86 lines
2.2 KiB
Markdown
86 lines
2.2 KiB
Markdown
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# 🛠️ Contributing to FastVideo
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Thank you for your interest in contributing to FastVideo. We want the process
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to be smooth and beginner‑friendly, whether you are adding a new pipeline,
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improving performance, or fixing a bug.
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## Quick prerequisites
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- **OS**: Linux is the primary development target (WSL can work).
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- **GPU**: NVIDIA GPU recommended for inference and training workflows.
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- **CUDA**: Use a recent CUDA 12.x toolchain (see the installation guide for
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the current recommendation).
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For a full install checklist, see `docs/getting_started/installation/gpu.md`.
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## Local development (UV + editable install)
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If you previously used Conda for local setup, switch to uv for a faster and more stable development environment.
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Install `uv`:
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```bash
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curl -LsSf https://astral.sh/uv/install.sh | sh
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# or
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wget -qO- https://astral.sh/uv/install.sh | sh
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```
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Create and activate a uv environment (recommended):
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```bash
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uv venv --python 3.12 --seed
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source .venv/bin/activate
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```
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Conda alternative (supported):
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```bash
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conda create -n fastvideo python=3.12 -y
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conda activate fastvideo
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```
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Clone the repo:
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```bash
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git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
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```
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Install FastVideo in editable mode and set up hooks:
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```bash
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UV_TORCH_BACKEND=cu126 uv pip install -e ".[dev]" # use cu130 on CUDA 13
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# Optional: FlashAttention (builds native kernels)
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uv pip install flash-attn --no-build-isolation -v
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# Linting, formatting, static typing
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pre-commit install --hook-type pre-commit --hook-type commit-msg
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pre-commit run --all-files
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# Unit tests
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pytest tests/
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```
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If you are on a Hopper GPU, installing FlashAttention 3 can improve
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performance (see `docs/inference/optimizations.md`).
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## Docker development (optional)
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If you prefer a containerized environment, use the dev image documented in
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`docs/contributing/developer_env/docker.md`.
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## Testing
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See the [Testing Guide](testing.md) for how to add and run tests in FastVideo.
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## Attention backend development
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If you are adding a new attention kernel or backend, follow
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[Attention Backend Development](attention_backend.md).
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## Contributing with coding agents
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For a step‑by‑step workflow on adding pipelines or components with coding
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agents, see `docs/contributing/coding_agents.md`.
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