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Image enhancement and refinement node for superior quality output with advanced algorithms for detail enhancement.
The MaraScottMcBoatyRefiner_v5 node is designed to enhance and refine images, providing a high level of detail and quality. This node is part of a series of tools aimed at upscaling and refining images, ensuring that the final output is of superior quality. The primary goal of this node is to take an already upscaled image and apply additional refinement processes to enhance its visual appeal. This can be particularly useful for AI artists looking to improve the clarity and detail of their images without losing the original artistic intent. The node leverages advanced algorithms to smooth out imperfections and bring out finer details, making it an essential tool for anyone looking to produce high-quality digital art.
This parameter accepts the image that you want to refine. The input image should be in a compatible format and resolution that the node can process effectively. The quality of the input image can significantly impact the final output, so it is recommended to use a high-resolution image for the best results.
This parameter controls the intensity of the refinement process. It typically ranges from 1 to 10, with 1 being the least intense and 10 being the most intense. Adjusting this parameter allows you to fine-tune the level of detail and smoothness applied to the image. A higher refinement level will result in a more polished and detailed image, but it may also increase processing time.
This parameter determines the amount of noise reduction applied during the refinement process. It usually ranges from 0 to 1, where 0 means no noise reduction and 1 means maximum noise reduction. This is useful for cleaning up any unwanted artifacts or graininess in the image, resulting in a cleaner and more professional look.
The refined_image parameter provides the final output image after the refinement process. This image will have enhanced details and reduced imperfections, making it more visually appealing. The quality of the refined image depends on the input parameters and the original image quality.
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