CrossDistill is a trajectory-level hybrid few-step distillation framework for diffusion models that balances quality and diversity by splitting the sampling trajectory at a crossover point. The high-noise interval uses a trajectory-preserving objective to maintain global mode coverage, while the low-noise interval applies a distribution-matching objective to sharpen local details, with the two stages coupled through the crossover state. This noise-level scheduling policy, demonstrated on text-to-video and image-to-video diffusion models, expands the few-step quality-diversity frontier by preserving seed-level variation while achieving competitive visual fidelity.
By Yuxi Liu, Haoyu Li, Yixiang Cai, Tengxu Sun, Zekun Zhang, Baole Ai, Ang Wang, Jiamang Wang, Lin Qu, Kun Yuan, Kai Zhang
Diffusion models have enabled high-quality video generation in recent years, but the high cost of iterative sampling hinders their practical deployment. Few-step distillation alleviates this cost, yet exposes a quality--diversity trade-off between its two dominant paradigms: trajectory-level distillation (e.
arXiv:2608. 09637v1 Announce Type: cross Abstract: Diffusion models have enabled high-quality video generation in recent years, but the high cost of iterative sampling hinders their practical deployment.
By Zian Li, Litong Gong, Borui Liao, Pengfei Liu, Xinyu Wang, Xinyuan Wei, Yifan Gao, Tiezheng Ge, Muhan Zhang
The paper introduces DM-Align, a single-stage optimization framework that jointly performs distribution matching for distillation and aligns video generative models with human preferences. By deriving complementary gradient directions—one minimizing the gap between real and fake models and another guiding the model toward preferred samples—the method eliminates the need for separate reinforcement learning and distillation stages. Experiments on multiple foundational video models show that this sample-guided approach consistently outperforms both standalone variants and traditional two-stage pipelines.
By Jiuzhou Lin, Junlong Wu, Fei Zuo, Huan Ouyang, Dewen Fan, Boheng Zhang, Huaiqing Wang, Jia Sun, Fan Yang, Houde Liu, Kehai Chen, Min Zhang, Tingting Gao, Han Li
The paper introduces Probability‑Flow Distillation (PFD), a new method for matching parameter distributions in diffusion‑based models. It extends the particle variational inference framework of Variational Score Distillation to Score Distillation Sampling (SDS) and Score Distillation via Inversion (SDI), revealing that SDS focuses on mode collapse while SDI converges to a contracted distribution. By replacing a single Euler step in SDI with a full reverse probability‑flow ODE solve and simplifying the gradient, PFD achieves distribution matching with only a forward ODE solve, and experiments on synthetic data, CelebA, and text‑to‑3D tasks confirm its effectiveness.
By Rohith Ramanan, A. N. Rajagopalan
arXiv:2606.03746v3 Announce Type: replace-cross
Abstract: Few-step distillation has emerged as a critical component in the development of advanced visual generative foundation models, substantially r...
By Tianhe Wu, Zikai Zhou, Kun Yan, Kaiyuan Gao, Lihan Jiang, Jiahao Li, Jie Zhang, Ningyuan Tang, Shengming Yin, Xiaoyue Chen, Xiao Xu, Yilei Chen, Yuxiang Chen, Yan Shu, Yixian Xu, Yanran Zhang, Zihao Liu, Zhendong Wang, Zekai Zhang, Deqing Li, Liang Peng, Yi Wang, Zeke Xie, Jingren Zhou, Bo Zheng, Chenfei Wu