| --- |
| license: apache-2.0 |
| language: |
| - en |
| pipeline_tag: text-to-video |
| base_model: |
| - Wan-AI/Wan2.1-T2V-1.3B |
| - gdhe17/Self-Forcing |
| tags: |
| - video-generation |
| - autoregressive |
| - diffusion |
| - adversarial-gradient-forcing |
| - arxiv:2610.14553 |
| --- |
| |
| <h1 align="center" style="font-family: Georgia, 'Times New Roman', serif; font-size: 2.15em; font-weight: 500; line-height: 1.2; margin: 12px 0 8px;">Adversarial <span style="color: #658bc9;">Gradient Forcing</span></h1> |
| <p align="center" style="font-size: 1.02em; line-height: 1.5; margin: 0 0 14px; opacity: 0.85;">Robust Few-Step Autoregressive Video Generation</p> |
| <p align="center" style="margin: 8px 0 14px;"> |
| <sub>Zhejiang University · Alibaba Group · Nanjing University</sub> |
| </p> |
| <div align="center" style="display: flex; flex-wrap: wrap; justify-content: center; align-items: center; gap: 8px; margin: 12px 0 22px;"> <a href="https://github.com/wuli-art/Adversarial-Gradient-Forcing"><img src="assets/github.svg" height="44" style="display: inline-block; vertical-align: middle; margin: 4px 3px;" alt="GitHub code"></a> <a href="https://wuli-art.github.io/Adversarial-Gradient-Forcing/"><img src="assets/project.svg" height="44" style="display: inline-block; vertical-align: middle; margin: 4px 3px;" alt="Project page"></a> <a href="https://arxiv.org/abs/2610.14553"><img src="assets/arxiv.svg" height="44" style="display: inline-block; vertical-align: middle; margin: 4px 3px;" alt="arXiv: 2610.14553"></a> |
| </div> |
|
|
| **AGF** improves long-video generation by training models to withstand **adversarial perturbations in their generated history**. Trained on five-second rollouts, it supports generation up to four minutes with the same four-step sampler. |
|
|
| <p align="center"> <a href="https://wuli-art.github.io/Adversarial-Gradient-Forcing/#gallery"><img src="https://raw.githubusercontent.com/wuli-art/Adversarial-Gradient-Forcing/main/project-page/assets/showcase/agf-scenes.gif" width="960" alt="Selected AGF video excerpts: pirate ships, a robot, felt creatures, a creature by candlelight, a watercolor dance, and a yawning child."></a> |
| <br> |
| <sub>Generated with AGF · Selected video excerpts</sub> |
| </p> |
| <p align="center"><a href="https://wuli-art.github.io/Adversarial-Gradient-Forcing/#gallery">Full videos and comparisons ↗</a></p> |
|
|
| <table style="display: table; width: 100%; table-layout: fixed;"> |
| <tr> |
| <td align="center" width="33%" style="text-align: center; padding: 12px 8px;"><strong>1.3B</strong><br><sub>Wan2.1 backbone</sub></td> |
| <td align="center" width="33%" style="text-align: center; padding: 12px 8px;"><strong>4 steps</strong><br><sub>Denoising per block</sub></td> |
| <td align="center" width="33%" style="text-align: center; padding: 12px 8px;"><strong>Up to 4 min</strong><br><sub>Video generation</sub></td> |
| </tr> |
| </table> |
| |
| ## 🧩 Model details |
|
|
| AGF selects adversarial history perturbations using distribution-matching gradients from later video blocks, then trains jointly on clean and perturbed histories. A first-step prediction consistency regularizer helps preserve scene structure during the initial training stage. |
|
|
| | Property | Paper checkpoint | |
| | --- | --- | |
| | Architecture | Wan2.1 1.3B autoregressive video generator | |
| | Initialization | Self-Forcing ODE initialization | |
| | Training | 200 regularized AGF + 200 pure AGF outer iterations | |
| | Selected weights | Global step 400, `generator_ema` | |
| | Training rollouts | 5 seconds; effective batch size 64 | |
| | Training prompts | Filtered, LLM-extended VidProM prompts used by Self-Forcing | |
| | Inference | 832 × 480, 16 fps, four denoising steps per block | |
|
|
| Iterations count outer training loops; the full schedule contains **80 generator updates**. |
|
|
| ## 🚀 Download and inference |
|
|
| The paper checkpoint is available as [`model.pt`](model.pt) (17.03 GB). Download it with the Hugging Face CLI: |
|
|
| ```bash |
| hf download Wuli-art/Adversarial-Gradient-Forcing model.pt \ |
| --local-dir checkpoints/AGF |
| ``` |
|
|
| Follow the [installation and inference guide](https://github.com/wuli-art/Adversarial-Gradient-Forcing/blob/main/docs/inference.md) for the Python/CUDA environment and Wan 1.3B assets. Place the downloaded checkpoint at `checkpoints/AGF/model.pt` and run from the code repository root: |
|
|
| ```bash |
| AGF_RUN_ID=preview bash scripts/infer.sh \ |
| --checkpoint checkpoints/AGF/model.pt \ |
| --prompts prompts/examples.txt --latent-frames 21 --gpus 0 |
| ``` |
|
|
| The script selects **`generator_ema` by default**, matching the paper. Use `--latent-frames 21`, `240`, or `960` for approximately **5 seconds**, **1 minute**, or **4 minutes**. The default policy uses 12 latent frames of local attention, including 3 persistent sink frames, with relative sink positions enabled. See the [training guide](https://github.com/wuli-art/Adversarial-Gradient-Forcing/blob/main/docs/training.md) for the full training and checkpoint-resumption workflow. |
| |
| ## 📊 Results |
| |
| Paper results under the shared evaluation protocol (higher is better). The 5-second and 60-second evaluations use 944 and 240 VBench prompts, respectively; the 240-second evaluation uses 120 MovieGenVideoBench extended prompts. |
| |
| | Model | 5 s · VBench Total | 60 s · VBench Total | 240 s · Quality 5 | |
| | --- | ---: | ---: | ---: | |
| | **AGF** | **82.03** | **81.47** | **84.82** | |
| |
| Quality 5 (Q5) is the arithmetic mean of subject consistency, background consistency, motion smoothness, aesthetic quality, and imaging quality. It excludes dynamic degree and is not an official VBench Total score. See the [evaluation protocol](https://github.com/wuli-art/Adversarial-Gradient-Forcing/blob/main/docs/evaluation.md) for metric definitions and reproducibility details. |
| |
| ## Intended use and limitations |
| |
| AGF is intended for text-to-video generation and research on autoregressive video models. Long sequences may exhibit visual artifacts, motion inconsistencies, or prompt drift. |
| |
| ## 📝 Citation |
| |
| ```bibtex |
| @misc{xia2026adversarial, |
| title = {Adversarial Gradient Forcing for Robust Few-Step Autoregressive Video Generation}, |
| author = {Chenxi Xia and Yixuan Xu and Shan Liu and Jiapeng Wang |
| and Maohua Li and Hanlin Tang and Kan Liu and Tao Lan |
| and Weiyi Lu and Xinggao Liu}, |
| year = {2026}, |
| eprint = {2610.14553}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.CV}, |
| url = {https://arxiv.org/abs/2610.14553} |
| } |
| ``` |
| |
| License: Apache 2.0. Third-party models, datasets, and other assets retain their respective licenses. |
| |
