Moebius: 0.2B Lightweight Image Inpainting Framework with 10B-Level Performance
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 m...
| Authors | Kangsheng Duan, Ziyang Xu, Wenyu Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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