Unified multimodal models (UMMs) have emerged as a powerful paradigm in foundational computer vision research, demonstrating significant potential in both image understanding and generation. However, existing research in the face domain primarily focuses on $\textbf{coarse}$ facial attribute understanding, with limited capacity to handle $\textbf{fine-grained}$ facial attributes and without addressing generation capabilities. To overcome these limitations, we propose Uni$\textbf{F}^2$ace, the first UMM tailored specifically for fine-grained face understanding and generation. In general, we train Uni$\textbf{F}^2$ace on a self-constructed, specialized dataset utilizing two mutually beneficial diffusion techniques and a two-level mixture-of-experts architecture. Specifically, we first build a large-scale facial dataset, Uni$\textbf{F}^2$ace-130K, which contains 130K image-text pairs with one million question-answering pairs that span a wide range of facial attributes. Second, we establish a theoretical connection between discrete diffusion score matching and masked generative models, optimizing both evidence lower bounds simultaneously, which significantly improves the model's ability to synthesize facial details. Finally, we introduce both token-level and sequence-level mixture-of-experts, enabling efficient fine-grained representation learning for both understanding and generation tasks. Extensive experiments on Uni$\textbf{F}^2$ace-130K demonstrate that Uni$\textbf{F}^2$ace outperforms existing UMMs and generative models, achieving superior performance across both understanding and generation tasks.

本研究针对现有面部领域在细粒度面部属性理解和生成能力上的不足，提出了Uni$\textbf{F}^2$ace，首个专门为细粒度面部理解与生成而设计的统一多模态模型。通过构建包含130K图像-文本对和百万个问答对的大规模数据集，并结合改进的扩散技术，该模型在理解和生成任务上显著超越了现有模型，展现出卓越的细节合成能力。

Uni$\textbf{F}^2$ace：用于细粒度面部理解与生成的统一多模态模型