diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py
index c932b747a..0c9edc71d 100644
--- a/comfy_extras/nodes_post_processing.py
+++ b/comfy_extras/nodes_post_processing.py
@@ -11,7 +11,6 @@ import kornia
 import comfy.utils
 import comfy.model_management
 from comfy_extras.nodes_latent import reshape_latent_to
-import node_helpers
 from comfy_api.latest import ComfyExtension, io
 from nodes import MAX_RESOLUTION
 
@@ -36,8 +35,16 @@ class Blend(io.ComfyNode):
 
     @classmethod
     def execute(cls, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str) -> io.NodeOutput:
-        image1, image2 = node_helpers.image_alpha_fix(image1, image2)
         image2 = image2.to(image1.device)
+        # Match channel counts by padding the image with fewer channels with 1.0s
+        # (e.g. RGB + RGBA, or any other channel-count mismatch). Mirrors the
+        # logic used by the ImageStitch node so behavior is consistent.
+        if image1.shape[-1] != image2.shape[-1]:
+            max_channels = max(image1.shape[-1], image2.shape[-1])
+            if image1.shape[-1] < max_channels:
+                image1 = torch.cat([image1, torch.ones(*image1.shape[:-1], max_channels - image1.shape[-1], device=image1.device, dtype=image1.dtype)], dim=-1)
+            if image2.shape[-1] < max_channels:
+                image2 = torch.cat([image2, torch.ones(*image2.shape[:-1], max_channels - image2.shape[-1], device=image2.device, dtype=image2.dtype)], dim=-1)
         if image1.shape != image2.shape:
             image2 = image2.permute(0, 3, 1, 2)
             image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center')