Masked-Token Model

Key Insight

Generating image tokens strictly one at a time is slow. A MaskGIT-style masked-token model speeds this up by predicting many tokens at once: it starts from a grid where most tokens are hidden, fills in the ones it is most confident about, then repeats — refining the whole image in a handful of passes instead of hundreds. The analogy is solving a crossword by first writing in the answers you are sure of, which then make the remaining blanks easier to guess. This project implements such a parallel decoder over the same VQ-GAN tokens and compares its sampling speed and quality against the row-by-row transformer from the previous project.