title: Generative Powers of Ten
summary: We present a method that uses a text-to-image model to generate consistent
  content across multiple image scales, enabling extreme semantic zooms into a
  scene, e.g., ranging from a wide-angle landscape view of a forest to a macro
  shot of an insect sitting on one of the tree branches. We achieve this through
  a joint multi-scale diffusion sampling approach that encourages consistency
  across different scales while preserving the integrity of each individual
  sampling process. Since each generated scale is guided by a different text
  prompt, our method enables deeper levels of zoom than traditional
  super-resolution methods that may struggle to create new contextual structure
  at vastly different scales. We compare our method qualitatively with
  alternative techniques in image super-resolution and outpainting, and show that
  our method is most effective at generating consistent multi-scale content.
paper_authors._id: 656e9c174b5b6953536dbe36
paper_authors.name: Xiaojuan Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-05T09:38:28.597Z
paper_authors._id: 643ee9a6022a0350efd745d1
paper_authors.avatarUrl: /avatars/c8c17689ed88b1966d2a36361e3f83cc.svg
paper_authors.fullname: Xiaojuan Wang
paper_authors.type: user
paper_authors.user: xiaojwan
paper_authors._id: 656e9c174b5b6953536dbe37
paper_authors.name: Janne Kontkanen
paper_authors._id: 656e9c174b5b6953536dbe38
paper_authors.name: Brian Curless
paper_authors._id: 656e9c174b5b6953536dbe39
paper_authors.name: Steve Seitz
paper_authors._id: 656e9c174b5b6953536dbe3a
paper_authors.name: Ira Kemelmacher
paper_authors._id: 656e9c174b5b6953536dbe3b
paper_authors.name: Ben Mildenhall
paper_authors._id: 656e9c174b5b6953536dbe3c
paper_authors.name: Pratul Srinivasan
paper_authors._id: 656e9c174b5b6953536dbe3d
paper_authors.name: Dor Verbin
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-05T09:39:24.235Z
paper_authors._id: 6315b0fc915d0b806830b424
paper_authors.avatarUrl: /avatars/e25a7c8df2364c6e8024ec5c646a2889.svg
paper_authors.fullname: Dor Verbin
paper_authors.type: user
paper_authors.user: dorverbin
paper_authors._id: 656e9c174b5b6953536dbe3e
paper_authors.name: Aleksander Holynski
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-12-05T09:39:31.241Z
paper_authors._id: 62f6e2792e53c2efd33faa92
paper_authors.avatarUrl: /avatars/85d94b4022577747b8d2d10a82c2f3c7.svg
paper_authors.fullname: Aleksander Holynski
paper_authors.type: user
paper_authors.user: holynski
paper_title: Generative Powers of Ten
paper_summary: We present a method that uses a text-to-image model to generate consistent
  content across multiple image scales, enabling extreme semantic zooms into a
  scene, e.g., ranging from a wide-angle landscape view of a forest to a macro
  shot of an insect sitting on one of the tree branches. We achieve this through
  a joint multi-scale diffusion sampling approach that encourages consistency
  across different scales while preserving the integrity of each individual
  sampling process. Since each generated scale is guided by a different text
  prompt, our method enables deeper levels of zoom than traditional
  super-resolution methods that may struggle to create new contextual structure
  at vastly different scales. We compare our method qualitatively with
  alternative techniques in image super-resolution and outpainting, and show that
  our method is most effective at generating consistent multi-scale content.
paper_ai_keywords: text-to-image model multi-scale diffusion ordering semantic zooms wide-angle landscape view macro shot joint multi-scale diffusion ordering sampling process text prompt super-resolution methods contextual structure image super-resolution outpainting consistent multi-scale content