README.md

---
license: apache-2.0
pipeline_tag: text-to-video
tags:
- video-generation
- sparse-attention
- autoregressive
---

# Light Forcing: Accelerating Autoregressive Video Diffusion via Sparse Attention

This repository contains the model checkpoints for **Light Forcing**, the first sparse attention solution specifically tailored for autoregressive (AR) video generation models. 

[**Paper**](https://huggingface.co/papers/2602.04789) | [**GitHub Code**](https://github.com/chengtao-lv/LightForcing)

## Introduction
Advanced autoregressive video generation models often suffer from the quadratic complexity of attention. Light Forcing addresses this bottleneck with two key innovations:
1. **Chunk-Aware Growth:** A mechanism to quantitatively estimate the contribution of each chunk, determining their sparsity allocation.
2. **Hierarchical Sparse Attention:** A strategy to capture historical and local context in a coarse-to-fine manner.

The method achieves a **1.2x–1.3x** end-to-end speedup while maintaining high visual quality. When combined with FP8 quantization and LightVAE, it can achieve up to a **3.0x** speedup on hardware like the RTX 5090.

## Usage

For the full environment setup, the authors recommend using the provided Docker image:
```bash
docker pull lvchengtao/light_forcing:v1
```

### Fast Inference

After setting up the environment and downloading the necessary checkpoints (e.g., Wan2.1-T2V-1.3B), you can run inference using the scripts provided in the repository.

**For short-video generation (e.g., 5s):**
```bash
python inference.py \
  --config_path configs/light_forcing_short.yaml \
  --output_folder videos/light_forcing_short \
  --checkpoint_path path/to/short_video_gen.pt \
  --data_path prompts/MovieGenVideoBench_extended.txt \
  --use_ema
```

**For long-video generation (e.g., 15s):**
```bash
python inference.py \
  --config_path configs/light_forcing_long.yaml \
  --output_folder videos/light_forcing_long \
  --checkpoint_path path/to/long_video_gen.pt \
  --data_path prompts/MovieGenVideoBench_extended.txt \
  --use_ema \
  --num_output_frames 63
```

## Citation
If you find this work or the code useful, please cite:
```bibtex
@article{lv2026light,
  title={Light Forcing: Accelerating Autoregressive Video Diffusion via Sparse Attention},
  author={Lv, Chengtao and Shi, Yumeng and Huang, Yushi and Gong, Ruihao and Ren, Shen and Wang, Wenya},
  journal={arXiv preprint arXiv:2602.04789},
  year={2026}
}
```