Moebius: 0.2B Lightweight Image Inpainting Framework with 10B-Level Performance (*) Equal Contribution, ( † ) Project Leader, ( 📧 ) Corresponding Author.  1 Huazhong University of Science and Technology   2 VIVO AI Lab   Abstract While 10B-level industrial foundation models have pushed the boundaries of image inpainting, their prohibitive computational costs severely hinder practical deployment. Constructing a highly optimized task-specific specialist offers a promising solution; however, extreme structural compression inevitably triggers a severe representation bottleneck. To conquer this, we propose Moebius, a highly efficient lightweight inpainting framework. We systematically reconstruct the diffusion backbone by introducing the Local-λ Mix Interaction (LλMI) block. Comprising Local-λ and Interactive-λ modules, it elegantly summarizes spatial contexts and global semantic priors into fixed-size linear matrices, preserving complex latent interactions while drastically shedding parameters.…