[Inference.Core] Layer Diffuse Decode (RGBA)

Documentation

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

This node is designed to decode the alpha channel from pixel values in images, effectively converting RGB images to RGBA format. It leverages a layered diffusion process to enhance image quality by adding transparency information, thereby facilitating more nuanced image manipulation and generation tasks.

Input types

Required

• samples
  • Represents the input samples to be decoded, playing a crucial role in determining the final image output by providing the necessary data for the alpha channel decoding process.
  • Comfy dtype: LATENT
  • Python dtype: Dict[str, Any]
• images
  • The input images in tensor format, which are to be processed to decode the alpha channel and convert from RGB to RGBA format.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• sd_version
  • Specifies the version of the stable diffusion model to be used for decoding, impacting the decoding process and the quality of the output images.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• sub_batch_size
  • Determines the size of sub-batches for processing, affecting the efficiency and speed of the decoding operation.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• image
  • Comfy dtype: IMAGE
  • The output RGBA images, where the alpha channel has been decoded and added to the original RGB images, enhancing their representation with transparency information.
  • 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)