状态混合：面向多模态生成的路由式 token 级动态
Mixture of States: Routing Token-Level Dynamics for Multimodal Generation
把跨模态融合从固定结构改为 token 级、随去噪时间步变化的可学习稀疏路由，可直接迁移到任何多模态扩散骨干（如 DiT）中替换固定的 cross-attention 融合层，在几乎不增加参数与算力的情况下提升文生图/编辑质量。
原文依据 a learnable, token-wise router that creates denoising timestep- and input-dependent interactions between modalities' hidden states
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论我们提出 MoS（Mixture of States，状态混合），一种用于多模态扩散模型的新型融合范式，通过灵活的、基于状态的交互来融合各模态。MoS 的核心是一个可学习的、token 级的路由器，它在各模态的隐状态之间建立依赖于去噪时间步和输入的交互，从而将 token 级特征与扩散轨迹精确对齐。该路由器稀疏地选择 top-k 个隐状态，并采用 epsilon-greedy 策略训练，以极少的可学习参数和可忽略的计算开销高效地选取上下文特征。我们通过文生图生成（MoS-Image）与编辑（MoS-Editing）验证了该设计，二者均取得最优结果。我们的模型仅有 3B 至 5B 参数，即可匹配甚至超越参数量最多为其 4 倍的同类模型。这些发现确立了 MoS 作为一种灵活且计算高效的多模态扩散模型扩展范式。项目主页见 https://haozheliu-st.github.io/mos-homepage/。
英文原文摘要 1013 字符
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 epsilon-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 4xlarger. These findings establish MoS as a flexible and compute-efficient paradigm for scaling multimodal diffusion models. The project homepage is available at https://haozheliu-st.github.io/mos-homepage/