PermuQuant: Lowering Per-Group Quantization Error by Reordering Channels for Diffusion Models
Large-scale visual generative models have achieved remarkable performance. However, their high computational and memory costs make deployment challenging in resource-constrained scenarios, such as interactive applications and personal single-GPU usage. Post-training quantization (PTQ) offers a practical solution by compressing pretrained models without expensive retraining. However, existing PTQ methods still suffer from severe quality degradation under extremely low-bit settings. In this paper, we identify channel ordering as an important but underexplored factor in per-group quantization. In this setting, each contiguous group shares one quantization scale. When channels with very diffe...
| Authors | Yongsen Cheng, Kai Liu, Kaiwen Tao et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 May 2026 |
| Last updated at source | 31 May 2026 |
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