NVIDIA GPU

NVIDIA GPU#

Instructions to install FastVideo for NVIDIA CUDA GPUs.

Requirements#

  • OS: Linux or Windows WSL

  • Python: 3.10-3.12

  • CUDA 12.4

  • At least 1 NVIDIA GPU

Set up using Python#

Create a new Python environment#

Conda#

You can create a new 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#
# (Recommended) Create a new conda environment.
conda create -n fastvideo python=3.12 -y
conda activate fastvideo

Note

PyTorch has deprecated the conda release channel. If you use conda, please only use it to create Python environment rather than installing packages.

uv#

Tip

We highly recommend using uv to install FastVideo. In our experience, uv speeds up installation by at least 3x.

Or you can create a new Python environment using uv, a very fast Python environment manager. Please follow the documentation to install uv. After installing uv, you can create a new Python environment using the following command:

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

Installation#

pip install fastvideo

# or if you are using uv
uv pip install fastvideo

Also optionally install flash-attn:

pip install flash-attn==2.7.4.post1 --no-build-isolation

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:

pip install -e .

# or if you are using uv
uv pip install -e .

Optional Dependencies#

Flash Attention#

pip install flash-attn==2.7.4.post1 --no-build-isolation

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 12.4 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.

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