LayerUtility: ImageChannelSplit

Documentation

• Class name: LayerUtility: ImageChannelSplit
• Category: 😺dzNodes/LayerUtility
• Output node: False

The ImageChannelSplit node is designed to split an input image into its constituent channels based on a specified color mode. It facilitates the manipulation of individual color channels for advanced image processing tasks, enabling users to work with each channel separately for detailed analysis or modification.

Input types

Required

• image
  • The input image to be split into its constituent channels. This parameter is crucial for the operation as it determines the source image for channel separation.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• mode
  • Specifies the color mode for channel splitting. This parameter affects how the input image is processed and determines the output channel format.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Optional

Output types

• channel_1
  • Comfy dtype: IMAGE
  • The first channel of the split image, which varies depending on the specified mode.
  • Python dtype: torch.Tensor
• channel_2
  • Comfy dtype: IMAGE
  • The second channel of the split image, which varies depending on the specified mode.
  • Python dtype: torch.Tensor
• channel_3
  • Comfy dtype: IMAGE
  • The third channel of the split image, which varies depending on the specified mode.
  • Python dtype: torch.Tensor
• channel_4
  • Comfy dtype: IMAGE
  • The fourth channel of the split image, applicable in modes that support four channels.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ImageChannelSplit:

    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(self):
        channel_mode = ['RGBA', 'YCbCr', 'LAB', 'HSV']
        return {
            "required": {
                "image": ("IMAGE", ),  #
                "mode": (channel_mode,),  # 通道设置
            },
            "optional": {
            }
        }

    RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "IMAGE",)
    RETURN_NAMES = ("channel_1", "channel_2", "channel_3", "channel_4",)
    FUNCTION = 'image_channel_split'
    CATEGORY = '😺dzNodes/LayerUtility'

    def image_channel_split(self, image, mode):

        c1_images = []
        c2_images = []
        c3_images = []
        c4_images = []

        for i in image:
            i = torch.unsqueeze(i, 0)
            _image = tensor2pil(i).convert('RGBA')
            channel1, channel2, channel3, channel4 = image_channel_split(_image, mode)
            c1_images.append(pil2tensor(channel1))
            c2_images.append(pil2tensor(channel2))
            c3_images.append(pil2tensor(channel3))
            c4_images.append(pil2tensor(channel4))

        log(f"{NODE_NAME} Processed {len(c1_images)} image(s).", message_type='finish')
        return (torch.cat(c1_images, dim=0), torch.cat(c2_images, dim=0), torch.cat(c3_images, dim=0), torch.cat(c4_images, dim=0),)