Instructions to use lBroth/Wan2.2-I2V-A14B-MLX-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use lBroth/Wan2.2-I2V-A14B-MLX-bf16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Wan2.2-I2V-A14B-MLX-bf16 lBroth/Wan2.2-I2V-A14B-MLX-bf16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
Wan2.2-I2V-A14B — MLX bf16 (relay-shedding)
Self-contained MLX bf16 conversion of Wan-AI/Wan2.2-I2V-A14B, the Quality tier in Videoboom. Both denoise experts are kept unquantized so mlx-video's relay-shedding can load one expert at a time — measured ~32.6 GB peak (fits 48 GB unified), vs the ~67.7 GB of Q4/Q8 that keep both experts resident. Pairs with the Wan2.2-Lightning 4-step I2V LoRA for a 4-step fast path.
| file | size |
|---|---|
high_noise_model.safetensors |
28.6 GB |
low_noise_model.safetensors |
28.6 GB |
t5_encoder.safetensors |
11.4 GB |
vae.safetensors |
0.5 GB |
License Apache-2.0 (inherited from Wan-AI/Wan2.2-I2V-A14B).
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Hardware compatibility
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Quantized
Model tree for lBroth/Wan2.2-I2V-A14B-MLX-bf16
Base model
Wan-AI/Wan2.2-I2V-A14B