Instructions to use AbstractFramework/wan2.2-i2v-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-i2v-a14b-diffusers-bf16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir wan2.2-i2v-a14b-diffusers-bf16 AbstractFramework/wan2.2-i2v-a14b-diffusers-bf16
- Wan2.2
How to use AbstractFramework/wan2.2-i2v-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-i2v-a14b-diffusers-bf16
This repository contains BF16 MLX-Gen saved weights for
Wan-AI/Wan2.2-I2V-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-I2V-A14B-Diffusers.
This prepared derivative follows the Apache 2.0 license of the source model.
Precision
This package stores the Wan A14B I2V transformer and VAE weights for MLX-Gen BF16 runtime use. The UMT5 text encoder, scheduler metadata, tokenizer files, model index, and a public spacecraft example image 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: public spacecraft source image, 384x384, 33 frames, 12 denoising steps, guidance 3.5, guidance-2 3.5, 8 fps, seed 4242, --low-ram.
| Package | Disk | Full-Process Physical Peak | Max RSS | MLX Peak | Total Time | Video Health |
|---|---|---|---|---|---|---|
| This BF16 package | 64.1 GiB | 33.7 GiB | 31.8 GiB | 28.2 GiB | 228.2 s | 33/33 frames, 384x384, 8 fps, temporal delta 10.4 |
| Mixed q8/BF16 package | 39.7 GiB | 21.5 GiB | 19.6 GiB | 15.9 GiB | 242.2 s | 33/33 frames, 384x384, 8 fps, temporal delta 10.5 |
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
The included public sample image is available at examples/i2v_takeoff_source.png when this repository is cloned locally. For best I2V stability, use an input image whose aspect ratio matches the requested video dimensions and keep the subject inside the frame.
python -m pip install -U mlx-gen
mlxgen download --model AbstractFramework/wan2.2-i2v-a14b-diffusers-bf16
mlxgen generate \
--model AbstractFramework/wan2.2-i2v-a14b-diffusers-bf16 \
--task image-to-video \
--image path/to/input.png \
--prompt "Cinematic image-to-video of the spacecraft lifting off from a snowy landing field, engines glowing, exhaust plume expanding, the full craft remains centered in frame." \
--width 384 \
--height 384 \
--frames 33 \
--steps 12 \
--guidance 3.5 \
--guidance-2 3.5 \
--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-i2v-a14b-diffusers-bf16
Base model
Wan-AI/Wan2.2-I2V-A14B-Diffusers