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Install FastVideo with MLX

Install FastVideo on a Mac, then generate from the cookbook. Local video uses the native MLX runtime, not CUDA, and not the old PyTorch MPS demo at examples/inference/basic/basic_mps.py.

Requirements

  • macOS 14 or newer
  • Python 3.12
  • ffmpeg (brew install ffmpeg)

Install

Cookbook commands run from a clone.

git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
uv venv --python 3.12 --seed
source .venv/bin/activate
brew install ffmpeg
uv pip install -e ".[mlx]"

Conda is optional. After you activate a Conda env, still install with uv pip as above.

uv pip install "fastvideo[mlx]" from PyPI installs the extra only. It does not ship the example scripts the cookbook copies.

Generate a video

Open the cookbook. Select Apple Silicon as the runtime. Each recipe has a Python command. FastH3 also has a server path for the playground and the OpenAI Python client.

FastH3 is two distilled MiniMax-H3 checkpoints. V1 is the four-step launch. Some Hub repo names still say Preview. That name is historical. V1 is a full model, not a demo. V2 is the eight-step checkpoint. More forwards is why V2 is the higher-quality FastH3.

Recorded shapes and evidence live in the support matrix.

Hardware

  • FastMetal 1.3B and 5B: 16 GB unified memory and up
  • FastMetal 14B: 36 GB unified memory and up
  • FastH3 V1 and V2: validated on an M4 Max with 36 GB unified memory

Troubleshooting

  • basic_mps.py is the wrong path. That script is PyTorch MPS. Use an Apple Silicon recipe in the cookbook.
  • Muxing fails. Install ffmpeg with Homebrew.
  • A cookbook command cannot find a script. Run it from the FastVideo clone after uv pip install -e ".[mlx]".

If that does not match what you see, open an issue on the GitHub repository or ask in the Slack community.