Mixture-of-Attention (MoA) is a novel architecture for personalized text-to-image diffusion models. It leverages two attention pathways - a personalization branch and a non-personalization prior branch - to allocate the generation workload. MoA is designed to retain the prior knowledge of the original model while minimally interfering with the generation process through the personalization branch, which learns to embed themes into the layout and context generated by the prior branch. MoA employs a novel routing mechanism to manage the distribution of each pixel across these branches at each layer, optimizing the blending of personalized and general content creation. After training, MoA can create high-quality, personalized images that showcase the composition and interaction of multiple themes, with the same diversity as images generated by the original model. MoA enhances the model's ability to distinguish between pre-existing capabilities and newly introduced personalized interventions, providing previously unattainable decoupled theme context control.