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.
Abstract (translated)
扩散模型已经展示了令人印象深刻的图像生成能力。个人化方法（如文本反演和Dreambooth）通过针对特定图像来增强模型的个性化程度。这些方法使用特定的图像来生成特定物体的图像，基于不同的文本上下文。我们提出的方法旨在在信息融合过程中保留模型的原始知识，从而在需要更少的训练时间的同时实现卓越的性能。
URL
https://arxiv.org/abs/2407.05312
