MaskBit: Embedding-free Image Generation via Bit Tokens
<|reference_start|>MaskBit: Embedding-free Image Generation via Bit Tokens: 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.<|reference_end|>
@article{weber2024maskbit:,
    title={MaskBit: Embedding-free Image Generation via Bit Tokens},
    author={Mark Weber, Lijun Yu, Qihang Yu, Xueqing Deng, Xiaohui Shen, Daniel
  Cremers, Liang-Chieh Chen},
    journal={arXiv preprint arXiv:2409.16211},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.16211},
    primaryClass={cs.CV cs.LG}
}