An Improved Method for Personalizing Diffusion Models

Yan Zeng, Masanori Suganuma, Takayuki Okatani
7/7/2024
cs.CV

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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.

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Cite this paper

@article{zeng2024improved,
  title  = {An Improved Method for Personalizing Diffusion Models},
  author = {Yan Zeng and Masanori Suganuma and Takayuki Okatani},
  year   = {2024},
  eprint = {2407.05312},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.05312v1}
}

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