueeze(2)
 
-            if dst_alpha.shape != dst_image.shape:
-                upscale_input = dst_alpha[None,:,:,:].permute(0, 3, 1, 2)
+            if dst_alpha.shape[:2] != dst_image.shape[:2]:
+                upscale_input = dst_alpha.unsqueeze(0).permute(0, 3, 1, 2)
                 upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
                 dst_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
             if src_image.shape != dst_image.shape:
-                upscale_input = src_image[None,:,:,:].permute(0, 3, 1, 2)
+                upscale_input = src_image.unsqueeze(0).permute(0, 3, 1, 2)
                 upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
                 src_image = upscale_output.permute(0, 2, 3, 1).squeeze(0)
             if src_alpha.shape != dst_alpha.shape:
-                upscale_input = src_alpha[None,:,:,:].permute(0, 3, 1, 2)
+                upscale_input = src_alpha.unsqueeze(0).permute(0, 3, 1, 2)
                 upscale_output = comfy.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center')
                 src_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
 
@@ -177,7 +179,7 @@ class JoinImageWithAlpha:
         out_images = []
 
         for i in range(batch_size):
-           out_images.append(torch.cat((image[i], alpha[i].unsqueeze(2)), dim=2))
+           out_images.append(torch.cat((image[i][:,:,:3], alpha[i].unsqueeze(2)), dim=2))
 
         result = (torch.stack(out_images),)
         return result