Overview

ProCache speeds up Diffusion Transformers with constraint-aware feature caching and selective computation, trading redundant work for quality-preserving inference.

📣 News

• 2026/03/21 📌 The code has been released.
• 2025/11/08 💥💥 ProCache is honored to be accepted by AAAI 2026!

Experiments

To run experiments with ProCache, follow the instructions in the model-specific markdown files in this repository (each backbone has its own guide):

• DiT-ProCache.md — DiT
• FLUX-ProCache.md — FLUX
• PixArt-ProCache.md — PixArt-α

TODO

• HunyuanVideo: We plan to add ProCache-style acceleration for HunyuanVideo (text-to-video) in a future release.

Acknowledgement

ProCache is built upon the awesome ToCa and TaylorSeer. We are grateful to the authors for their open releases and for advancing feature caching in diffusion transformers.

📌 Citation

@inproceedings{cao2026procache,
  title={ProCache: Constraint-Aware Feature Caching with Selective Computation for Diffusion Transformer Acceleration},
  author={Cao, Fanpu and Chen, Yaofo and You, Zeng and Luo, Wei},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={40},
  number={24},
  pages={19862--19870},
  year={2026}
}

:e-mail: Contact

If you have any questions, please email fanpucao@gmail.com or chenyaofo@scut.edu.cn.