title: HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image
    Models
summary: 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
paper_authors._id: 64b0b332c434dbcdcd81ee70
paper_authors.name: Nataniel Ruiz
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-07-31T15:13:18.673Z
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paper_authors.fullname: Nataniel
paper_authors.type: user
paper_authors.user: flavoredquark
paper_authors._id: 64b0b332c434dbcdcd81ee71
paper_authors.name: Yuanzhen Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-17T07:00:40.798Z
paper_authors._id: 6334ee5831a2be3938c85639
paper_authors.avatarUrl: /avatars/9821af26a585f54b595ec39f061f2250.svg
paper_authors.fullname: Yuanzhen Li
paper_authors.type: user
paper_authors.user: yzli
paper_authors._id: 64b0b332c434dbcdcd81ee72
paper_authors.name: Varun Jampani
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-17T07:00:57.938Z
paper_authors._id: 630e7cfe3fc17ffc50f6e835
paper_authors.avatarUrl: /avatars/0b742ff094a09f9374fafcd97ab9e002.svg
paper_authors.fullname: Varun Jampani
paper_authors.type: user
paper_authors.user: varunjampani
paper_authors._id: 64b0b332c434dbcdcd81ee73
paper_authors.name: Wei Wei
paper_authors._id: 64b0b332c434dbcdcd81ee74
paper_authors.name: Tingbo Hou
paper_authors._id: 64b0b332c434dbcdcd81ee75
paper_authors.name: Yael Pritch
paper_authors._id: 64b0b332c434dbcdcd81ee76
paper_authors.name: Neal Wadhwa
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-07-17T10:05:45.243Z
paper_authors._id: 63041bb6eedc089484c82ed3
paper_authors.avatarUrl: /avatars/fca4b823f748cde5f5cff1212213523d.svg
paper_authors.fullname: Neal Wadhwa
paper_authors.type: user
paper_authors.user: NealWadhwa
paper_authors._id: 64b0b332c434dbcdcd81ee77
paper_authors.name: Michael Rubinstein
paper_authors._id: 64b0b332c434dbcdcd81ee78
paper_authors.name: Kfir Aberman
paper_title: HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image
    Models
paper_summary: 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
paper_ai_summary: HyperDreamBooth leverages hypernetworks for efficient personalized face generation, significantly speeding up the process and reducing model size compared to existing methods.
paper_ai_keywords: hypernetwork diffusion model DreamBooth Textual Inversion