hao-ai-lab-FastVideo/docs/source/getting_started/installation.md

(fastvideo-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.