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Fix ImageBlend node to handle mismatched channel counts

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Replace the limited node_helpers.image_alpha_fix call (which only handles
RGB<->RGBA differences by padding by exactly one channel) with the same
generalized channel-padding logic used by the ImageStitch node.

This allows ImageBlend to work with any combination of channel counts
(e.g. 3 vs 4, 3 vs 5, 4 vs 3, etc.) by padding the image with fewer
channels using 1.0s up to the larger channel count. Behavior between
ImageBlend and ImageStitch is now consistent.

Fixes CORE-103.
This commit is contained in:
Glary-Bot 2026-04-27 06:21:33 +00:00
parent 115f418b64
commit 6a1284e20b
1 changed files with 9 additions and 2 deletions
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11
comfy_extras/nodes_post_processing.py
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@ -11,7 +11,6 @@ import kornia
import comfy.utils import comfy.utils
import comfy.model_management import comfy.model_management
from comfy_extras.nodes_latent import reshape_latent_to from comfy_extras.nodes_latent import reshape_latent_to
import node_helpers
from comfy_api.latest import ComfyExtension, io from comfy_api.latest import ComfyExtension, io
from nodes import MAX_RESOLUTION from nodes import MAX_RESOLUTION
@ -36,8 +35,16 @@ class Blend(io.ComfyNode):
@classmethod @classmethod
def execute(cls, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str) -> io.NodeOutput: 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) 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: if image1.shape != image2.shape:
image2 = image2.permute(0, 3, 1, 2) image2 = image2.permute(0, 3, 1, 2)
image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center') image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center')