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README.md

@@ -1,7 +1,77 @@
 ---
 license: apache-2.0
+pipeline_tag: text-to-video
+library_name: diffusers
+arxiv: 2603.00040
 ---
 
 # FastWan-QAD-1.3B-SA2
+<p align="center">
+  <img src="https://raw.githubusercontent.com/hao-ai-lab/FastVideo/main/assets/logos/logo.svg" width="200"/>
+</p>
+<div>
+  <div align="center">
+    <a href="https://github.com/hao-ai-lab/FastVideo">Github</a> |
+    <a href="https://haoailab.com/blogs/fastwan-qad/">Blog</a> |
+    <a href="https://hao-ai-lab.github.io/FastVideo">Documentation</a>
+  </div>
+</div>
 
-NVFP4 linear layers with SageAttention2++
+## 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](https://huggingface.co/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](https://huggingface.co/FastVideo/FastWan-QAD-FP8-1.3B) for RTX 4090 compatibility.
+
+---
+
+## 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](https://github.com/madebyollin/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
+
+```bash
+# 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.
+
+---
+
+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}
+}
+```