l structures and inefficient use of computational
resources. To address this, we propose a Unified image-to-image Generation
(UniGen) framework that supports diverse conditional inputs while enhancing
generation efficiency and expressiveness. Specifically, to tackle the widely
existing parameter redundancy and computational inefficiency in controllable
conditional generation architectures, we propose the Condition Modulated Expert
(CoMoE) module. This module aggregates semantically similar patch features an