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Infinity**⭐️**: Unified SpaceTime AutoRegressive Modeling for Visual Generation

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</div> <p align="center" style="font-size: larger;"> <a href="http://arxiv.org/abs/2511.04675">Infinity⭐️: Unified Spacetime AutoRegressive Modeling for Visual Generation</a> </p> <!-- <p align="center"> <img src="assets/show_images.jpg" width=95%> <p> -->

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🔥 Updates!!

• Nov 7, 2025: 🔥 Paper, Training and Inference Codes && Checkpoints && Demo Website released!
• Sep 18, 2025: 🎉 InfinityStar is accepted as NeurIPS 2025 Oral.

🕹️ Try and Play with Infinity⭐️!

We provide a demo website for you to play with InfinityStar and generate videos. Enjoy the fun of bitwise video autoregressive modeling!

✨ Overview

We introduce InfinityStar, a unified spacetime autoregressive framework for high-resolution image and dynamic video synthesis.

• 🧠 Unified Spacetime Model: A purely discrete, autoregressive approach that jointly captures spatial and temporal dependencies within a single, elegant architecture.

• 🎬 Versatile Generation: This unified design naturally supports a variety of generation tasks such as text-to-image, text-to-video, image-to-video, and long interactive video synthesis via straightforward temporal autoregression.

• 🏆 Leading Performance & Speed: Through extensive experiments, InfinityStar scores 83.74 on VBench, outperforming all autoregressive models by large margins, even surpassing diffusion competitors like HunyuanVideo, approximately 10x faster than leading diffusion-based methods.

• 📖 Pioneering High-Resolution Autoregressive Generation: To our knowledge, InfinityStar is the first discrete autoregressive video generator capable of producing industrial-level 720p videos, setting a new standard for quality in its class.

🔥 Unified modeling for image, video generation and long interactive video synthesis 📈:

<div align="left"> <img src="assets/framework.png" alt="" style="width: 100%;" /> </div>

🎬 Video Demos

General Aesthetics

<div align="left"> <video src="https://github.com/user-attachments/assets/14e2b18b-9234-42ce-bdab-670faeef4b2a" width="100%" controls autoplay loop></video> </div>

Anime & 3D Animation

<div align="left"> <video src="https://github.com/user-attachments/assets/478e9571-b550-4c23-a567-6fee9a0afb5b" width="100%" controls autoplay loop></video> </div>

Motion

<div align="left"> <video src="https://github.com/user-attachments/assets/adab669b-d38f-4607-9a52-32d8d0bf0e53" width="100%" controls autoplay loop></video> </div>

Extended Application: Long Interactive Videos

<div align="center"> <video src="https://github.com/user-attachments/assets/411666a6-563d-4551-a3f8-dc5de00436c1" width="100%" controls autoplay loop></video> </div>

Benchmark

Achieve sota performance on image generation benchmark:

<div align="left"> <img src="assets/Infinitystar_image_gen_benchmark.png" alt="Image Generation Evaluation" style="width: 100%;" /> </div>

Achieve sota performance on video generation benchmark:

<div align="left"> <img src="assets/Infinitystar_videogen_benchmark.png" alt="" style="width: 100%;" /> </div>

Surpassing diffusion competitors like HunyuanVideo*:

<div align="left"> <img src="assets/Infinitystar_videogen_humaneval.png" alt="" style="width: 100%;" /> </div>

Visualization

Text to image examples

<div align="left"> <img src="assets/supp_show_images.png" alt="Text to Image Examples" style="width: 100%;" /> </div>

Image to video examples

<div align="left"> <img src="assets/i2v_examples.png" alt="Image to Video Examples" style="width: 100%;" /> </div>

Video extrapolation examples

<div align="left"> <img src="assets/v2v_examples.png" alt="Video Extrapolation Examples" style="width: 100%;" /> </div>

📑 Open-Source Plan

• [x] Training Code
• [x] Web Demo
• [x] InfinityStar Inference Code
• [x] InfinityStar Models Checkpoints
• [x] InfinityStar-Interact Inference Code
• [x] InfinityStar-Interact Checkpoints

Installation

1. We use FlexAttention to speedup training, which requires torch>=2.5.1.
2. Install other pip packages via pip3 install -r requirements.txt.

Training Scripts

We provide a comprehensive workflow for training and finetuning our model, covering data organization, feature extraction, and training scripts. For detailed instructions, please refer to data/README.md.

Inference

• 720p Video Generation: Use tools/infer_video_720p.py to generate 5-second videos at 720p resolution. Due to the high computational cost of training, our released 720p model is trained for 5-second video generation. This script also supports image-to-video generation by specifying an image path.

  python3 tools/infer_video_720p.py
  

• 480p Variable-Length Video Generation: We also provide an intermediate checkpoint for 480p resolution, capable of generating videos of 5 and 10 seconds. Since this model is not specifically optimized for Text-to-Video (T2V), we recommend using the experimental Image-to-Video (I2V) and Video-to-Video (V2V) modes for better results. To specify the video duration, you can edit the generation_duration variable in tools/infer_video_480p.py to either 5 or 10. This script also supports image-to-video and video continuation by providing a path to an image or a video.

  python3 tools/infer_video_480p.py
  

• 480p Long Interactive Video Generation: Use tools/infer_interact_480p.py to generate a long interactive video in 480p. This script supports interactive video generation. You can provide a reference video and multiple prompts. The model will generate a video interactively with your assistance.

  python3 tools/infer_interact_480p.py
  

Citation

If our work assists your research, feel free to give us a star ⭐ or cite us using:

@Article{VAR,
      title={Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction}, 
      author={Keyu Tian and Yi Jiang and Zehuan Yuan and Bingyue Peng and Liwei Wang},
      year={2024},
      eprint={2404.02905},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@misc{Infinity,
    title={Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis}, 
    author={Jian Han and Jinlai Liu and Yi Jiang and Bin Yan and Yuqi Zhang and Zehuan Yuan and Bingyue Peng and Xiaobing Liu},
    year={2024},
    eprint={2412.04431},
    archivePrefix={arXiv},
    primaryClass={cs.CV},
    url={https://arxiv.org/abs/2412.04431}, 
}

@misc{InfinityStar,
      title={InfinityStar: Unified Spacetime AutoRegressive Modeling for Visual Generation}, 
      author={Jinlai Liu and Jian Han and Bin Yan and Hui Wu and Fengda Zhu and Xing Wang and Yi Jiang and Bingyue Peng and Zehuan Yuan},
      year={2025},
      eprint={2511.04675},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2511.04675}, 
}

License

This project is licensed under the MIT License - see the LICENSE file for details.