Instructions to use SceneWorks/wan2.2-t2v-a14b-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use SceneWorks/wan2.2-t2v-a14b-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir wan2.2-t2v-a14b-mlx SceneWorks/wan2.2-t2v-a14b-mlx
- Wan2.2
How to use SceneWorks/wan2.2-t2v-a14b-mlx with Wan2.2:
# 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
Wan2.2-T2V-A14B β MLX (bf16)
Native MLX (Apple Silicon) conversion of Wan-AI/Wan2.2-T2V-A14B, packaged as a turnkey, self-contained snapshot for the SceneWorks app.
Wan2.2 A14B is a high/low-noise mixture-of-experts text-to-video model β two transformers switched at the noise boundary.
Contents (self-contained, bf16)
| file | what |
|---|---|
high_noise_model.safetensors |
high-noise expert DiT (~28.6 GB) |
low_noise_model.safetensors |
low-noise expert DiT (~28.6 GB) |
t5_encoder.safetensors |
UMT5-XXL text encoder (~11.4 GB) |
vae.safetensors |
Wan z16 VAE |
tokenizer.json |
UMT5 tokenizer |
config.json |
architecture config |
Quantization (Q4/Q8) is applied at load by the engine β these weights are full bf16.
Provenance
- Source:
Wan-AI/Wan2.2-T2V-A14B(Apache-2.0). - Converted with: the SceneWorks native Rust MLX converter (
mlx-gen-wan, converter idwan_t2v_14b), dtypebfloat16, dense (no baked-in quant). - Lean snapshot β only the files the MLX engine loads.
License
Apache-2.0, inherited from the upstream model. This repository redistributes a converted copy of the upstream Apache-2.0 weights, with attribution, as permitted by that license. See the source model card.
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Wan-AI/Wan2.2-T2V-A14B