Instructions to use ivanfioravanti/FasterLivePortrait-MLX-weights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ivanfioravanti/FasterLivePortrait-MLX-weights with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir FasterLivePortrait-MLX-weights ivanfioravanti/FasterLivePortrait-MLX-weights
- LivePortrait
How to use ivanfioravanti/FasterLivePortrait-MLX-weights with LivePortrait:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
FasterLivePortrait-MLX Weights
Converted MLX .npz runtime weights for
FasterLivePortrait-MLX.
These files are converted from permissively licensed source checkpoints:
- KlingTeam/LivePortrait, MIT
- warmshao/FasterLivePortrait, MIT
- jdh-algo/JoyVASA, MIT
- TencentGameMate/chinese-hubert-base, MIT
Included
- Human LivePortrait core MLX weights
- Human landmark MLX weights
- Human stitching / eye / lip retargeting MLX weights
- Animal LivePortrait v1.1 core MLX weights
- JoyVASA MLX audio-to-motion weights for the configured Chinese HuBERT path
- JoyVASA motion template runtime asset
Not Included
This repository intentionally does not include XPose. XPose is used only for animal landmark detection in FasterLivePortrait-MLX, and its upstream license is restricted to non-commercial research use.
This repository also does not include the original JoyVASA PyTorch checkpoint or the original Transformers HuBERT directory. Those are conversion inputs only.
This repository also does not include the MediaPipe Face Landmarker task model; download it from Google's MediaPipe model URL as documented in the project README.
Use
uv run python scripts/download_mlx_weights.py --repo-id ivanfioravanti/FasterLivePortrait-MLX-weights
Conversion
The weights were produced with:
uv run --group convert python scripts/export_mlx_weights.py --include-animal
JoyVASA weights were produced with:
uv run --group convert python scripts/export_mlx_weights.py --include-joyvasa
Converted tensors are derivative model weights and inherit the obligations of the original model licenses.
Model tree for ivanfioravanti/FasterLivePortrait-MLX-weights
Base model
KlingTeam/LivePortrait