# Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Models

**Authors:** Moab Arar, Rinon Gal, Yuval Atzmon, Gal Chechik, Daniel Cohen-Or, Ariel Shamir, Amit H. Bermano  
**arXiv:** [2307.06925](https://arxiv.org/abs/2307.06925)  
**Date:** 2023-07-13  
**Summary page:** https://www.summarizepaper.com/en/paper/2307.06925v1/

## Key points

- Text-to-image (T2I) personalization is a popular approach for generating customized images based on natural language prompts.
- Existing encoder-based techniques are limited to single-class domains, restricting their ability to handle diverse concepts.
- The authors propose a domain-agnostic method that overcomes these limitations.
- They introduce a novel contrastive-based regularization technique to maintain high fidelity to target concept characteristics and keep predicted embeddings close to editable regions of the latent space.
- Experimental results show that the proposed method achieves state-of-the-art performance and generates more semantic tokens compared to unregularized models.
- The method requires less training time and memory compared to previous methods while achieving high quality and fast personalization across diverse domains.
- Related work in text-driven image generation using diffusion models and text-based image editing is discussed, highlighting progress driven by pre-trained diffusion models and large-scale text-to-image models.
- The proposed method builds upon pre-trained models to extend vocabulary and generate personalized concepts.

## Summary

Text-to-image (T2I) personalization has become a popular approach for generating customized images based on natural language prompts. However, existing encoder-based techniques are limited to single-class domains, which restricts their ability to handle diverse concepts. In this work, the authors propose a domain-agnostic method that overcomes these limitations. They introduce a novel contrastive-based regularization technique that maintains high fidelity to the target concept characteristics while keeping the predicted embeddings close to editable regions of the latent space. This is achieved by pushing the predicted tokens towards their nearest existing CLIP tokens. Experimental results demonstrate the effectiveness of this approach, showing that the learned tokens are more semantic than those predicted by unregularized models. The proposed method achieves state-of-the-art performance while being more flexible and requiring less training time and memory compared to previous methods. The authors also discuss related work in text-driven image generation using diffusion models and text-based image editing. They highlight the progress made in these areas, driven by pre-trained diffusion models and large-scale text-to-image models. Their approach builds upon these pre-trained models to extend their vocabulary and generate personalized concepts. Overall, this work presents a comprehensive solution for T2I personalization that addresses the limitations of existing encoders. The proposed method achieves high quality and fast personalization across diverse domains, making it a valuable contribution to the field of generative content creation.