hao-ai-lab-FastVideo/docs/contributing/overview.md

🛠️ Contributing to FastVideo

Thank you for your interest in contributing to FastVideo. We want the process to be smooth and beginner‑friendly, whether you are adding a new pipeline, improving performance, or fixing a bug.

Quick prerequisites

• OS: Linux is the primary development target (WSL can work).
• GPU: NVIDIA GPU recommended for inference and training workflows.
• CUDA: Use a recent CUDA 12.x toolchain (see the installation guide for the current recommendation).

For a full install checklist, see docs/getting_started/installation/gpu.md.

Local development (UV + editable install)

If you previously used Conda for local setup, switch to uv for a faster and more stable development environment.

Install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh
# or
wget -qO- https://astral.sh/uv/install.sh | sh

Create and activate a uv environment (recommended):

uv venv --python 3.12 --seed
source .venv/bin/activate

Conda alternative (supported):

conda create -n fastvideo python=3.12 -y
conda activate fastvideo

Clone the repo:

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

Install FastVideo in editable mode and set up hooks:

UV_TORCH_BACKEND=cu126 uv pip install -e ".[dev]"  # use cu130 on CUDA 13

# Optional: FlashAttention (builds native kernels)
uv pip install flash-attn --no-build-isolation -v

# Linting, formatting, static typing
pre-commit install --hook-type pre-commit --hook-type commit-msg
pre-commit run --all-files

# Unit tests
pytest tests/

If you are on a Hopper GPU, installing FlashAttention 3 can improve performance (see docs/inference/optimizations.md).

Docker development (optional)

If you prefer a containerized environment, use the dev image documented in docs/contributing/developer_env/docker.md.

Testing

See the Testing Guide for how to add and run tests in FastVideo.

Cookbook configuration metadata

See Extending cookbook configuration metadata for where to declare model options, ranges and defaults, and how they reach the UI.

Attention backend development

If you are adding a new attention kernel or backend, follow Attention Backend Development.

Contributing with coding agents

For a step‑by‑step workflow on adding pipelines or components with coding agents, see docs/contributing/coding_agents.md.