Video diffusion transformers (vDiTs) have made tremendous progress in text-to-video generation, but their high compute demands pose a major challenge for practical deployment. While studies propose acceleration methods to reduce workload at various granularities, they often rely on heuristics, limiting their applicability. We introduce Astraea, a framework that searches for near-optimal configurations for vDiT-based video generation under a performance target. At its core, Astraea proposes a lightweight token selection mechanism and a memory-efficient, GPU-friendly sparse attention strategy, enabling linear savings on execution time with minimal impact on generation quality. Meanwhile, to determine optimal token reduction for different timesteps, we further design a search framework that leverages a classic evolutionary algorithm to automatically determine the distribution of the token budget effectively. Together, Astraea achieves up to 2.4× inference speedup on a single GPU with great scalability (up to 13.2× speedup on 8 GPUs) while achieving up to over 10~dB video quality compared to the state-of-the-art methods (<0.5\% loss on VBench compared to baselines).
@article{arxiv.2506.05096,
title = {Astraea: A Token-wise Acceleration Framework for Video Diffusion Transformers},
author = {Haosong Liu and Yuge Cheng and Wenxuan Miao and Zihan Liu and Aiyue Chen and Jing Lin and Yiwu Yao and Chen Chen and Jingwen Leng and Yu Feng and Minyi Guo},
journal= {arXiv preprint arXiv:2506.05096},
year = {2025}
}