ImageTransformPaddingAbsolute

Documentation

• Class name: ImageTransformPaddingAbsolute
• Category: image/transform
• Output node: False

This node applies absolute padding to a batch of images, allowing for the addition of a specified number of pixels to the height and width of each image. The padding method can be chosen from predefined options to control how the added space is filled.

Input types

Required

• images
  • The batch of images to which padding will be applied. This is the primary input for the transformation process.
  • Comfy dtype: IMAGE
  • Python dtype: List[torch.Tensor]
• add_width
  • The number of pixels to add to the width of each image.
  • Comfy dtype: INT
  • Python dtype: int
• add_height
  • The number of pixels to add to the height of each image.
  • Comfy dtype: INT
  • Python dtype: int
• method
  • The method used to fill the added padding area, with options including 'reflect', 'edge', and 'constant'.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Output types

• image
  • Comfy dtype: IMAGE
  • The batch of images after padding has been applied, with increased dimensions according to the specified add_width and add_height.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ImageTransformPaddingAbsolute:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE",),
                "add_width": ("INT", {
                    "default": 64,
                    "min": 0,
                }),
                "add_height": ("INT", {
                    "default": 64,
                    "min": 0,
                }),
                "method": (["reflect", "edge", "constant"],),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "node"
    CATEGORY = "image/transform"

    def node(self, images, add_width, add_height, method):
        def transpose_tensor(image):
            tensor = image.clone().detach()
            tensor_pad = TF.pad(tensor.permute(2, 0, 1), [add_height, add_width], padding_mode=method).permute(1, 2, 0)

            return tensor_pad

        return (torch.stack([
            transpose_tensor(images[i]) for i in range(len(images))
        ]),)