ImageTransformPaddingRelative

Documentation

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

This node applies relative padding to images based on specified scale factors for width and height, and a padding method. It dynamically calculates the amount of padding to add to each dimension of the images, allowing for flexible image transformations that maintain the aspect ratio.

Input types

Required

• images
  • The collection of images to which padding will be applied. This parameter is crucial for defining the input data that will undergo the transformation.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• scale_width
  • A scale factor for determining the width of the padding relative to the original image width. It influences the final size of the padded image.
  • Comfy dtype: FLOAT
  • Python dtype: float
• scale_height
  • A scale factor for determining the height of the padding relative to the original image height. It influences the final size of the padded image.
  • Comfy dtype: FLOAT
  • Python dtype: float
• method
  • The method used for padding (e.g., reflect, edge, constant), affecting the appearance of the padded areas.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Output types

• image
  • Comfy dtype: IMAGE
  • The transformed images with added padding, maintaining the original aspect ratio while adjusting the overall dimensions.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ImageTransformPaddingRelative:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE",),
                "scale_width": ("FLOAT", {
                    "default": 0.25,
                    "step": 0.1,
                }),
                "scale_height": ("FLOAT", {
                    "default": 0.25,
                    "step": 0.1,
                }),
                "method": (["reflect", "edge", "constant"],),
            },
        }

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

    def node(self, images, scale_width, scale_height, method):
        height, width = images[0, :, :, 0].shape

        add_width = int(width * scale_width)
        add_height = int(height * scale_height)

        return ImageTransformPaddingAbsolute().node(images, add_width, add_height, method)