https://github.com/Vchitect/FasterCache
[ICLR 2025] FasterCache: Training-Free Video Diffusion Model Acceleration with High Quality
https://github.com/Vchitect/FasterCache
Last synced: about 1 month ago
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[ICLR 2025] FasterCache: Training-Free Video Diffusion Model Acceleration with High Quality
- Host: GitHub
- URL: https://github.com/Vchitect/FasterCache
- Owner: Vchitect
- Created: 2024-10-25T06:07:48.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-12-27T08:57:19.000Z (5 months ago)
- Last Synced: 2025-03-21T11:07:17.707Z (about 2 months ago)
- Language: Python
- Homepage:
- Size: 77.5 MB
- Stars: 201
- Watchers: 2
- Forks: 10
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-diffusion-categorized - [Code
README
FasterCache: Training-Free Video Diffusion Model Acceleration with High Quality
Zhengyao Lv1 |
Chenyang Si2‡ |
Junhao Song3 |
Zhenyu Yang3 |
Yu Qiao3 |
Ziwei Liu2† |
Kwan-Yee K. Wong1†
1The University of Hong Kong
2S-Lab, Nanyang Technological University
3Shanghai Artificial Intelligence Laboratory
(‡: Project lead; †: Corresponding authors)
## 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://vchitect.github.io/FasterCache/).
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](https://github.com/hpcaitech/Open-Sora), [Open-Sora-Plan 1.1](https://github.com/PKU-YuanGroup/Open-Sora-Plan), [Latte](https://github.com/Vchitect/Latte), [CogvideoX-2B&5B](https://github.com/THUDM/CogVideo), [Vchitect 2.0](https://github.com/Vchitect/Vchitect-2.0) and [Mochi](https://github.com/genmoai/models). 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](https://github.com/genmoai/models) 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](https://github.com/NUS-HPC-AI-Lab/VideoSys), [Vchitect-2.0](https://github.com/Vchitect/Vchitect-2.0), [Mochi](https://github.com/genmoai/models), and [CogVideo](https://github.com/THUDM/CogVideo),.Thanks for their contributions!