(*) Equal Contribution, (†) Project Leader, (📧) Corresponding Author.
1Huazhong University of Science and Technology 2VIVO AI Lab
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. Furthermore, to unlock the full representational capacity of this highly compact architecture, we synergistically pair it with an adaptive multi-granularity distillation strategy. Operating strictly within the latent space to avoid expensive pixel-space decoding, this strategy dynamically balances multiple gradient-based losses to achieve high-fidelity alignment. Extensive experiments across natural and portrait benchmarks demonstrate that this optimal synergy enables Moebius to rival or even surpass the generation quality of the 10B-level industrial generalist FLUX.1-Fill-Dev. Remarkably, Moebius achieves this using less than 2\% of the parameters (0.22B vs. 11.9B) while delivering a >15× acceleration in total inference time, setting a new efficiency standard for high-fidelity inpainting.
Overall pipeline of Moebius. We adopt the Latent Diffusion Model (LDM) framework equipped with Latent Categories Guidance (LCG). To achieve extreme architectural efficiency, the denoising U-Net is systematically restructured using our proposed LλM I blocks (detailed in Sec. 3.2). Furthermore, an adaptive multi-granularity distillation strategy (Sec. 3.3) is applied during training to align our lightweight specialist with the high-capacity teacher, successfully mitigating the capacity drop caused by extreme structural compression.
@misc{DuanAndXu2026Moebius,
title={Moebius: 0.2B Lightweight Image Inpainting Framework with 10B-Level Performance},
author={Kangsheng Duan and Ziyang Xu and Wenyu Liu and Xiaohu Ruan and Xiaoxin Chen and Xinggang Wang},
year={2026},
eprint={2606.19195},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2606.19195},
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