NVIDIA GPU¶

Instructions to install FastVideo for NVIDIA CUDA GPUs.

Requirements¶

• OS: Linux or Windows WSL
• Python: 3.10-3.12
• CUDA: 12.6 or 13.0
• 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_TORCH_BACKEND=cu126 uv pip install fastvideo

Also optionally install FlashAttention:

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

With Conda environment (alternative)¶

uv works inside an active conda env too, so prefer uv pip for the actual install:

UV_TORCH_BACKEND=cu126 uv pip install fastvideo

Also optionally install FlashAttention:

uv 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¶

FastVideo requires PyTorch 2.12.0. Use UV_TORCH_BACKEND=cu126 for CUDA 12 or UV_TORCH_BACKEND=cu130 for CUDA 13.

Basic installation:

UV_TORCH_BACKEND=cu126 uv pip install -e .

Alternative with Conda environment:

UV_TORCH_BACKEND=cu126 uv pip install -e .

Optional Dependencies¶

Flash Attention¶

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

Alternative with Conda environment:

uv 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 compatible with CUDA 12.6 or newer

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.