MaskBit: Embedding-free Image Generation via Bit Tokens
Authors: name: Mark Weber, name: Lijun Yu, name: Qihang Yu, name: Xueqing Deng, name: Xiaohui Shen, name: Daniel Cremers, name: Liang-Chieh Chen

Masked transformer models for class-conditional image generation have become
a compelling alternative to diffusion models. Typically comprising two stages -
an initial VQGAN model for transitioning between latent space and image space,
and a subsequent Transformer model for image generation within latent space -
these frameworks offer promising avenues for image synthesis. In this study, we
present two primary contributions: Firstly, an empirical and systematic
examination of VQGANs, leading to a modernized VQGAN. Secondly, a novel
embedding-free generation network operating directly on bit tokens - a binary
quantized representation of tokens with rich semantics. The first contribution
furnishes a transparent, reproducible, and high-performing VQGAN model,
enhancing accessibility and matching the performance of current
state-of-the-art methods while revealing previously undisclosed details. The
second contribution demonstrates that embedding-free image generation using bit
tokens achieves a new state-of-the-art FID of 1.52 on the ImageNet 256x256
benchmark, with a compact generator model of mere 305M parameters.