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🚀 VILA-U: a Unified Foundation Model Integrating Visual Understanding and Generation

VILA-U is a unified foundation model that combines video, image, and language understanding and generation, simplifying the model structure and achieving near state - of - the - art performance.

🚀 Quick Start

This section provides a high - level introduction to VILA - U. For more detailed usage, please refer to the official links below.

✨ Features

• Unified Framework: VILA - U uses a single autoregressive next - token prediction framework for both visual understanding and generation tasks, eliminating the need for additional components like diffusion models.
• Enhanced Visual Perception: The unified vision tower aligns discrete visual tokens with textual inputs during pretraining, enhancing visual perception.
• High - Quality Image Generation: Autoregressive image generation can achieve similar quality as diffusion models with a high - quality dataset, allowing VILA - U to perform comparably to more complex models using a fully token - based autoregressive framework.

📚 Documentation

Abstract

VILA - U is a Unified foundation model that integrates Video, Image, Language understanding and generation. Traditional visual language models (VLMs) use separate modules for understanding and generating visual content, which can lead to misalignment and increased complexity. In contrast, VILA - U employs a single autoregressive next - token prediction framework for both tasks, eliminating the need for additional components like diffusion models. This approach not only simplifies the model but also achieves near state - of - the - art performance in visual language understanding and generation. The success of VILA - U is attributed to two main factors: the unified vision tower that aligns discrete visual tokens with textual inputs during pretraining, which enhances visual perception, and autoregressive image generation can achieve similar quality as diffusion models with high - quality dataset. This allows VILA - U to perform comparably to more complex models using a fully token - based autoregressive framework.

Useful links

• Paper: https://arxiv.org/abs/2409.04429
• GitHub: [https://github.com/mit - han - lab/vila - u](https://github.com/mit - han - lab/vila - u)
• Project: [https://hanlab.mit.edu/projects/vila - u](https://hanlab.mit.edu/projects/vila - u)

📄 License

This project is licensed under the MIT license.

📖 Citation

@article{wu2024vila,
  title={Vila - u: a unified foundation model integrating visual understanding and generation},
  author={Wu, Yecheng and Zhang, Zhuoyang and Chen, Junyu and Tang, Haotian and Li, Dacheng and Fang, Yunhao and Zhu, Ligeng and Xie, Enze and Yin, Hongxu and Yi, Li and others},
  journal={arXiv preprint arXiv:2409.04429},
  year={2024}
}