| title | LongLive-Plug: Once-for-All Distillation for Video Generation |
|---|---|
| authors | Shuai Yang, Luozhou Wang, Wei Huang, ZhiFei Chen, Bohan Zhang, Xiao Fu, Qianli Ma, Chen-Hsuan Lin, Weian Mao, Bryan Chu, Song Han, Yukang Chen |
| date | 2026-09-29 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.38154 |
| affiliation | NVIDIA |
| upvotes | 29 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Video diffusion models are increasingly developed into specialized models for diverse downstream tasks, and this development often includes a distillation stage, for example to accelerate sampling or to improve long-video generation. This stage is typically repeated for every specialized model. We introduce LongLive-Plug, a once-for-all distillation framework that learns reusable capabilities as LoRAs on a base model for training-free, plug-and-play deployment to compatible downstream models. These capabilities include single-pass classifier-free guidance, few-step sampling, and long-context error correction for autoregressive generation. The adapters remain reusable even when downstream models add conditioning branches, expand output channels. Despite training at a fixed guidance scale, our dedicated CFG LoRA provides text guidance control through its inference weight. Combining it with a few-step LoRA simultaneously preserves few-step generation and CFG controllability on downstream tasks. We verify training-free deployment on 54 downstream models across three backbone families and eight task categories, including world modeling, robotics, editing, and multimodal generation. The approach may support additional compatible models. Each capability can thus be distilled once per backbone family and reused without per-target retraining. |
cite
@misc{yang2026longlive,
title={LongLive-Plug: Once-for-All Distillation for Video Generation},
author={Shuai Yang and Luozhou Wang and Wei Huang and ZhiFei Chen and Bohan Zhang and Xiao Fu and Qianli Ma and Chen-Hsuan Lin and Weian Mao and Bryan Chu and Song Han and Yukang Chen},
year={2026},
eprint={2609.38154},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.38154}
}