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NVIDIA GPU

Instructions to install FastVideo for NVIDIA CUDA GPUs.

Requirements

  • OS: Linux or Windows WSL
  • Python: 3.10-3.12
  • CUDA 12.8
  • At least 1 NVIDIA GPU

Set up using Python

Create a new Python environment

uv

Recommended default: use uv for faster and more stable environment setup.

Please follow the documentation to install uv. After installing uv, create a new environment using:

# (Recommended) Create a new uv environment. Use `--seed` to install `pip` and `setuptools`.
uv venv --python 3.12 --seed
source .venv/bin/activate

Conda (alternative)

You can also create a Python environment using Conda.

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
# Create and activate a Conda environment
conda create -n fastvideo python=3.12 -y
conda activate fastvideo

Installation

With uv (recommended)

uv pip install fastvideo

Also optionally install FlashAttention:

uv pip install flash-attn --no-build-isolation -v

With Conda environment (alternative)

pip install fastvideo

Also optionally install FlashAttention:

pip install flash-attn --no-build-isolation -v

Installation from Source

1. Clone the FastVideo repository

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

2. Install FastVideo

Basic installation:

uv pip install -e .

Alternative with Conda environment:

pip install -e .

Optional Dependencies

Flash Attention

uv pip install flash-attn --no-build-isolation -v

Alternative with Conda environment:

pip install flash-attn --no-build-isolation -v

Set up using Docker

We also have prebuilt docker images with FastVideo dependencies pre-installed: Docker Images

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 12.8 support

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.