FSQ Tokenizer

Key Insight

FSQ (Finite Scalar Quantization) is a surprisingly simple way to make image tokens without learning a codebook at all. Instead of looking up the nearest entry in a trained table, it just rounds each number in the latent to the nearest value on a fixed grid — like snapping every measurement to the nearest tick on a ruler. Because there is no codebook to train, FSQ sidesteps codebook collapse entirely and is far easier to get working. This project implements FSQ and compares it to a learned VQ-VAE on reconstruction FID: the learned VQ-VAE is usually a little better, but in many settings the much simpler FSQ matches it closely — that is what "holds its own" means here.