ShivamDuggal4/UNITE-tokenization-generation

This project helps machine learning researchers efficiently train generative models that create high-fidelity images or molecular structures. It takes raw image datasets (like ImageNet) or molecule data as input and produces a trained model capable of both converting images into a compressed 'latent' representation (tokenization) and generating new, realistic images or molecules from text descriptions or noise. This is ideal for researchers developing new latent diffusion models or exploring efficient training methods for generative AI.