---
pipeline_tag: text-to-video
license: apache-2.0
language:
- en
base_model:
- Wan-AI/Wan2.1-T2V-1.3B
---

# ShotStream: Streaming Multi-Shot Video Generation for Interactive Storytelling

**ShotStream** is a novel causal multi-shot architecture that enables interactive storytelling and efficient on-the-fly frame generation. It achieves sub-second latency and 16 FPS on a single NVIDIA GPU by reformulating the task as next-shot generation conditioned on historical context.

[**Project Page**](https://luo0207.github.io/ShotStream/) | [**Paper**](https://arxiv.org/abs/2603.25746) | [**Code**](https://github.com/KlingAIResearch/ShotStream)

## Introduction
Multi-shot video generation is crucial for long narrative storytelling. ShotStream allows users to dynamically instruct ongoing narratives via streaming prompts. It preserves visual coherence through a dual-cache memory mechanism and mitigates error accumulation using a two-stage self-forcing distillation strategy (Distribution Matching Distillation).

## Usage

**Training and inference code, as well as the models, are all released.** For the full implementation and **training details**, please refer to the [official GitHub repository](https://github.com/KlingAIResearch/ShotStream).

### 1. Environment Setup

```bash
git clone https://github.com/KlingAIResearch/ShotStream.git
cd ShotStream
# Setup environment using the provided script
bash tools/setup/env.sh
```

### 2. Download Checkpoints

```bash
# Download the checkpoints of Wan-T2V-1.3B and ShotStream
bash tools/setup/download_ckpt.sh
```

### 3. Run Inference

To perform autoregressive 4-step long multi-shot video generation:

```bash
bash tools/inference/causal_fewsteps.sh
```

## Citation
If you find our work helpful, please cite our paper:

```bibtex
@article{luo2026shotstream,
  title={ShotStream: Streaming Multi-Shot Video Generation for Interactive Storytelling},
  author={Luo, Yawen and Shi, Xiaoyu and Zhuang, Junhao and Chen, Yutian and Liu, Quande and Wang, Xintao and Wan, Pengfei and Xue, Tianfan},
  journal={arXiv preprint arXiv:2603.25746},
  year={2026}
}
```