Unified multimodal models (UMMs) aim to jointly perform multimodal understanding and generation within a single framework. We present TUNA, a native UMM that builds a unified continuous visual representation by cascading a VAE encoder with a representation encoder. This unified representation space allows end-to-end processing of images and videos for both understanding and generation tasks. Compared to prior UMMs with decoupled representations, TUNA's unified visual space avoids representation format mismatches introduced by separate encoders, outperforming decoupled alternatives in both understanding and generation. Moreover, we observe that stronger pretrained representation encoders consistently yield better performance across all multimodal tasks, highlighting the importance of the representation encoder. Finally, in this unified setting, jointly training on both understanding and generation data allows the two tasks to benefit from each other rather than interfere. Our extensive experiments on multimodal understanding and generation benchmarks show that TUNA achieves state-of-the-art results in image and video understanding, image and video generation, and image editing, demonstrating the effectiveness and scalability of its unified representation design.

翻译：统一多模态模型旨在单一框架内联合执行多模态理解与生成任务。本文提出TUNA，一种原生统一多模态模型，通过级联变分自编码器编码器与表征编码器构建统一的连续视觉表征空间。该统一表征空间支持对图像与视频进行端到端的理解与生成任务处理。相较于采用解耦表征的先前统一多模态模型，TUNA的统一视觉空间避免了因独立编码器引入的表征格式失配问题，在理解与生成任务上均优于解耦方案。此外，我们发现更强的预训练表征编码器能在所有多模态任务中持续带来性能提升，这凸显了表征编码器的重要性。最终，在此统一框架下，联合训练理解与生成数据能使两项任务相互促进而非相互干扰。我们在多模态理解与生成基准测试上的大量实验表明，TUNA在图像/视频理解、图像/视频生成及图像编辑任务中均取得最先进成果，验证了其统一表征设计的有效性与可扩展性。