🔧 Installation

🔧 Installation#

FastVideo currently only supports Linux and CUDA GPUs. The code is tested on Python 3.10.0 and CUDA 12.4, primarily with NVIDIA H100 GPUs.

Prerequisites#

  • CUDA 12.4 installed and supported

  • Linux operating system

Installation Options#

Option 1: Quick Install#

pip install fastvideo

Option 2: Installation from Source#

1. Install Miniconda (if not already installed)#

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
source ~/.bashrc

2. Create and activate a Conda environment for FastVideo#

conda create -n fastvideo python=3.10 -y
conda activate fastvideo

3. Clone the FastVideo repository#

git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo

4. Install FastVideo#

Basic installation:

pip install -e .

Optional Dependencies#

Flash Attention#

pip install flash-attn==2.7.0.post2 --no-build-isolation

Sliding Tile Attention (STA)#

To try Sliding Tile Attention (optional), please follow the instructions in csrc/sliding_tile_attention/README.md to install STA.

Development Environment Setup#

If you’re planning to contribute to FastVideo please see the following page: Contributor Guide

Hardware Requirements#

For Basic Inference#

  • NVIDIA GPU with CUDA support

  • Minimum 20GB VRAM for quantized models (e.g., single RTX 4090)

For Lora Finetuning#

  • 40GB GPU memory each for 2 GPUs with lora

  • 30GB GPU memory each for 2 GPUs with CPU offload and lora

For Full Finetuning/Distillation#

  • Multiple high-memory GPUs recommended (e.g., H100)

Troubleshooting#

If you encounter any issues during installation, please open an issue on our GitHub repository.

You can also join our Slack community for additional support.