Mixture of States: Routing Token-Level Dynamics for Multimodal Generation
Authors: name: Haozhe Liu, name: Ding Liu, name: Mingchen Zhuge, name: Zijian Zhou, name: Tian Xie, name: Sen He, name: Yukang Yang, name: Shuming Liu, name: Yuren Cong, name: Jiadong Guo, name: Hongyu Xu, name: Ke Xu, name: Kam-Woh Ng, name: Juan C. Pérez, name: Juan-Manuel~Pérez-Rúa, name: Tao Xiang, name: Wei Liu, name: Shikun Liu, name: Jürgen Schmidhuber
Organization: AI at Meta

We introduce MoS (Mixture of States), a novel fusion paradigm for multimodal diffusion models that merges modalities using flexible, state-based interactions. The core of MoS is a learnable, token-wise router that creates denoising timestep- and input-dependent interactions between modalities' hidden states, precisely aligning token-level features with the diffusion trajectory. This router sparsely selects the top-k hidden states and is trained with an ε-greedy strategy, efficiently selecting contextual features with minimal learnable parameters and negligible computational overhead. We validate our design with text-to-image generation (MoS-Image) and editing (MoS-Editing), which achieve state-of-the-art results. With only 3B to 5B parameters, our models match or surpass counterparts up to 4times larger. These findings establish MoS as a flexible and compute-efficient paradigm for scaling multimodal diffusion models.