Instructions to use AbstractFramework/wan2.2-t2v-a14b-diffusers-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use AbstractFramework/wan2.2-t2v-a14b-diffusers-bf16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir wan2.2-t2v-a14b-diffusers-bf16 AbstractFramework/wan2.2-t2v-a14b-diffusers-bf16
- Wan2.2
How to use AbstractFramework/wan2.2-t2v-a14b-diffusers-bf16 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-diffusers-bf16
This repository contains BF16 MLX-Gen saved weights for
Wan-AI/Wan2.2-T2V-A14B-Diffusers.
It is designed for local Apple Silicon inference with
mlx-gen.
It uses the mflux/MLX saved-weight layout. It is not a Diffusers or Transformers
from_pretrained() checkpoint.
Source Model
Original model: Wan-AI/Wan2.2-T2V-A14B-Diffusers.
This prepared derivative follows the Apache 2.0 license of the source model.
Precision
This package stores the Wan A14B T2V transformer and VAE weights for MLX-Gen BF16 runtime use. The UMT5 text encoder, scheduler metadata, tokenizer files, and model index are included in the prepared folder.
Validation
Measured on 2026-06-04 with 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.
Validation profile: 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 |
Physical peak is Darwin 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.
Usage
python -m pip install -U mlx-gen
mlxgen download --model AbstractFramework/wan2.2-t2v-a14b-diffusers-bf16
mlxgen generate \
--model AbstractFramework/wan2.2-t2v-a14b-diffusers-bf16 \
--task text-to-video \
--prompt "A cinematic scene of a scientist working on agentic AI through the night, monitors glowing, papers shifting in a slow dolly shot." \
--width 384 \
--height 224 \
--frames 33 \
--steps 12 \
--guidance 4 \
--guidance-2 3 \
--fps 8 \
--seed 4242 \
--low-ram \
--metadata \
--output video.mp4
Compatibility
Requires mlx-gen >= 0.18.9.
Generated with mlx-gen 0.18.9.
Use the mlxgen command and Python import path for new MLX-Gen projects.
Attribution
MLX-Gen is based on mflux by Filip Strand and the original mflux contributors.
Prepared and contributed by @lpalbou.
Quantized
Model tree for AbstractFramework/wan2.2-t2v-a14b-diffusers-bf16
Base model
Wan-AI/Wan2.2-T2V-A14B-Diffusers
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir wan2.2-t2v-a14b-diffusers-bf16 AbstractFramework/wan2.2-t2v-a14b-diffusers-bf16