| --- |
| 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: |
|
|
| ```bash |
| 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: |
|
|
| ```bash |
| --checkpoint_path "weights/MaskCLIP_sd15_20241103_17_45_16.pth" |
| ``` |
|
|
| ## Citation |
|
|
| If you find OpenSDI useful for your research and applications, please cite: |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
