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Users should be aware of the limitations and biases of the model and use it accordingly. They should also be prepared to handle false positives and false negatives. It is recommended to fine-tune the model for specific downstream tasks and to evaluate its performance on relevant datasets.
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### Load model directly
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```python
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Users should be aware of the limitations and biases of the model and use it accordingly. They should also be prepared to handle false positives and false negatives. It is recommended to fine-tune the model for specific downstream tasks and to evaluate its performance on relevant datasets.
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## Citation
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If you find our work useful, please consider citing us!
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```bibtex
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@misc{khader2025diffguardtextbasedsafetychecker,
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title={DiffGuard: Text-Based Safety Checker for Diffusion Models},
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author={Massine El Khader and Elias Al Bouzidi and Abdellah Oumida and Mohammed Sbaihi and Eliott Binard and Jean-Philippe Poli and Wassila Ouerdane and Boussad Addad and Katarzyna Kapusta},
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year={2025},
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eprint={2412.00064},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2412.00064},
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}
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```
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### Load model directly
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```python
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