README.md

metadata

license: mit
pipeline_tag: image-to-image

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

Moebius is a highly efficient, lightweight image inpainting framework that operates with a mere 0.22B parameters (less than 2% of the size of the 11.9B FLUX.1-Fill-Dev) while delivering comparable or superior generation quality and a $>15\times$ acceleration in total inference time.

Key Features

• Extreme Parametric Efficiency (< 2%): Moebius operates with a mere 0.22B (226M) parameters, bypassing heavy-compute bottlenecks.
• 15× Inference Speedup: Blistering inference latency of only 26.01 ms per step on a single GPU.
• 10B-Level Inpainting Quality: Performs on par with, and in certain scenarios surpasses, SOTA generalist models (FLUX.1-Fill-Dev, SD3.5 Large-Inpainting) across both natural and portrait scenes.

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📦 Environment Setup

To get started, clone the official GitHub repository, set up the environment, and install dependencies:

git clone https://github.com/hustvl/Moebius.git
cd Moebius
conda create -n moebius python=3.14.4
conda activate moebius
pip install -r requirements.txt

🗃️ Model Checkpoints Setup

Organize your model weights in the ./weight folder as follows:

1. Download the VAE checkpoint from hustvl/PixelHacker and put it into ./weight/vae.
2. Download the Moebius checkpoints (e.g., pretrained, ft_places2, ft_celebahq, ft_ffhq) and place them under ./weight/Moebius.

├── weight
|   ├── Moebius
|        ├── pretrained
|            ├── diffusion_pytorch_model.bin
|        ├── ft_places2
|            ├── diffusion_pytorch_model.bin
|        ├── ft_celebahq
|            ├── diffusion_pytorch_model.bin
|        ├── ft_ffhq
|            ├── diffusion_pytorch_model.bin
|    ├── vae
|        ├── config.json
|        ├── diffusion_pytorch_model.bin

🔮 Inference

Run the following command to perform inpainting on custom images and masks. Place your input images and masks with matching filenames under data/images and data/masks respectively:

python -m infer.infer_moebius \
    --model-config config/model_cfg/moebius.yaml \
    --model-weight weight/Moebius/ft_celebahq/diffusion_pytorch_model.bin \
    --real-dir data/images \
    --mask-dir data/masks \
    --save-dir ./outputs \
    --cfg 2.0 \
    --batch-size 8 \
    --num-workers 8

🎓 Citation

If you find Moebius useful in your research, please consider citing:

@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}, 
}