Layer Diffuse Decode (RGBA)

Documentation

• Class name: LayeredDiffusionDecodeRGBA
• Category: layer_diffuse
• Output node: False

The LayeredDiffusionDecodeRGBA node is designed to decode the alpha channel value from a given pixel value, transforming an RGB image input into an RGBA image output. This process involves adding an alpha channel to the existing RGB channels, thereby enabling the representation of transparency in the resulting image.

Input types

Required

• samples
  • The latent representation of the image to be decoded. It plays a crucial role in reconstructing the image with the added alpha channel for transparency.
  • Comfy dtype: LATENT
  • Python dtype: torch.Tensor
• images
  • The initial RGB images that are to be transformed into RGBA format by adding an alpha channel.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• sd_version
  • Specifies the version of the Stable Diffusion model used for decoding, affecting the decoding process and the final image quality.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• sub_batch_size
  • Determines the size of sub-batches for processing, impacting the efficiency and speed of the decoding operation.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The output image in RGBA format, where the alpha channel has been decoded and added to the original RGB image.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class LayeredDiffusionDecodeRGBA(LayeredDiffusionDecode):
    """
    Decode alpha channel value from pixel value.
    [B, C=3, H, W] => [B, C=4, H, W]
    Outputs RGBA image.
    """

    RETURN_TYPES = ("IMAGE",)

    def decode(self, samples, images, sd_version: str, sub_batch_size: int):
        image, mask = super().decode(samples, images, sd_version, sub_batch_size)
        alpha = 1.0 - mask
        return JoinImageWithAlpha().join_image_with_alpha(image, alpha)