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z_dim=48). It runs comfortably at 720p (1280×704), 24fps on a
64 GB Apple Silicon machine, where the heavier 14B dual-model (I2V-A14B) struggles.| File | Size | Notes |
|---|---|---|
model.safetensors | ~5 GB | Transformer (8-bit, group_size=64) |
t5_encoder.safetensors | ~11 GB | UMT5-XXL text encoder (bf16) |
vae.safetensors | ~2.6 GB | Wan2.2 VAE (z_dim=48, fp32) |
config.json | — | Includes quantization metadata |
mlx_video.models.wan_2.convert --quantize --bits 8 --group-size 64.1pip install git+https://github.com/Blaizzy/mlx-video.git
2# or: uv pip install git+https://github.com/Blaizzy/mlx-video.git
3
4huggingface-cli download <THIS_REPO_ID> --local-dir ./Wan2.2-TI2V-5B-MLX-Q8
5
6python -m mlx_video.models.wan_2.generate \
7 --model-dir ./Wan2.2-TI2V-5B-MLX-Q8 \
8 --image ./start.png \
9 --prompt "the subject waves hello, warm sunlight, film grain" \
10 --width 1280 --height 704 --num-frames 81 \
11 --steps 40 --guide-scale 5.0 \
12 --output-path out.mp44n+1. Output is 24 fps.--image is optional (TI2V also does pure text-to-video).NOTICE are included. All credit for the base model goes
to the Wan-AI / Alibaba team. MLX conversion via Prince Canuma's
mlx-video. This repository only quantizes and
re-packages the weights for MLX; the model architecture and training are unchanged.