README.md

---
license: cc-by-nc-sa-4.0
library_name: pytorch
pipeline_tag: image-to-video
tags:
  - image-to-video
  - video-generation
  - autoregressive-video-generation
  - one-step-generation
  - adversarial-distillation
  - wan
base_model:
  - Wan-AI/Wan2.1-T2V-14B
---

# AAD-1: Asymmetric Adversarial Distillation for One-Step Autoregressive Video Generation

<p align="center">
  <a href="https://github.com/HaobroLi">Haobo Li</a><sup>1,2</sup> ·
  <a href="https://zengyh1900.github.io/">Yanhong Zeng</a><sup>2,3,&#9993;</sup> ·
  <a href="https://github.com/JaydenLyh">Yunhong Lu</a><sup>4,2</sup> ·
  <a href="https://github.com/zhujiapeng">Jiapeng Zhu</a><sup>2</sup> ·
  <a href="https://ken-ouyang.github.io/">Hao Ouyang</a><sup>2</sup> ·
  <a href="https://github.com/qiuyu96">Qiuyu Wang</a><sup>2</sup> ·
  <a href="https://felixcheng97.github.io/">Ka Leong Cheng</a><sup>2</sup> ·
  <a href="https://shenyujun.github.io/">Yujun Shen</a><sup>2</sup> ·
  <a href="https://zhipengzhang.cn/">Zhipeng Zhang</a><sup>1,5,&#9993;</sup>
</p>

<p align="center">
  <sup>1</sup>AutoLab, SAI, SJTU
  <sup>2</sup>Ant Group
  <sup>3</sup>Department of Automation, Tsinghua University
  <sup>4</sup>Zhejiang University
  <sup>5</sup>Anyverse Dynamics
</p>

<h2 align="center">
  <a href="https://arxiv.org/abs/2606.03972">📄 Paper</a> |
  <a href="https://aad-1.github.io/">🌐 Website</a> |
  <a href="https://huggingface.co/Watay/AAD-1">🤗 Models</a>
</h2>

We present **AAD-1**, an Asymmetric Adversarial Distillation framework for one-step autoregressive video world model generation. AAD-1 addresses motion collapse and training instability by combining an asymmetric generator-discriminator design with phased training: the generator remains causal for autoregressive sampling, while a bidirectional video-level discriminator scores full spatiotemporal sequences to detect global temporal failures and long-range drift. A distribution-matching warmup first bootstraps a stable one-step generator before adversarial distillation, enabling state-of-the-art one-step autoregressive video generation on VBench.

![AAD-1 training pipeline](assets/training_pipeline.png)

AAD-1 trains a one-step autoregressive generator in three stages. Stage I adapts a pretrained bidirectional video model into a causal generator with ODE initialization. Stage II performs one-step DMD warmup under self-rollout training. Stage III applies asymmetric adversarial refinement: the generator remains causal, while a bidirectional video-level discriminator observes full-video context to penalize temporal drift and motion collapse.

## Progress

- [x] 📝 Technical Report / Paper
- [x] 🌐 Project Homepage
- [x] 💻 Inference Code
- [x] 🤗 Pretrained Checkpoints

## Setup

Clone the repository:

```bash
git clone https://github.com/AutoLab-SAI-SJTU/AAD-1.git
cd AAD-1
```

Install with `uv`:

```bash
uv venv --python 3.10
source .venv/bin/activate
uv pip install -r requirements.txt
uv pip install flash-attn --no-build-isolation
uv pip install -e .
```

Alternatively, use `conda`:

```bash
conda create -n AAD-1 python=3.10 -y
conda activate AAD-1
pip install -r requirements.txt
pip install flash-attn --no-build-isolation
python setup.py develop
```

## Checkpoints

The public release path only needs:

1. 🤗 [Official shared Wan model: Wan2.1-T2V-14B](https://huggingface.co/Wan-AI/Wan2.1-T2V-14B)
2. 🤗 [Released AAD-1 sharded generator checkpoint](https://huggingface.co/Watay/AAD-1)

Download the shared Wan components:

```bash
huggingface-cli download \
  Wan-AI/Wan2.1-T2V-14B \
  --local-dir-use-symlinks False \
  --local-dir wan_models/Wan2.1-T2V-14B
```

If you use a custom shared Wan path, pass it explicitly with `--wan_model_dir`.

Download the AAD-1 sharded generator checkpoint:

```bash
huggingface-cli download \
  Watay/AAD-1 \
  --include "14b_i2v_1step_transformer/*" \
  --local-dir-use-symlinks False \
  --local-dir checkpoints
```

Optional 2-step checkpoint:

```bash
huggingface-cli download \
  Watay/AAD-1 \
  --include "14b_i2v_2step_transformer/*" \
  --local-dir-use-symlinks False \
  --local-dir checkpoints
```

## Quick Start

Run from the repository root. This command generates an 81-frame video from an input image with the `1step` checkpoint on a single GPU.

```bash
python aad1/inference.py \
  --prompt "two people scuba diving in the ocean" \
  --image_path assets/examples/scuba_diving_ocean.jpg \
  --output_path outputs/aad1_scuba_1step.mp4 \
  --checkpoint_path checkpoints/14b_i2v_1step_transformer/self_forcing_generator_bf16.index.json \
  --wan_model_dir wan_models/Wan2.1-T2V-14B \
  --num_frames 81 \
  --seed 1000 \
  --denoising_timestep_list 1000
```

Example `2step` command:

```bash
python aad1/inference.py \
  --prompt "two people scuba diving in the ocean" \
  --image_path assets/examples/scuba_diving_ocean.jpg \
  --output_path outputs/aad1_scuba_2step.mp4 \
  --checkpoint_path checkpoints/14b_i2v_2step_transformer/self_forcing_generator_bf16.index.json \
  --wan_model_dir wan_models/Wan2.1-T2V-14B \
  --num_frames 81 \
  --seed 1000 \
  --denoising_timestep_list 1000,500
```

More examples, including 20s generation, are in [docs/inference-examples.md](docs/inference-examples.md).

## Acknowledgements

We thank the authors and contributors of [Wan2.1](https://github.com/Wan-Video/Wan2.1), [CausVid](https://github.com/tianweiy/CausVid), [Self Forcing](https://github.com/guandeh17/Self-Forcing), and [FastVideo](https://github.com/hao-ai-lab/FastVideo) for their open research and codebases. AAD-1 builds on these foundations for causal video generation, distillation, and efficient inference.