Difference Checker

Documentation

• Class name: DifferenceChecker
• Category: image/filters
• Output node: False

The DifferenceChecker node is designed to compute the absolute difference between two images, scaled by a specified multiplier. This functionality is useful for highlighting differences or changes between two images, which can be applied in various image processing and analysis tasks.

Input types

Required

• images1
  • The first set of images to compare. This input is crucial for determining the baseline for comparison.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• images2
  • The second set of images to compare against the first set. This input is essential for identifying the differences from the baseline.
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• multiplier
  • A scaling factor applied to the computed differences, allowing for adjustment of the difference intensity.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• image
  • Comfy dtype: IMAGE
  • The output is an image or a set of images representing the scaled absolute difference between the input images.
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class DifferenceChecker:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "images1": ("IMAGE", ),
                "images2": ("IMAGE", ),
                "multiplier": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 1000.0, "step": 0.01,  "round": 0.01}),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "difference_checker"

    CATEGORY = "image/filters"

    def difference_checker(self, images1, images2, multiplier):
        t = copy.deepcopy(images1)
        t = torch.abs(images1 - images2) * multiplier
        return (torch.clamp(t, min=0, max=1),)