| --- |
| license: apache-2.0 |
| datasets: |
| - FastVideo/Wan-Syn_77x448x832_600k |
| base_model: |
| - FastVideo/FastWan2.1-T2V-14B-Diffusers |
| library_name: fastvideo |
| tags: |
| - video-generation |
| - text-to-video |
| - wan |
| - int8 |
| - quantization |
| - apple-silicon |
| pipeline_tag: text-to-video |
| --- |
| |
| # FastMetal-14B-QAD |
|
|
| **3-step text-to-video, INT8 pre-quantized for Apple Silicon.** |
|
|
| FastMetal is a family of DMD2-distilled Wan video models with |
| quantization-aware-trained INT8 DiTs. We ship the DiT **already quantized**, |
| so there is no startup quantization — download, load, generate. |
|
|
| ## What's inside |
|
|
| This repo is fully self-contained for generation: |
|
|
| | Path | Contents | |
| |---|---| |
| | `mlx_dit.safetensors` / `mlx_dit.json` | INT8 (affine, group-64) DiT — student checkpoint | |
| | `ema/` | EMA-smoothed variant of the same DiT | |
| | `text_encoder/`, `vae/`, `tokenizer/`, `scheduler/` | everything needed to run standalone (fp16 UMT5 text encoder) | |
|
|
| ## Quickstart |
|
|
| Requires macOS with Apple silicon (MPS) and Python 3.11+: |
|
|
| ```bash |
| pip install torch transformers mlx safetensors av imageio imageio-ffmpeg |
| git clone https://github.com/FastVideo/FastVideo.git |
| cd FastVideo |
| |
| # student checkpoint |
| python examples/inference/basic/mlx_wan_prompt_to_video.py \ |
| --model-root ./FastMetal-14B-QAD \ |
| --mlx-checkpoint ./FastMetal-14B-QAD \ |
| --prompt "a cinematic slow pan over a mountain river at golden hour" |
| |
| # EMA-smoothed variant |
| python examples/inference/basic/mlx_wan_prompt_to_video.py \ |
| --model-root ./FastMetal-14B-QAD \ |
| --mlx-checkpoint ./FastMetal-14B-QAD/ema \ |
| --prompt "a cinematic slow pan over a mountain river at golden hour" |
| ``` |
|
|
| ## Model details |
|
|
| | | | |
| |---|---| |
| | Base model | FastWan 2.1 T2V 14B | |
| | Distillation | DMD2, 3 denoising steps | |
| | Quantization | affine INT8, group size 64, QAT-trained | |
| | Resolution | 448×832 (480p), 77 frames | |
| | Flow shift | 8.0 | |
| | DiT weights | ~15 GB (INT8) | |
|
|
| ## Training |
|
|
| DMD2 distillation of the FastWan 2.1 T2V 14B teacher onto an INT8 student |
| on NVIDIA GB200 clusters, with quantization-aware training (affine INT8, |
| group 64) so the deployed model matches the training objective. |
| Training corpus: `FastVideo/Wan-Syn_77x448x832_600k`. |
|
|
| ## FastMetal family |
|
|
| | Model | Tier | |
| |---|---| |
| | [FastMetal-1.3B-QAD](https://huggingface.co/FastVideo/FastMetal-1.3B-QAD) | Entry — 16 GB+ class Macs | |
| | **FastMetal-5B-QAD** | Mid — 720p | 16 GB+ class Macs | |
| | [FastMetal-14B-QAD](https://huggingface.co/FastVideo/FastMetal-14B-QAD) | Quality — 24 GB+/ Ideally 36 Macs | |
|
|
