[AAAI 2025] Click2Mask: Local Editing with Dynamic Mask Generation

Official Model Card for "Click2Mask: Local Editing with Dynamic Mask Generation".

Paper by: Omer Regev, Omri Avrahami, Dani Lischinski

Given an image, a Click, and a prompt for an added object, a Mask is generated dynamically, simultaneously with the object generation throughout the diffusion process.

Current methods rely on existing objects/segments, or user effort (masks/detailed text), to localize object additions. Our approach enables free-form editing, where the manipulated area is not well-defined, using just a Click for localization.

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For additional usage options, please refer to our repository.

Examples

Qualitative Comparisons with SoTA Methods

A brief glimpse into the qualitative comparison of SoTA methods — Emu Edit, MagicBrush and InstructPix2Pix — against our model, Click2Mask.
Upper prompts were given to baselines, and lower (shorter) ones to Click2Mask. Inputs contain the Click given to Click2Mask.

Evaluating Edited Regions in Maskless Methods

We introduce Edited Alpha-CLIP to evaluate mask-free methods by extracting a mask of the edited region and using Alpha-CLIP to assess its alignment with the prompt.
Examples of mask extractions: outputs are on the left, extracted masks (green overlay) on the right.

Citation

If you find this helpful for your research, please reference the following:

@inproceedings{regev2025click2mask,
    title={Click2Mask: Local Editing with Dynamic Mask Generation},
    author={Regev, Omer and Avrahami, Omri and Lischinski, Dani},
    booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
    volume={39},
    number={7},
    pages={6713-6721},
    year={2025},
    url={https://arxiv.org/abs/2409.08272},
    note={Full version with appendices available on arXiv}
}

Acknowledgements

This code is based on Blended Latent Diffusion and Stable Diffusion.