TUNA：驯服统一视觉表征以构建原生统一多模态模型

摘要

统一多模态模型（UMMs）致力于在单一框架内协同实现多模态理解与生成任务。我们提出TUNA——一种原生统一多模态模型，其通过将VAE编码器与表征编码器级联构建出统一的连续视觉表征空间。这种统一表征空间支持对图像和视频进行端到端的理解与生成处理。相较于采用解耦表征的先前模型，TUNA的统一视觉空间避免了分立编码器带来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持续提升所有多模态任务性能，这凸显了表征编码器的重要性。最终在这种统一架构下，联合训练理解与生成数据可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上的大量实验表明，TUNA在图像/视频理解、图像/视频生成以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。

English

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.

TUNA：驯服统一视觉表征以构建原生统一多模态模型

TUNA: Taming Unified Visual Representations for Native Unified Multimodal Models

摘要

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