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

💡 The paper presents Moebius, a lightweight image inpainting framework that achieves high fidelity results with significantly reduced parameters and inference time. The problem addressed is that current industrial foundation models for image inpainting have high computational costs, making them impractical for deployment. To solve this, the authors propose a novel approach that reconstructs the diffusion backbone using local-global interaction blocks and adaptive distillation strategies.

The method involves introducing the Local-λ Mix Interaction block, which consists of Local-λ and Interactive-λ modules. This block summarizes spatial contexts and global semantic priors into fixed-size linear matrices, preserving complex latent interactions while reducing parameters. The authors also propose an adaptive multi-granularity distillation strategy that operates within the latent space to avoid expensive pixel-space decoding. This strategy dynamically balances multiple gradient-based losses to achieve high-fidelity alignment.

The results show that Moebius achieves high-fidelity image inpainting results that rival or surpass those of the 10B-level industrial generalist FLUX.1-Fill-Dev, while using less than 2% of the parameters and delivering a more than 15 times acceleration in total inference time. The Moebius framework has 0.22 billion parameters, compared to 11.9 billion parameters in the industrial generalist model, and achieves a new efficiency standard for high-fidelity inpainting. Overall, the paper presents a highly efficient and optimized image inpainting framework that can be practically deployed.

📅 Published on Jun 17

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.19195
• PDF: https://arxiv.org/pdf/2606.19195
• Project Page: https://hustvl.github.io/Moebius

🤖 Models citing this paper:
• https://huggingface.co/hustvl/Moebius
• https://huggingface.co/simonw/Moebius-ONNX

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/multimodalart/Moebius
• https://huggingface.co/spaces/Mike0021/moebius
• https://huggingface.co/spaces/jonatei/MoebiusDemo

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📢 By: https://xn--r1a.website/PaperNexus

#ImageInpainting #DiffusionBackbone #LightweightDeepLearning #LocalGlobalInteraction #EfficientComputerVision