Taming the Entropy Cliff: Variable Codebook Size Quantization for Autoregressive Visual Generation
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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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.
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