FastWan-QAD-1.3B-SA2

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Introduction

FastWan-QAD-1.3B-SA2 is a variant of FastWan-QAD-1.3B that swaps the SageAttention3 FP4 backend for SageAttention2++, trading a small amount of speed for improved visual quality. It generates a 5-second 480p video in approximately 2 seconds on an RTX 5090.

Like all FastWan-QAD models, it is built on Wan-AI/Wan2.1-T2V-1.3B-Diffusers and trained with quantization-aware distillation (QAD) for 3-step inference with NVFP4 linear layers.

Hardware requirement: RTX 5090 (sm100+). NVFP4 linear layers require Blackwell-native support. See FastWan-QAD-FP8-1.3B for RTX 4090 compatibility.

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Model Overview

• 3-step inference via quantization-aware distillation
• NVFP4 linear layers for Blackwell GPU throughput
• SageAttention2++ 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

# Install Tiny Autoencoder
git clone https://github.com/madebyollin/taehv.git
uv pip install -e taehv/

git clone https://github.com/hao-ai-lab/FastVideo.git
cd FastVideo
uv pip install -e .
cd examples/inference/optimizations
python nvfp4_sa2_wan_2_1_3b.py --taehv-checkpoint /path/to/taehv/taew2_1.pth

Training

More details coming soon.

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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}
}