ViPer: Visual Personalization of Generative Models via Individual Preference Learning

Tuning-free framework for personalized image generation

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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.

Installation

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

Usage

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

from transformers import AutoProcessor, BitsAndBytesConfig, AutoModelForVision2Seq
from peft import PeftModel

model = AutoModelForVision2Seq.from_pretrained("HuggingFaceM4/idefics2-8b")
model = PeftModel.from_pretrained(model, "EPFL-VILAB/Metric-ViPer")

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

Citation

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},
}

License

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