arxiv:2407.05312

An Improved Method for Personalizing Diffusion Models

Published on Jul 7, 2024

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Abstract

The proposed method integrates new information into diffusion models without losing original knowledge, improving image generation outcomes and reducing training time compared to existing approaches.

AI-generated summary

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.

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