FastWan-QAD-1.3B
Introduction
FastWan-QAD-1.3B is the fastest variant of the FastWan-QAD series, targeting RTX 5090 users. It uses NVFP4 quantized linear layers paired with the SageAttention3 FP4 attention backend, achieving end-to-end generation of a 5-second 480p video in 1.78 seconds — over 3.4× faster than prior distilled models on the same hardware.
The model is built on Wan-AI/Wan2.1-T2V-1.3B-Diffusers and trained with quantization-aware distillation (QAD), jointly optimizing for low-bit precision and 3-step inference quality.
Hardware requirement: RTX 5090 (sm100+). NVFP4 is a Blackwell-native format and is not supported on older GPUs. See FastWan-QAD-1.3B-SA2 for an alternative using SageAttention2++ or FastWan-QAD-FP8-1.3B for RTX 4090 support.
Model Overview
- 3-step inference via quantization-aware distillation
- NVFP4 linear layers for maximum throughput on Blackwell GPUs
- SageAttention3 FP4 backend for attention computation
- Trained at 480p (832×480) resolution, 81 frames (5 seconds at 16 fps)
- No classifier-free guidance at inference time
- Fast decoding via TAEHV tiny autoencoder
Performance
| Model | Hardware | Generation Time (5s 480p) |
|---|---|---|
| FastWan-QAD-1.3B | RTX 5090 | 1.78s |
| FastWan-QAD-1.3B-SA2 | RTX 5090 | ~2.0s |
| FastWan-QAD-FP8-1.3B | RTX 4090 | ~3.4s |
| TurboDiffusion | RTX 5090 | 6.10s |
| LightX2V | RTX 5090 | 6.91s |
Inference
docker run --gpus all --ipc=host --rm -it ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:py3.12-sha-f889e6b bash
# should drop you in /FastVideo with venv already activated
git fetch && git checkout main
# build fastvideo-kernel
cd fastvideo-kernels/ && ./build.sh && cd ..
git clone https://github.com/madebyollin/taehv
uv pip install ./taehv
# run generation:
FASTVIDEO_DISABLE_ATTENTION_COMPILE=0 FASTVIDEO_ATTENTION_BACKEND=ATTN_QAT_INFER python examples/inference/optimizations/FastWan_QAD_TAEHV.py --model FastVideo/FastWan-QAD-1.3B --distilled_model "" --taehv_checkpoint taehv/taew2_1.pth
Training
More details coming soon.
It would be greatly appreciated if you cite our paper:
@article{Zhang2026AttnQAT,
title={Attn-QAT: 4-Bit Attention With Quantization-Aware Training},
author={Zhang, Peiyuan and Noto, Matthew and Tan, Wenxuan and Jiang, Chengquan and Lin, Will and Zhou, Wei and Zhang, Hao},
journal={arXiv preprint arXiv:2603.00040},
year={2026}
}