title: StyleDrop: Text-to-Image Generation in Any Style
summary: Pre-trained large text-to-image models synthesize impressive images with an
  appropriate use of text prompts. However, ambiguities inherent in natural
  language and out-of-distribution effects make it hard to synthesize image
  styles, that leverage a specific design pattern, texture or material. In this
  paper, we introduce StyleDrop, a method that enables the synthesis of images
  that faithfully follow a specific style using a text-to-image model. The
  proposed method is extremely versatile and captures nuances and details of a
  user-provided style, such as color schemes, shading, design patterns, and local
  and global effects. It efficiently learns a new style by fine-tuning very few
  trainable parameters (less than 1% of total model parameters) and improving
  the quality via iterative training with either human or automated feedback.
  Better yet, StyleDrop is able to deliver impressive results even when the user
  supplies only a single image that specifies the desired style. An extensive
  study shows that, for the task of style tuning text-to-image models, StyleDrop
  implemented on Muse convincingly outperforms other methods, including
  DreamBooth and textual inversion on Imagen or Stable Diffusion. More results
  are available at our project website: https://styledrop.github.io
paper_authors._id: 64793ddf12ae749b62ed0b66
paper_authors.name: Kihyuk Sohn
paper_authors._id: 64793ddf12ae749b62ed0b67
paper_authors.name: Nataniel Ruiz
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paper_authors.fullname: Nataniel
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paper_authors.user: flavoredquark
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paper_authors.name: Kimin Lee
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paper_authors.fullname: Kimin Lee
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paper_authors.user: kiminle2
paper_authors._id: 64793ddf12ae749b62ed0b69
paper_authors.name: Daniel Castro Chin
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paper_authors.name: Irina Blok
paper_authors._id: 64793ddf12ae749b62ed0b6b
paper_authors.name: Huiwen Chang
paper_authors._id: 64793ddf12ae749b62ed0b6c
paper_authors.name: Jarred Barber
paper_authors._id: 64793ddf12ae749b62ed0b6d
paper_authors.name: Lu Jiang
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paper_authors.fullname: Lu Jiang
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paper_authors.user: roadjiang
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paper_authors.name: Glenn Entis
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paper_authors.fullname: Glenn Entis
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paper_authors.user: GEinSF
paper_authors._id: 64793ddf12ae749b62ed0b6f
paper_authors.name: Yuanzhen Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-07T13:01:39.465Z
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: 64793ddf12ae749b62ed0b70
paper_authors.name: Yuan Hao
paper_authors._id: 64793ddf12ae749b62ed0b71
paper_authors.name: Irfan Essa
paper_authors._id: 64793ddf12ae749b62ed0b72
paper_authors.name: Michael Rubinstein
paper_authors._id: 64793ddf12ae749b62ed0b73
paper_authors.name: Dilip Krishnan
paper_title: StyleDrop: Text-to-Image Generation in Any Style
paper_summary: Pre-trained large text-to-image models synthesize impressive images with an
  appropriate use of text prompts. However, ambiguities inherent in natural
  language and out-of-distribution effects make it hard to synthesize image
  styles, that leverage a specific design pattern, texture or material. In this
  paper, we introduce StyleDrop, a method that enables the synthesis of images
  that faithfully follow a specific style using a text-to-image model. The
  proposed method is extremely versatile and captures nuances and details of a
  user-provided style, such as color schemes, shading, design patterns, and local
  and global effects. It efficiently learns a new style by fine-tuning very few
  trainable parameters (less than 1% of total model parameters) and improving
  the quality via iterative training with either human or automated feedback.
  Better yet, StyleDrop is able to deliver impressive results even when the user
  supplies only a single image that specifies the desired style. An extensive
  study shows that, for the task of style tuning text-to-image models, StyleDrop
  implemented on Muse convincingly outperforms other methods, including
  DreamBooth and textual inversion on Imagen or Stable Diffusion. More results
  are available at our project website: https://styledrop.github.io
paper_ai_summary: StyleDrop fine-tunes pre-trained text-to-image models to synthesize images in a specific style using minimal parameters and feedback.
paper_ai_keywords: text-to-image models StyleDrop fine-tuning parameter-efficient fine-tuning iterative training human feedback automated feedback Muse DreamBooth textual inversion Imagen Stable Diffusion