用李希格莱茨格拉特量化提升图像生成与压缩效果
Spherical Leech Quantization for Visual Tokenization and Generation
- 基于李希格莱茨格拉特晶格设计新量化方法，优化了训练稳定性
- 在图像重建与压缩上优于现有最佳方法，比特数略少但质量更高
- 适合追求高效图像生成与低冗余表示的研究者使用
非参数量化因其参数效率和对大规模码本的可扩展性而备受关注。本文通过晶格编码视角统一阐述多种非参数量化方法。晶格几何解释了在训练自编码器时，某些无查找表量化变体（如BSQ）需引入辅助损失项的原因。进一步探索了随机晶格、广义斐波那契晶格及最密球堆积晶格等候选方案。其中，基于李希格莱茨格拉特晶格的量化方法（Λ₂₄-SQ）因高对称性与超球面均匀分布，带来更简化的训练流程和更优的重建-压缩权衡。在图像标记化与压缩任务中，该方法在所有指标上均优于当前最佳的BSQ方法，且比特消耗略低。该优势亦延伸至先进的自回归图像生成框架。
原文摘要 · Abstract (English)
Non-parametric quantization has received much attention due to its efficiency on parameters and scalability to a large codebook. In this paper, we present a unified formulation of different non-parametric quantization methods through the lens of lattice coding. The geometry of lattice codes explains the necessity of auxiliary loss terms when training auto-encoders with certain existing lookup-free quantization variants such as BSQ. As a step forward, we explore a few possible candidates, including random lattices, generalized Fibonacci lattices, and densest sphere packing lattices. Among all, we find the Leech lattice-based quantization method, which is dubbed as Spherical Leech Quantization ($Λ_{24}$-SQ), leads to both a simplified training recipe and an improved reconstruction-compression tradeoff thanks to its high symmetry and even distribution on the hypersphere. In image tokenization and compression tasks, this quantization approach achieves better reconstruction quality across all metrics than BSQ, the best prior art, while consuming slightly fewer bits. The improvement also extends to state-of-the-art auto-regressive image generation frameworks.
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