  Personalization has emerged as a prominent aspect within the field of
generative AI, enabling the synthesis of individuals in diverse contexts and
styles, while retaining high-fidelity to their identities. However, the process
of personalization presents inherent challenges in terms of time and memory
requirements. Fine-tuning each personalized model needs considerable GPU time
investment, and storing a personalized model per subject can be demanding in
terms of storage capacity. To overcome these challenges, we propose
HyperDreamBooth-a hypernetwork capable of efficiently generating a small set of
personalized weights from a single image of a person. By composing these
weights into the diffusion model, coupled with fast finetuning, HyperDreamBooth
can generate a person's face in various contexts and styles, with high subject
details while also preserving the model's crucial knowledge of diverse styles
and semantic modifications. Our method achieves personalization on faces in
roughly 20 seconds, 25x faster than DreamBooth and 125x faster than Textual
Inversion, using as few as one reference image, with the same quality and style
diversity as DreamBooth. Also our method yields a model that is 10000x smaller
than a normal DreamBooth model. Project page: https://hyperdreambooth.github.io
