FasterCache

<div align="center"> <h1>FasterCache: Training-Free Video Diffusion Model Acceleration with High Quality</h1></div> <div align="center"> <a href="https://scholar.google.com/citations?user=FkkaUgwAAAAJ&hl=en" target="_blank">Zhengyao Lv</a>¹ | <a href="https://chenyangsi.github.io/" target="_blank">Chenyang Si</a>^2‡ | <a href="" target="_blank">Junhao Song</a>³ | <a href="" target="_blank">Zhenyu Yang</a>³ | <a href="https://mmlab.siat.ac.cn/yuqiao" target="_blank">Yu Qiao</a>³ | <a href="https://liuziwei7.github.io/" target="_blank">Ziwei Liu</a>^2† | <a href="https://i.cs.hku.hk/~kykwong/" target="_blank">Kwan-Yee K. Wong</a>^1† </div> <div align="center"> ¹The University of Hong Kong &nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ²S-Lab, Nanyang Technological University <br> ³Shanghai Artificial Intelligence Laboratory </div> <div align="center">(‡: Project lead; †: Corresponding authors)</div> <p align="center"> <a href="https://arxiv.org/abs/2410.19355">Paper</a> | <a href="https://vchitect.github.io/FasterCache/">Project Page</a> </p> <p align="center"> <a href="https://hits.seeyoufarm.com"><img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2FVchitect%2FFasterCache&count_bg=%2379C83D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=Github+visitors&edge_flat=false"/></a> <a href="https://hits.seeyoufarm.com"><img src="https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fvchitect.github.io%2FFasterCache%2F&count_bg=%23C83D5D&title_bg=%23555555&icon=&icon_color=%23E7E7E7&title=Pages+visitors&edge_flat=false"/></a> </p>

About

We present FasterCache, a novel training-free strategy designed to accelerate the inference of video diffusion models with high-quality generation. For more details and visual results, go checkout our Project Page.

https://github.com/user-attachments/assets/035c50c2-7b74-4755-ac1e-e5aa1cffba2a

News

• (🔥 New) 2024/11/8 We support the multi-device inference script for CogvideoX
• (🔥 New) 2024/11/8 We implemented FasterCache based on the Mochi

Usage

Installation

Run the following instructions to create an Anaconda environment.

conda create -n fastercache python=3.10 -y
conda activate fastercache
git clone https://github.com/Vchitect/FasterCache
cd FasterCache
pip install -e .

Inference

We currently support Open-Sora 1.2, Open-Sora-Plan 1.1, Latte, CogvideoX-2B&5B, Vchitect 2.0 and Mochi. You can achieve accelerated sampling by executing the scripts we provide.

• Open-Sora

  For single-GPU inference on Open-Sora, run the following command:

  bash scripts/opensora/fastercache_sample_opensora.sh
  

  For multi-GPU inference on Open-Sora, run the following command:

  bash scripts/opensora/fastercache_sample_multi_device_opensora.sh
  

• Open-Sora-Plan

  For single-GPU inference on Open-Sora-Plan, run the following command:

  bash scripts/opensora_plan/fastercache_sample_opensoraplan.sh
  

  For multi-GPU inference on Open-Sora-Plan, run the following command:

  bash scripts/opensora_plan/fastercache_sample_multi_device_opensoraplan.sh
  

• Latte

  For single-GPU inference on Latte, run the following command:

  bash scripts/latte/fastercache_sample_latte.sh
  

  For multi-GPU inference on Latte, run the following command:

  bash scripts/latte/fastercache_sample_multi_device_latte.sh
  

• CogVideoX

  For single-GPU or multi-GPU batched inference on CogVideoX-2B, run the following command:

  bash scripts/cogvideox/fastercache_sample_cogvideox.sh
  

  For multi-GPU inference on CogVideoX-2B, run the following command:

  bash scripts/cogvideox/fastercache_sample_cogvideox_multi_device.sh
  

  For inference on CogVideoX-5B, run the following command:

  bash scripts/cogvideox/fastercache_sample_cogvideox5b.sh
  

• Vchitect 2.0

  For inference on Vchitect 2.0, run the following command:

  bash scripts/vchitect/fastercache_sample_vchitect.sh
  

• Mochi

  We also provide acceleration scripts for Mochi. Before running these scripts, please follow the official Mochi repository to complete model downloads, environment setup, and installation of genmo. Then, execute the following script:

  bash scripts/mochi/fastercache_sample_mochi.sh 
  

BibTeX

@inproceedings{lv2024fastercache,
  title={FasterCache: Training-Free Video Diffusion Model Acceleration with High Quality},
  author={Lv, Zhengyao and Si, Chenyang and Song, Junhao and Yang, Zhenyu and Qiao, Yu and Liu, Ziwei and Kwan-Yee K. Wong},
  booktitle={arxiv},
  year={2024}
}

Acknowledgement

This repository borrows code from VideoSys, Vchitect-2.0, Mochi, and CogVideo,.Thanks for their contributions!