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[NeurIPS 2025 Spotlight] LeMiCa: Lexicographic Minimax Path Caching for Efficient Diffusion-Based Video Generation

Huanlin Gao^1,2*,  Ping Chen^1,2*,  Fuyuan Shi^1,2,  Chao Tan^1,2,  Zhaoxiang Liu^1,2
Fang Zhao^1,2†,  Kai Wang^1,2,  Shiguo Lian^1,2

¹Data Science & Artificial Intelligence Research Institute, China Unicom,  ²Unicom Data Intelligence, China Unicom

(* Equal contribution. † Corresponding author.)

Introduction

LeMiCa is a training-free acceleration framework for diffusion-based video generation (and extendable to image generation). Instead of using local heuristic thresholds, LeMiCa formulates cache scheduling as a global path optimization problem with error-weighted edges and introduces a Lexicographic Minimax strategy to bound the worst-case global error. This global planning improves both inference speed and consistency across frames. For more details and visual results, please visit our project page.

🔥 Latest News

• [2025/11/07] 🔥 Support Qwen-Image and Inference Code Released !
• [2025/10/29] 🚀 Code will be released soon !
• [2025/09/18] ✨ Selected as a NeurIPS 2025 Spotlight paper.
• [2025/09/18] ✨ Initial public release of LeMiCa.

Demo

Open-Sora

opensora_grid_5x5_with_header_bold.mp4

Qwen-Image

Supported Models

LeMiCa currently supports and has been tested on the following diffusion-based models:

Text-to-Video

• Open-Sora
• Latte
• CogVideoX 1.5
• HunyuanVideo

Text-to-Image

• Qwen-Image

• FLUX.1

ToDo List

• 🗹 Public Project Page
• 🗹 Paper Released
• ☐ Text-to-Image Forward Inference
• ☐ Text-to-Video Forward Inference
• ☐ DAG Construction Code
• ☐ Support Acceleration Framework

Acknowledgement

This repository is built based on or inspired by the following open-source projects: Diffusers, Qwen-Image, TeaCache, VideoSys. We sincerely thank these communities for their open contributions and inspiration.

License

The majority of this project is released under the Apache 2.0 license as found in the LICENSE file.

📖 Citation

If you find LeMiCa useful in your research or applications, please consider giving us a star ⭐ and citing it by the following BibTeX entry:

@inproceedings{gao2025lemica,
  title     = {LeMiCa: Lexicographic Minimax Path Caching for Efficient Diffusion-Based Video Generation},
  author    = {Huanlin Gao and Ping Chen and Fuyuan Shi and Chao Tan and Zhaoxiang Liu and Fang Zhao and Kai Wang and Shiguo Lian},
  journal   = {Advances in Neural Information Processing Systems (NeurIPS)},
  year      = {2025},
  url       = {https://arxiv.org/abs/2511.00090}
}