An Improved Method for Personalizing Diffusion Models

TL;DR

This paper introduces an enhanced method for personalizing diffusion models that preserves original knowledge, improves image generation quality, and reduces training time compared to existing techniques like Dreambooth and textual inversion.

Contribution

The proposed approach effectively retains original model knowledge during personalization, leading to better results with less training time than prior methods.

Findings

Outperforms Dreambooth and textual inversion in image quality.

Requires less training time for personalization.

Maintains original model knowledge effectively.

Abstract

Diffusion models have demonstrated impressive image generation capabilities. Personalized approaches, such as textual inversion and Dreambooth, enhance model individualization using specific images. These methods enable generating images of specific objects based on diverse textual contexts. Our proposed approach aims to retain the model's original knowledge during new information integration, resulting in superior outcomes while necessitating less training time compared to Dreambooth and textual inversion.