| title | SoL-Refiner: Speed-of-Light One-Step Refinement for High-Resolution Video |
|---|---|
| authors | Haozhe Liu, Tian Ye, Shuchen Xue, Yitong Li, Junsong Chen, Haopeng Li, Jincheng Yu, Duomin Wang, Ruihua Zhang, Lei Zhu, Song Han, Enze Xie |
| date | 2026-09-29 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.37969 |
| upvotes | 34 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | High-resolution video generation is expensive, as its cost grows rapidly with the number of spatiotemporal tokens. A practical alternative first generates a lower-resolution video and then applies a refiner, but conventional multi-step refinement introduces a second sampling bottleneck. We present SoL-Refiner, a one-step video refiner that transforms low-resolution model outputs into 4K videos with a single denoising step. Our three-stage recipe combines high-resolution continual training, reinforcement learning (RL) post-training, and a final one-step distillation. We introduce Refiner-Bench, a video refinement benchmark constructed from the outputs of different video generators, and use a shared-input protocol to compare refiners at approximately 2K output resolution. At 2K, the one-step SoL-Refiner outperforms all external refiners on the VBench and UniPercept averages, while at 3840!times!2176 it improves both metrics over the three-step LTX-2.3 Refiner. With the complete acceleration stack, SoL-Refiner achieves an 8.91times speedup in refinement latency over the same baseline in our 2K latency setting. |
cite
@misc{liu2026sol,
title={SoL-Refiner: Speed-of-Light One-Step Refinement for High-Resolution Video},
author={Haozhe Liu and Tian Ye and Shuchen Xue and Yitong Li and Junsong Chen and Haopeng Li and Jincheng Yu and Duomin Wang and Ruihua Zhang and Lei Zhu and Song Han and Enze Xie},
year={2026},
eprint={2609.37969},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.37969}
}