MaskBit: Embedding-free Image Generation via Bit Tokens

• URL: http://arxiv.org/abs/2409.16211v1
• Date: Tue, 24 Sep 2024 16:12:12 GMT
• Title: MaskBit: Embedding-free Image Generation via Bit Tokens
• Authors: Mark Weber, Lijun Yu, Qihang Yu, Xueqing Deng, Xiaohui Shen, Daniel Cremers, Liang-Chieh Chen,
• Abstract summary: We present an empirical and systematic examination of VQGANs, leading to a modernized VQGAN. A novel embedding-free generation network operating directly on 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.
• Score: 54.827480008982185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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