Moebius: 0.2B Lightweight Image Inpainting Framework with 10B-Level Performance

While 10B-level industrial foundation models have pushedthe boundaries of image inpainting, their prohibitive computational costsseverely hinder practical deployment. Constructing a highly optimizedtask-specific specialist offers a promising solution; however, extreme struc-tural compression inevitably triggers a severe representation bottleneck.To conquer this, we propose Moebius, a highly efficient lightweight in-painting framework. We systematically reconstruct the diffusion back-bone by introducing the Local-λ Mix Interaction (LλM I) block. Com-prising Local-λ and Interactive-λ modules, it elegantly summarizes spa-tial contexts and global semantic priors into fixed-size linear matrices,preserving complex latent interactions while drastically shedding param-eters. Furthermore, to unlock the full representational capacity of thishighly compact architecture, we synergistically pair it with an adap-tive multi-granularity distillation strategy. Operating strictly within thelatent space to avoid expensive pixel-space decoding, this strategy dy-namically balances multiple gradient-based losses to achieve high-fidelityalignment. Extensive experiments across natural and portrait bench-marks demonstrate that this optimal synergy enables Moebius to rival oreven surpass the generation quality of the 10B-level industrial generalistFLUX.1-Fill-Dev. Remarkably, Moebius achieves this using less than 2%of the parameters (0.22B vs. 11.9B) while delivering a > 15× accelerationin total inference time, setting a new efficiency standard for high-fidelityinpainting. Project page at https://hustvl.github.io/Moebius.