title: Guiding a Diffusion Model with a Bad Version of Itself
summary: The primary axes of interest in image-generating diffusion models are image
  quality, the amount of variation in the results, and how well the results align
  with a given condition, e.g., a class label or a text prompt. The popular
  classifier-free guidance approach uses an unconditional model to guide a
  conditional model, leading to simultaneously better prompt alignment and
  higher-quality images at the cost of reduced variation. These effects seem
  inherently entangled, and thus hard to control. We make the surprising
  observation that it is possible to obtain disentangled control over image
  quality without compromising the amount of variation by guiding generation
  using a smaller, less-trained version of the model itself rather than an
  unconditional model. This leads to significant improvements in ImageNet
  generation, setting record FIDs of 1.01 for 64x64 and 1.25 for 512x512, using
  publicly available networks. Furthermore, the method is also applicable to
  unconditional diffusion models, drastically improving their quality.
paper_authors._id: 665fe7147c3f9c721120365d
paper_authors.name: Tero Karras
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-05T09:34:08.340Z
paper_authors._id: 66587947b7d313010028ac02
paper_authors.avatarUrl: /avatars/cb95f524dd1c80aa6ec8af977c5f2606.svg
paper_authors.fullname: Tero Karras
paper_authors.type: user
paper_authors.user: tkarras
paper_authors._id: 665fe7147c3f9c721120365e
paper_authors.name: Miika Aittala
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-05T09:34:14.745Z
paper_authors._id: 630f80e2a119d49bc1e3836f
paper_authors.avatarUrl: /avatars/e0ffe1c19df64606cb30801ad20a9f43.svg
paper_authors.fullname: Miika Aittala
paper_authors.type: user
paper_authors.user: miika
paper_authors._id: 665fe7147c3f9c721120365f
paper_authors.name: Tuomas Kynkäänniemi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-05T09:34:21.022Z
paper_authors._id: 632c060d930d186432d4e1db
paper_authors.avatarUrl: /avatars/da8898374dc3677892e2538c2cfa6984.svg
paper_authors.fullname: Tuomas Kynkäänniemi
paper_authors.type: user
paper_authors.user: kynkaat
paper_authors._id: 665fe7147c3f9c7211203660
paper_authors.name: Jaakko Lehtinen
paper_authors._id: 665fe7147c3f9c7211203661
paper_authors.name: Timo Aila
paper_authors._id: 665fe7147c3f9c7211203662
paper_authors.name: Samuli Laine
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-13T08:27:15.858Z
paper_authors._id: 67ac68cda78e0ee095322704
paper_authors.avatarUrl: /avatars/7cc9fbd7ba8c02e48b01e56bbccea01e.svg
paper_authors.fullname: Samuli Laine
paper_authors.type: user
paper_authors.user: slaine2
paper_title: Guiding a Diffusion Model with a Bad Version of Itself
paper_summary: The primary axes of interest in image-generating diffusion models are image
  quality, the amount of variation in the results, and how well the results align
  with a given condition, e.g., a class label or a text prompt. The popular
  classifier-free guidance approach uses an unconditional model to guide a
  conditional model, leading to simultaneously better prompt alignment and
  higher-quality images at the cost of reduced variation. These effects seem
  inherently entangled, and thus hard to control. We make the surprising
  observation that it is possible to obtain disentangled control over image
  quality without compromising the amount of variation by guiding generation
  using a smaller, less-trained version of the model itself rather than an
  unconditional model. This leads to significant improvements in ImageNet
  generation, setting record FIDs of 1.01 for 64x64 and 1.25 for 512x512, using
  publicly available networks. Furthermore, the method is also applicable to
  unconditional diffusion models, drastically improving their quality.
paper_ai_keywords: diffusion models image quality variation prompt alignment classifier-free guidance unconditional model conditional model FIDs