yuliusharjoseputro/KmeansSEAPixmodel — reverse-engineered prompt

I want a working Python project for textile screen printing that can learn from example images and then automatically separate colors and correct them for print prep. The main user flow should be simple, I should be able to drop in training images, train the model, then run it on a new design and get clean separated output images saved in a results folder.

Please use the idea from this repo, combining image clustering with a conditional image generation model and some attention based improvement, but keep it practical and end to end instead of feeling like disconnected research code. If there are already pieces for datasets, checkpoints, augmentation, and scoring, wire them together so training, inference, and evaluation actually run without a lot of manual fixing.

I also want a clear way to measure output quality with the existing similarity and overlap style metrics, and I want saved checkpoints and sample results to be easy to inspect. Look up current docs online if you need to.