StableVQ: Practical Guidelines for Stable Vector-Quantized Tokenizer Training
Vector Quantization (VQ) is fundamental to discrete visual tokenizers that power modern autoregressive and masked image generation models. While recent shared-projection codebook methods have substantially advanced codebook utilization, training stability remains a critical and underexplored challenge. We argue that the root cause lies in the entanglement of the Encoder--Decoder and Codebook training: because neither module can reliably fulfill its own responsibility in isolation, the system can only function when the two subsystems happen to cooperate---a fragile condition that breaks down precisely when training is most stressed. We propose StableVQ, which revisits the proper learning o...
| Authors | Bao Tang, Jiahao Guo, Haoxiang Cao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 September 2026 |
| Last updated at source | 22 September 2026 |
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