README.md

metadata

library_name: pytorch
tags:
  - opensdi
  - maskclip
  - diffusion-detection
  - image-forensics
  - forgery-localization
  - pytorch
datasets:
  - nebula/OpenSDI_train
  - nebula/OpenSDI_test

MaskCLIP Weights for OpenSDI

This repository hosts model checkpoints for OpenSDI: Spotting Diffusion-Generated Images in the Open World.

Links

• Model weights: https://huggingface.co/nebula/MaskCLIP-weights/tree/main
• Code: https://github.com/iamwangyabin/OpenSDI
• Project page: https://iamwangyabin.github.io/OpenSDI/
• Paper: https://arxiv.org/abs/2503.19653
• Training dataset: https://huggingface.co/datasets/nebula/OpenSDI_train
• Testing dataset: https://huggingface.co/datasets/nebula/OpenSDI_test

Checkpoints

The Files and versions tab contains .pth checkpoints for MaskCLIP and related OpenSDI baselines. For MaskCLIP evaluation, download one of the MaskCLIP_sd15_*.pth checkpoints and use it with the OpenSDI codebase.

Example:

hf download nebula/MaskCLIP-weights MaskCLIP_sd15_20241103_17_45_16.pth --local-dir weights

Then set --checkpoint_path in test.sh to the downloaded checkpoint path, for example:

--checkpoint_path "weights/MaskCLIP_sd15_20241103_17_45_16.pth"

Citation

If you find OpenSDI useful for your research and applications, please cite:

@InProceedings{wang2025opensdi,
    author={Wang, Yabin and Huang, Zhiwu and Hong, Xiaopeng},
    title={OpenSDI: Spotting Diffusion-Generated Images in the Open World},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2025}
}