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We introduce ViPer, a method that personalizes the output of generative models to align with different users’ visual preferences for the same prompt. This is done via a one-time capture of the user’s general preferences and conditioning the generative model on them without the need for engineering detailed prompts.

For install instructions, please see https://github.com/EPFL-VILAB/ViPer.

This model can be loaded from Hugging Face Hub as follows:

Please see https://github.com/EPFL-VILAB/ViPer for more detailed instructions.

For more examples and interactive demos, please see our website and Hugging Face Space.

If you find this repository helpful, please consider citing our work:

@article{ViPer,
  title={{ViPer}: Visual Personalization of Generative Models via Individual Preference Learning},
  author={Sogand Salehi and Mahdi Shafiei and Teresa Yeo and Roman Bachmann and Amir Zamir},
  journal={arXiv preprint arXiv:2407.17365},
  year={2024},
}

Licensed under the Apache License, Version 2.0. See LICENSE for details.