README.md

---
license: cc-by-nc-4.0
tags:
- hyperbolic
- clip
- safeclip
- vision-and-language
- retrieval
- safety
- nsfw
- responsible ai
pipeline_tag: image-text-to-text
library_name: pytorch
---

# Model Card: HySAC

Hyperbolic Safety-Aware CLIP (HySAC), introduced in the paper [**Hyperbolic Safety-Aware Vision-Language Models**](https://arxiv.org/abs/2503.12127), is a fine-tuned CLIP model that leverages the hierarchical properties of hyperbolic space to enhance safety in vision-language tasks. HySAC models the relationship between safe and unsafe image-text pairs, enabling effective retrieval of unsafe content and the ability to dynamically redirect unsafe queries to safer alternatives.

## NSFW Definition
In our work we use the [Safe-CLIP's definition of NSFW](https://arxiv.org/abs/2211.05105): a finite and fixed set concepts that are considered inappropriate, offensive, or harmful to individuals. These concepts are divided into seven categories: _hate, harassment, violence, self-harm, sexual, shocking and illegal activities_.

#### Use HySAC
The HySAC model can be loaded and used as shown below. Ensure you have installed the HySAC code from [our github repository](https://github.com/aimagelab/HySAC).

```python
>>> from hysac.models import HySAC

>>> model_id = "aimagelab/hysac"
>>> model = HySAC.from_pretrained(model_id, device="cuda").to("cuda")
```

Standard methods `encode_image` and `encode_text` encode images and text. The `traverse_to_safe_image` and `traverse_to_safe_text` methods can be used to direct query embeddings towards safer alternatives.


## Model Details

HySAC is a fine-tuned version of the CLIP model, trained in hyperbolic space using the ViSU (Visual Safe and Unsafe) Dataset, introduced in [this paper](https://arxiv.org/abs/2311.16254). The text portion of the ViSU dataset is publicly available on HuggingFace as [ViSU-Text](https://huggingface.co/datasets/aimagelab/ViSU-Text). The image portion is not released due to the presence of potentially harmful content.

**Model Release Date** 17 March 2025.

For more information about the model, training details, dataset, and evaluation, please refer to the [paper](https://arxiv.org/abs/2503.12127).
Additional details are available in the [official HySAC repository](https://github.com/aimagelab/HySAC).


## Citation

Please cite with the following BibTeX:
```
@inproceedings{poppi2025hyperbolic,
  title={{Hyperbolic Safety-Aware Vision-Language Models}},
  author={Poppi, Tobia and Kasarla, Tejaswi and Mettes, Pascal and Baraldi, Lorenzo and Cucchiara, Rita},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2025}
}
```