ComfyUI Node: KMeansColor

KMeansColor:

The KMeansColor node is designed to simplify the process of color quantization in images using the K-Means clustering algorithm. This node is particularly useful for AI artists who want to reduce the number of colors in an image to a specified number, creating a more stylized or abstract look. By grouping similar colors together, the node helps in achieving a cohesive color palette, which can be beneficial for various artistic and design purposes. The KMeansColor node leverages the power of the K-Means algorithm to efficiently cluster pixel colors, making it easier to manipulate and analyze the color composition of an image.

KMeansColor Input Parameters:

image

This parameter expects an image input that you want to process. The image should be in a format that the node can interpret, typically a tensor representation of the image.

number_of_colors

This integer parameter specifies the number of color clusters you want to reduce the image to. The default value is 2, with a minimum value of 1. Increasing this number will result in more color clusters, which can retain more details of the original image, while a lower number will create a more abstract representation.

max_iterations

This integer parameter defines the maximum number of iterations the K-Means algorithm will perform. The default value is 100. Higher values can lead to more accurate clustering but will take more time to compute.

eps

This floating-point parameter sets the convergence criteria for the K-Means algorithm. The default value is 0.2, with a step size of 0.05. This value determines the precision of the clustering process; smaller values can lead to more precise clustering but may require more iterations.

KMeansColor Output Parameters:

IMAGE

The output is an image that has been processed to contain only the specified number of colors. This image is returned in a tensor format, making it ready for further processing or analysis. The resulting image will have a stylized look with distinct color clusters, which can be used for various artistic and design applications.

KMeansColor Usage Tips:

• Experiment with the number_of_colors parameter to find the optimal number of color clusters for your specific artistic needs. Fewer colors can create a more abstract look, while more colors can retain more details.
• Adjust the max_iterations parameter if you notice that the clustering process is not converging. Increasing the number of iterations can improve the accuracy of the color clusters.
• Fine-tune the eps parameter to balance between precision and computation time. Smaller values can lead to more precise clustering but may require more iterations to converge.

KMeansColor Common Errors and Solutions:

"Image format not supported"

• Explanation: The input image is not in a format that the node can interpret.
• Solution: Ensure that the image is provided in a tensor format compatible with the node.

"Number of colors must be at least 1"

• Explanation: The number_of_colors parameter is set to a value less than 1. - Solution: Set the number_of_colors parameter to a value of 1 or higher.

"Max iterations must be a positive integer"

• Explanation: The max_iterations parameter is set to a non-positive value.
• Solution: Ensure that the max_iterations parameter is set to a positive integer.

"Eps must be a positive float"

• Explanation: The eps parameter is set to a non-positive value.
• Solution: Set the eps parameter to a positive float value to ensure proper convergence criteria.