---
license: apache-2.0
datasets:
- FastVideo/Wan2.2-Syn-121x704x1280_32k
base_model:
- Wan-AI/Wan2.2-TI2V-5B-Diffusers
library_name: fastvideo
tags:
- video-generation
- text-to-video
- wan
- int8
- quantization
- apple-silicon
pipeline_tag: text-to-video
---

# FastMetal-5B-QAD

**3-step text-to-video, INT8 pre-quantized for Apple Silicon.**

The mid-tier FastMetal model — a DMD2-distilled Wan2.2 TI2V 5B with a
quantization-aware-trained INT8 DiT. 720p-native, pre-quantized: no
startup quantization.

## What's inside

| Path | Contents |
|---|---|
| `mlx_dit.safetensors` / `mlx_dit.json` | INT8 (affine, group-64) DiT |
| `text_encoder/`, `vae/`, `tokenizer/`, `scheduler/` | everything needed to run standalone |

## 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

python examples/inference/basic/mlx_wan22_generate.py \
  --text-encoder-root ./FastMetal-5B-QAD \
  --mlx-checkpoint ./FastMetal-5B-QAD \
  --vae-root ./FastMetal-5B-QAD/vae \
  --prompt "a river winding through a fantasy valley at golden hour" \
  --fast
```

## Model details

| | |
|---|---|
| Base model | Wan 2.2 TI2V 5B |
| Distillation | DMD2, 3 denoising steps |
| Quantization | affine INT8, group size 64, QAT-trained |
| Resolution | 704×1280 (720p), 121 frames |
| Flow shift | 5.0 |
| DiT weights | ~5 GB (INT8) |

## Training

DMD2 distillation of the Wan 2.2 TI2V 5B teacher onto an INT8 student on
NVIDIA GB200 clusters, with quantization-aware training (affine INT8,
group 64). Training corpus: `FastVideo/Wan2.2-Syn-121x704x1280_32k`.

## 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 |