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Perform color quantization on images using K-means clustering for stylized visual effects and reduced complexity.
The ImageMagick Kmeans node is designed to perform color quantization on images using the K-means clustering algorithm. This process reduces the number of distinct colors in an image, which can be particularly useful for creating a more stylized or simplified visual effect. By grouping similar colors together, the node helps in reducing the complexity of an image while maintaining its essential visual characteristics. This can be beneficial for various artistic applications, such as creating a specific color palette, reducing file size, or preparing images for further processing. The Kmeans node is a powerful tool for AI artists looking to manipulate and enhance their images with precision and creativity.
This parameter represents the input image that you want to process using the Kmeans algorithm. The image should be in a compatible format that the node can handle.
This parameter specifies the number of colors you want the output image to have after the Kmeans clustering. The default value is 16, with a minimum of 1 and a maximum of 1024. Adjusting this value will directly impact the level of color detail in the resulting image.
This parameter defines the maximum number of iterations the Kmeans algorithm will perform to converge to a solution. The default value is 100, with a minimum of 0 and a maximum of 1024. Increasing the number of iterations can lead to a more accurate color quantization but may also increase processing time.
This parameter sets the tolerance level for the convergence of the Kmeans algorithm. The default value is 0.01, with a minimum of 0.0 and a maximum of 1024. A lower tolerance value can result in a more precise clustering but may require more iterations to achieve convergence.
The output parameter is the processed image with the reduced number of colors as specified by the number_colors
input parameter. This image retains the essential visual characteristics of the original but with a simplified color palette, making it suitable for various artistic and practical applications.
number_colors
parameter to find the optimal balance between color detail and simplicity for your specific artistic needs.max_iterations
value if you notice that the color quantization is not as accurate as desired, but be mindful of the increased processing time.tolerance
parameter to fine-tune the precision of the Kmeans clustering. A lower tolerance can lead to more accurate results but may require more iterations.number_colors
parameter is set to a value outside the allowed range (1-1024).number_colors
parameter is set within the valid range.max_iterations
.max_iterations
parameter to allow more iterations for the algorithm to converge.tolerance
parameter is set to a value outside the allowed range (0.0-1024).tolerance
parameter is set within the valid range.© Copyright 2024 RunComfy. All Rights Reserved.