VILA-U: A Unified Foundation Model Integrating Visual Understanding And Generation

Wu Yecheng, Zhang Zhuoyang, Chen Junyu, Tang Haotian, Li Dacheng, Fang Yunhao, Zhu Ligeng, Xie Enze, Yin Hongxu, Yi Li, Han Song, Lu Yao. Arxiv 2024

[Paper]    
GPT Merging Pretraining Methods Tools Training Techniques

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.

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