Channel-wise Vector Quantization

Researchers introduced Channel-wise Vector Quantization, which tokenizes images by feature-map channels instead of spatial patches.

A next-channel autoregressive model then generates images by refining details channel by channel.

The method achieves full codebook utilization and stronger reconstruction quality than prior approaches.

It also shows solid benchmark gains for text-to-image generation, including DPG and GenEval.