Merge Image Channels

Documentation

• Class name: MergeImageChannels
• Category: KJNodes/image
• Output node: False

The MergeImageChannels node is designed for combining separate image channels (red, green, and blue) into a single composite image. It optionally supports the inclusion of an alpha channel for transparency, allowing for more complex image compositions.

Input types

Required

• red
  • The red channel of the image, which contributes to the overall color composition by defining the red intensity.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• green
  • The green channel of the image, which contributes to the overall color composition by defining the green intensity.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• blue
  • The blue channel of the image, which contributes to the overall color composition by defining the blue intensity.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Optional

• mask
  • An optional alpha channel for adding transparency to the final image, enhancing its compositional complexity.
  • Comfy dtype: MASK
  • Python dtype: torch.Tensor

Output types

• image
  • Comfy dtype: IMAGE
  • The resulting image after merging the red, green, and blue channels, optionally with an alpha channel for transparency.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class MergeImageChannels:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { 
            "red": ("IMAGE",),
            "green": ("IMAGE",),
            "blue": ("IMAGE",),

            },
            "optional": {
                "mask": ("MASK", {"default": None}),
                },
            }

    RETURN_TYPES = ("IMAGE",)
    RETURN_NAMES = ("image",)
    FUNCTION = "merge"
    CATEGORY = "KJNodes/image"
    DESCRIPTION = """
Merges channel data into an image.  
"""

    def merge(self, red, green, blue, alpha=None):
        image = torch.stack([
        red[..., 0, None], # Red channel
        green[..., 1, None], # Green channel
        blue[..., 2, None]   # Blue channel
        ], dim=-1)
        image = image.squeeze(-2)
        if alpha is not None:
            image = torch.cat([image, alpha], dim=-1)
        return (image,)