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Wan-AI/Wan2.2-T2V-A14B-Diffusers.
It is designed for local Apple Silicon inference with
mlx-gen.from_pretrained() checkpoint.Wan-AI/Wan2.2-T2V-A14B-Diffusers.mlx-gen 0.18.9 on Apple Silicon. The upstream Diffusers source snapshot measured about 118 GiB in the local Hugging Face cache before preparing these packages. The table below reports prepared-package generation from model init through MP4 save and post-save video-health validation.384x224, 33 frames, 12 denoising steps, guidance 4, guidance-2 3, 8 fps, seed 4242, --low-ram.| Package | Disk | Full-Process Physical Peak | Max RSS | MLX Peak | Total Time | Video Health |
|---|---|---|---|---|---|---|
| This BF16 package | 64.3 GiB | 33.0 GiB | 31.8 GiB | 27.7 GiB | 152.7 s | 33/33 frames, 384x224, 8 fps, temporal delta 1.3 |
| Mixed q8/BF16 package | 39.7 GiB | 20.7 GiB | 19.5 GiB | 15.5 GiB | 154.8 s | 33/33 frames, 384x224, 8 fps, temporal delta 1.4 |
ri_phys_footprint sampled for the full process. The validation is intentionally small and repeatable; it is not a claim that every full-size 1280x720, 81-frame, 40-step job has the same memory or timing profile.1python -m pip install -U mlx-gen
2
3mlxgen download --model AbstractFramework/wan2.2-t2v-a14b-diffusers-bf16
4
5mlxgen generate \
6 --model AbstractFramework/wan2.2-t2v-a14b-diffusers-bf16 \
7 --task text-to-video \
8 --prompt "A cinematic scene of a scientist working on agentic AI through the night, monitors glowing, papers shifting in a slow dolly shot." \
9 --width 384 \
10 --height 224 \
11 --frames 33 \
12 --steps 12 \
13 --guidance 4 \
14 --guidance-2 3 \
15 --fps 8 \
16 --seed 4242 \
17 --low-ram \
18 --metadata \
19 --output video.mp4mlx-gen >= 0.18.9.mlx-gen 0.18.9.mlxgen command and Python import path for new MLX-Gen projects.