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Node highlights differences between images for quality control, image comparison, and visual effects by amplifying discrepancies.
The DifferenceChecker node is designed to compare two sets of images and highlight the differences between them. This node is particularly useful for identifying changes or discrepancies between two images, which can be beneficial in various image processing tasks such as quality control, image comparison, and visual effect creation. By calculating the absolute difference between corresponding pixels in the two images and applying a multiplier, the node amplifies the differences, making them more noticeable. This can help you quickly spot even minor changes that might otherwise go unnoticed.
This parameter represents the first set of images to be compared. It is essential to provide images that you want to use as the baseline for comparison. The images should be in a compatible format that the node can process.
This parameter represents the second set of images to be compared against the first set. These images will be compared pixel-by-pixel with the images provided in the images1
parameter. Ensure that the images in this set correspond to the images in the images1
set in terms of dimensions and format.
The multiplier
parameter is a floating-point value that amplifies the differences between the two sets of images. The default value is 1.0, with a minimum value of 0.01 and a maximum value of 1000.0. Adjusting this value allows you to control the sensitivity of the difference detection. A higher multiplier will make even small differences more pronounced, while a lower multiplier will make the node less sensitive to minor changes.
The output of the DifferenceChecker node is a set of images that highlight the differences between the input images. The differences are calculated as the absolute difference between corresponding pixels in the two input sets, multiplied by the specified multiplier. The resulting images are clamped to ensure that pixel values remain within a valid range, making the differences visually discernible.
images1
and images2
are aligned and of the same dimensions. Misaligned or differently sized images can lead to inaccurate difference detection.multiplier
parameter to find the optimal value for your specific use case. Start with the default value and adjust as needed to either amplify or reduce the sensitivity of the difference detection.<filename>
"images1
and images2
do not have the same dimensions.© Copyright 2024 RunComfy. All Rights Reserved.