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An Improved Method for Personalizing Diffusion Models

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arxiv 2407.05312 v2 pith:FEHWT6GT submitted 2024-07-07 cs.CV

Yan Zeng , Masanori Suganuma , Takayuki Okatani This is my paper · ORCID
classification cs.CV
keywords textualdiffusiondreamboothimagesinversionmodelmodelsspecific
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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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