🔧 Image Enhance Difference

Documentation

• Class name: ImageEnhanceDifference+
• Category: essentials
• Output node: False

This node is designed to enhance the difference between two images by applying a power transformation. It aims to highlight the disparities between the images, potentially for further analysis or visual effect enhancement.

Input types

Required

• image1
  • The first image to compare. It serves as the baseline for the enhancement process.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• image2
  • The second image to compare. This image is adjusted to match the first image's dimensions if necessary, before the difference enhancement.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• exponent
  • A factor that controls the intensity of the enhancement. Higher values increase the contrast between the differences.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• image
  • Comfy dtype: IMAGE
  • The enhanced difference image, highlighting disparities between the input images.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ImageEnhanceDifference:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "image1": ("IMAGE",),
                "image2": ("IMAGE",),
                "exponent": ("FLOAT", { "default": 0.75, "min": 0.00, "max": 1.00, "step": 0.05, }),
            }
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "execute"
    CATEGORY = "essentials"

    def execute(self, image1, image2, exponent):
        if image1.shape != image2.shape:
            image2 = p(image2)
            image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center')
            image2 = pb(image2)

        diff_image = image1 - image2
        diff_image = torch.pow(diff_image, exponent)
        diff_image = torch.clamp(diff_image, 0, 1)

        return(diff_image,)