🔧 Installation

FastVideo supports the following hardware platforms:

Quick Installation

Using uv (recommended)

Use uv as the default environment manager for faster and more stable installs. The commands below target NVIDIA CUDA 12; use UV_TORCH_BACKEND=cu130 on CUDA 13. Apple silicon users should follow the MPS guide.

# Create and activate a new uv environment
uv venv --python 3.12 --seed
source .venv/bin/activate

UV_TORCH_BACKEND=cu126 uv pip install fastvideo

Using Conda (alternative)

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

UV_TORCH_BACKEND=cu126 uv pip install fastvideo

From source

FastVideo pins PyTorch 2.12.0. Select its CUDA build explicitly with UV_TORCH_BACKEND=cu126 for CUDA 12 or UV_TORCH_BACKEND=cu130 for CUDA 13.

git clone https://github.com/hao-ai-lab/FastVideo.git
cd FastVideo
UV_TORCH_BACKEND=cu126 uv pip install -e .

# optional: install flash-attn
uv pip install flash-attn --no-build-isolation -v

Alternative with Conda environment (still drives installs through uv):

UV_TORCH_BACKEND=cu126 uv pip install -e .
uv pip install flash-attn --no-build-isolation -v

Requirements

Python: 3.10-3.12 is the tested and recommended range (the commands above pin 3.12)
NVIDIA GPUs: CUDA 12.6+ with compute capability 7.0+
Apple Silicon: macOS 14.0+ with M1/M2/M3/M4 chips
CPU: x86_64 architecture (for CPU-only inference)

Next Steps

Now that FastVideo is installed, head to the Quick Start to generate your first video.

!!! tip "Not sure which command to run?" - The Quick Start page has an interactive Config Generator. Select your task and hardware to get a ready-to-copy fastvideo generate command instantly. - For fine-grained control over all parameters, use the Advanced Tuning Guide.

2.2 KiB Raw Blame History