arXiv AI By Bowen Zheng, Weijian Luo, Guang Yang, Colin Zhang, Tianyang Hu

Taming the Entropy Cliff: Variable Codebook Size Quantization for Autoregressive Visual Generation

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arXiv Computer Vision
Sep 23

StableVQ: Practical Guidelines for Stable Vector-Quantized Tokenizer Training

StableVQ introduces practical guidelines to improve training stability for vector‑quantized tokenizers used in image generation models. It addresses instability caused by the entanglement of encoder–decoder and codebook training by proposing three techniques: Dynamic STE for the encoder, Region VQ Loss for the codebook, and a Decoupled Schedule for independent learning rates. Experiments on ImageNet show consistent gains in stability, codebook utilization, and reconstruction quality across various settings.

By Bao Tang, Jiahao Guo, Haoxiang Cao, Wenyu Liu, Changqian Yu, Kun Gai, Xinggang Wang