gulucaptain/easy-vqgan

Easy-vqgan makes it straightforward to use and adapt a type of AI image model called VQ-GAN. VQ-GANs learn to compress images into a compact code and then reconstruct them back into pictures. This project provides ready-to-run scripts so you can quickly generate or rebuild images using pre-trained models, without having to build the complex underlying machinery yourself. At a high level, VQ-GAN works in two stages: first, an encoder and decoder learn to reconstruct images, and second, a decoder pairs with a transformer to create new images. This project focuses on the first stage, which is all about teaching the model to accurately rebuild an image after it has been compressed. You can run a simple script to see this reconstruction in action using existing models, or you can fine-tune the model on your own set of images to teach it the specific visual style or details of your data. This would be useful for anyone who wants to experiment with image generation or compression using their own custom dataset. For example, if you have a specific collection of images, like product photos, medical scans, or artwork, and you want an AI model to better understand and reconstruct them, you can use this tool to fine-tune the model on that data. The project includes a sample dataset of COCO images to show you how the training process works. To get started, you download the provided model checkpoints, run an inference script to see the baseline results, and then adjust a configuration file to point to your own images. After training, a second inference script lets you compare how well the model reconstructs images before and after you fine-tuned it, so you can see the improvement. The outputs are saved to a logs folder for easy review. The project is designed to simplify a process that can otherwise be difficult for beginners. It bundles the necessary setup steps and provides clear scripts for both running the model and training it on custom data, though it expects you to manually edit specific lines in the configuration files to point to your images and model paths.