Despite rapid progress in auto-regressive video diffusion, we identify an emerging system–algorithm bottleneck that limits both deployability and capability: KV-cache memory. In auto-regressive video generation models, the KV-cache grows with history and rapidly dominates GPU memory (often ≥ 30 GB), blocking deployment on widely available hardware. More importantly, memory-bounded KV budgets force small working memory, which directly degrades long-horizon consistency in identity, layout, and motion, etc. To bridge this gap, we present Quant VideoGen (QVG), a training-free KV-cache quantization framework for auto-regressive video diffusion model. QVG exploits video’s spatiotemporal redundancy through Semantic-Aware Smoothing to produce low-magnitude, quantization-friendly residuals. QVG further propose Progressive Residual Quantization, a coarse-to-fine multi-stage scheme that reduces quantization error while enabling a smooth quality–memory trade-off. Across LongCat-Video, HY-WorldPlay, and Self-Forcing, QVG establishes a new Pareto quality-memory frontier, reducing KV memory by up to 7.0× with < 4% end-to-end latency overhead and significantly better quality over baselines.
@article{xi2026quantvideogen,
title = {{Quant VideoGen}: Auto-Regressive Long Video Generation via 2-Bit KV-Cache Quantization},
author = {Xi, Haocheng and Yang, Shuo and Zhao, Yilong and Li, Muyang and Cai, Han and
Li, Xingyang and Lin, Yujun and Zhang, Zhuoyang and Zhang, Jintao and Li, Xiuyu and
Xu, Zhiying and Wu, Jun and Xu, Chenfeng and Stoica, Ion and Han, Song and Keutzer, Kurt},
journal = {arXiv preprint arXiv:2602.02958},
year = {2026},
}
