Instructions to use FastVideo/FastVideo-FastH3-4-step-Preview-v1-Dense-DataFree-MLX-INT8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use FastVideo/FastVideo-FastH3-4-step-Preview-v1-Dense-DataFree-MLX-INT8 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir FastVideo-FastH3-4-step-Preview-v1-Dense-DataFree-MLX-INT8 FastVideo/FastVideo-FastH3-4-step-Preview-v1-Dense-DataFree-MLX-INT8
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
license: other
license_name: minimax-h3-community
license_link: >-
https://huggingface.co/FastVideo/FastVideo-FastH3-4-step-Preview-v1-Dense-DataFree/blob/main/LICENSE
base_model: FastVideo/FastVideo-FastH3-4-step-Preview-v1-Dense-DataFree
library_name: fastvideo
pipeline_tag: text-to-video
tags:
- mlx
- apple-silicon
- minimax-h3
- fasth3
- text-to-video
- audio
- quantized
- int8
FastH3 Preview v1 Dense DataFree MLX INT8
This repository contains the ready-to-run INT8 MLX DiT for FastVideo/FastVideo-FastH3-4-step-Preview-v1-Dense-DataFree. It avoids the local conversion step and its temporary unified-memory requirement on Apple silicon.
The checkpoint is the dense-attention, step-1000, four-forward Preview v1 model.
Quantization is affine, weight-only INT8 with group size 64. Activations
remain BF16. This export is dense-only and does not support --vsa.
Files
| File | Purpose |
|---|---|
mlx_h3_dit.safetensors |
Quantized H3 DiT weights, 20.07 GiB |
mlx_h3_dit.json |
Architecture, quantization, and cached AdaLN schedule |
conversion_manifest.json |
Source revision, converter version, validation, and checksums |
Weights SHA-256: 9821dd3386f13b5a4dc11ba12f37bf51aada227a319471c409066e79f7d2f59c
Download
Download the shared H3 components once. They include the tokenizer, Qwen3-VL text encoder, video VAE, and audio VAE:
hf download FastVideo/FastVideo-FastH3-4-step-Preview-v1-Dense-DataFree \
--revision f624f08c6c279ab43534c003e556fc5b295b6558 \
--local-dir ./FastH3-Preview-v1-Dense-DataFree
Then download this ready-to-run MLX DiT:
hf download FastVideo/FastVideo-FastH3-4-step-Preview-v1-Dense-DataFree-MLX-INT8 \
--local-dir ./FastH3-MLX-INT8
Run on Apple silicon
From a current FastVideo checkout:
uv venv --python 3.12 --seed
source .venv/bin/activate
uv pip install -e ".[mlx]"
python examples/inference/basic/mlx_fasth3.py \
--model-root ./FastH3-Preview-v1-Dense-DataFree \
--mlx-checkpoint ./FastH3-MLX-INT8 \
--prompt '(S1) A presenter says <d>[English] Fast H3 runs on Apple silicon.</d>' \
--height 480 --width 832 --num-frames 124 --steps 4 --seed 2026 \
--output-path ./outputs/fasth3_int8.mp4
The MLX runtime loads one heavyweight component at a time. The source model's MiniMax H3 Community License applies to this converted checkpoint.
Validation and provenance
The conversion used FastVideo commit
cf6a00b9be4675602126d6aeab902ad9a74810ea with MLX
0.32.2 on an Apple M4 Max. FastVideo
verified all 13 source transformer shards, opened the safetensors header,
validated 1,464 tensors and the quantization manifest, loaded the checkpoint,
and completed a 124-frame 832x480 generation with the full H3 VAE.