Improving Autoregressive Image Generation through Coarse-to-Fine Token
  Prediction
Authors: name: Ziyao Guo, name: Kaipeng Zhang, name: Michael Qizhe Shieh

Autoregressive models have shown remarkable success in image generation by
adapting sequential prediction techniques from language modeling. However,
applying these approaches to images requires discretizing continuous pixel data
through vector quantization methods like VQ-VAE. To alleviate the quantization
errors that existed in VQ-VAE, recent works tend to use larger codebooks.
However, this will accordingly expand vocabulary size, complicating the
autoregressive modeling task. This paper aims to find a way to enjoy the
benefits of large codebooks without making autoregressive modeling more
difficult. Through empirical investigation, we discover that tokens with
similar codeword representations produce similar effects on the final generated
image, revealing significant redundancy in large codebooks. Based on this
insight, we propose to predict tokens from coarse to fine (CTF), realized by
assigning the same coarse label for similar tokens. Our framework consists of
two stages: (1) an autoregressive model that sequentially predicts coarse
labels for each token in the sequence, and (2) an auxiliary model that
simultaneously predicts fine-grained labels for all tokens conditioned on their
coarse labels. Experiments on ImageNet demonstrate our method's superior
performance, achieving an average improvement of 59 points in Inception Score
compared to baselines. Notably, despite adding an inference step, our approach
achieves faster sampling speeds.