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Versatile image manipulation node for monochrome conversion with color channel isolation and threshold-based transformations.
The MonoClip node is a versatile tool designed for image manipulation, specifically focusing on converting images to monochrome using various clipping methods. This node allows you to isolate and process specific color channels (red, green, blue, or greyscale) and apply different threshold-based transformations to achieve desired visual effects. Whether you are looking to create binary images, apply inverse transformations, or truncate pixel values, MonoClip provides a range of modes to cater to your artistic needs. This node is particularly useful for AI artists who want to add unique monochrome effects to their images, enhancing their creative projects with ease.
This parameter expects an image input that you want to process. The image should be in a format compatible with the node, typically a tensor or array representation of the image.
This parameter allows you to select the color channel you wish to isolate and process. The available options are "red", "green", "blue", and "greyscale". By default, it is set to "greyscale". Selecting a specific channel will focus the transformation on that channel, which can significantly impact the final output.
The threshold parameter is an integer value that determines the cutoff point for the various clipping modes. It ranges from 0 to 255, with a default value of 0. This value is used to decide how pixel values are transformed based on the selected mode, influencing the intensity and appearance of the resulting image.
This parameter defines the type of transformation to apply to the image. The available modes are "binary", "inverse binary", "to zero", "inverse to zero", "truncate", and "inverse truncate". Each mode applies a different threshold-based transformation, allowing you to achieve various monochrome effects. The default mode is "binary".
The output is the processed image, which has been converted to monochrome based on the selected channel, threshold, and mode. The resulting image is returned as a tensor or array, ready for further use or display. This output allows you to incorporate the transformed image into your creative workflow seamlessly.
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