Address review feedback for ImageBlend channel-count fix

- Add regression tests covering RGB+RGBA, RGBA+RGB, channel gap > 1
  (the exact CORE-103 error case), all blend modes with mismatch, and
  output value clamping.
- Soften the inline comment to reflect that channel padding is well-
  defined for alpha-like extra channels rather than claiming support
  for arbitrary channel layouts.

comfy_extras/nodes_post_processing.py

@@ -36,9 +36,10 @@ class Blend(io.ComfyNode):
     @classmethod
     def execute(cls, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str) -> io.NodeOutput:
         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.
+        # Match channel counts when one image has an extra channel (typically
+        # an alpha channel, e.g. RGB + RGBA) by padding the image with fewer
+        # channels with 1.0s. Mirrors the logic used by the ImageStitch node
+        # so behavior is consistent across nodes.
         if image1.shape[-1] != image2.shape[-1]:
             max_channels = max(image1.shape[-1], image2.shape[-1])
             if image1.shape[-1] < max_channels:

tests-unit/comfy_extras_test/image_blend_test.py

@@ -0,0 +1,91 @@
+import sys
+from unittest.mock import patch, MagicMock
+
+# `comfy.model_management` initializes the GPU at module import time, which
+# fails in CPU-only environments. Stub it out before any `comfy.*` imports
+# load it transitively. We don't use it in these tests.
+sys.modules.setdefault("comfy.model_management", MagicMock())
+
+import torch  # noqa: E402
+
+# Mock nodes module to prevent CUDA initialization during import
+mock_nodes = MagicMock()
+mock_nodes.MAX_RESOLUTION = 16384
+
+# Mock server module for PromptServer
+mock_server = MagicMock()
+
+with patch.dict("sys.modules", {"nodes": mock_nodes, "server": mock_server}):
+    from comfy_extras.nodes_post_processing import Blend  # noqa: E402
+
+
+class TestImageBlend:
+    """Regression tests for the ImageBlend node, especially channel-count handling."""
+
+    def create_test_image(self, batch_size=1, height=64, width=64, channels=3):
+        return torch.rand(batch_size, height, width, channels)
+
+    def test_same_shape_rgb(self):
+        """Baseline: identical RGB inputs produce an RGB output."""
+        image1 = self.create_test_image(channels=3)
+        image2 = self.create_test_image(channels=3)
+        result = Blend.execute(image1, image2, 0.5, "normal")
+        assert result[0].shape == (1, 64, 64, 3)
+
+    def test_rgb_plus_rgba(self):
+        """RGB image1 + RGBA image2 should pad image1 to 4 channels."""
+        image1 = self.create_test_image(channels=3)
+        image2 = self.create_test_image(channels=4)
+        result = Blend.execute(image1, image2, 0.5, "normal")
+        assert result[0].shape == (1, 64, 64, 4)
+
+    def test_rgba_plus_rgb(self):
+        """RGBA image1 + RGB image2 should pad image2 to 4 channels."""
+        image1 = self.create_test_image(channels=4)
+        image2 = self.create_test_image(channels=3)
+        result = Blend.execute(image1, image2, 0.5, "normal")
+        assert result[0].shape == (1, 64, 64, 4)
+
+    def test_channel_gap_larger_than_one(self):
+        """Channel-count gap > 1 (e.g. 3 vs 5) should not raise.
+
+        This is the exact runtime error reported in CORE-103:
+        'The size of tensor a (5) must match the size of tensor b (3) at
+        non-singleton dimension 3'.
+        """
+        image1 = self.create_test_image(channels=3)
+        image2 = self.create_test_image(channels=5)
+        result = Blend.execute(image1, image2, 0.5, "multiply")
+        assert result[0].shape == (1, 64, 64, 5)
+
+    def test_different_size_and_channels(self):
+        """Different spatial size AND different channel counts should both be reconciled."""
+        image1 = self.create_test_image(height=64, width=64, channels=3)
+        image2 = self.create_test_image(height=32, width=32, channels=4)
+        result = Blend.execute(image1, image2, 0.5, "screen")
+        assert result[0].shape == (1, 64, 64, 4)
+
+    def test_all_blend_modes_with_channel_mismatch(self):
+        """Every blend mode should work with mismatched channel counts."""
+        image1 = self.create_test_image(channels=3)
+        image2 = self.create_test_image(channels=4)
+        for mode in [
+            "normal",
+            "multiply",
+            "screen",
+            "overlay",
+            "soft_light",
+            "difference",
+        ]:
+            result = Blend.execute(image1, image2, 0.5, mode)
+            assert result[0].shape == (1, 64, 64, 4), (
+                f"blend mode {mode} produced wrong shape"
+            )
+
+    def test_output_clamped(self):
+        """Output values should always be clamped to [0, 1]."""
+        image1 = self.create_test_image(channels=3)
+        image2 = self.create_test_image(channels=4)
+        result = Blend.execute(image1, image2, 0.5, "normal")
+        assert result[0].min() >= 0.0
+        assert result[0].max() <= 1.0