title: Applying Guidance in a Limited Interval Improves Sample and Distribution
    Quality in Diffusion Models
summary: Guidance is a crucial technique for extracting the best performance out of
  image-generating diffusion models. Traditionally, a constant guidance weight
  has been applied throughout the sampling chain of an image. We show that
  guidance is clearly harmful toward the beginning of the chain (high noise
  levels), largely unnecessary toward the end (low noise levels), and only
  beneficial in the middle. We thus restrict it to a specific range of noise
  levels, improving both the inference speed and result quality. This limited
  guidance interval improves the record FID in ImageNet-512 significantly, from
  1.81 to 1.40. We show that it is quantitatively and qualitatively beneficial
  across different sampler parameters, network architectures, and datasets,
  including the large-scale setting of Stable Diffusion XL. We thus suggest
  exposing the guidance interval as a hyperparameter in all diffusion models that
  use guidance.
paper_authors._id: 661898995267ebbf9096cf86
paper_authors.name: Tuomas Kynkäänniemi
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T12:29:49.813Z
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paper_authors.avatarUrl: /avatars/da8898374dc3677892e2538c2cfa6984.svg
paper_authors.fullname: Tuomas Kynkäänniemi
paper_authors.type: user
paper_authors.user: kynkaat
paper_authors._id: 661898995267ebbf9096cf87
paper_authors.name: Miika Aittala
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-04-18T12:29:57.828Z
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: 661898995267ebbf9096cf88
paper_authors.name: Tero Karras
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-05-31T06:53:31.082Z
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: 661898995267ebbf9096cf89
paper_authors.name: Samuli Laine
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-13T08:27:26.981Z
paper_authors._id: 67ac68cda78e0ee095322704
paper_authors.avatarUrl: /avatars/7cc9fbd7ba8c02e48b01e56bbccea01e.svg
paper_authors.fullname: Samuli Laine
paper_authors.type: user
paper_authors.user: slaine2
paper_authors._id: 661898995267ebbf9096cf8a
paper_authors.name: Timo Aila
paper_authors._id: 661898995267ebbf9096cf8b
paper_authors.name: Jaakko Lehtinen
paper_title: Applying Guidance in a Limited Interval Improves Sample and Distribution
    Quality in Diffusion Models
paper_summary: Guidance is a crucial technique for extracting the best performance out of
  image-generating diffusion models. Traditionally, a constant guidance weight
  has been applied throughout the sampling chain of an image. We show that
  guidance is clearly harmful toward the beginning of the chain (high noise
  levels), largely unnecessary toward the end (low noise levels), and only
  beneficial in the middle. We thus restrict it to a specific range of noise
  levels, improving both the inference speed and result quality. This limited
  guidance interval improves the record FID in ImageNet-512 significantly, from
  1.81 to 1.40. We show that it is quantitatively and qualitatively beneficial
  across different sampler parameters, network architectures, and datasets,
  including the large-scale setting of Stable Diffusion XL. We thus suggest
  exposing the guidance interval as a hyperparameter in all diffusion models that
  use guidance.
paper_ai_keywords: guidance diffusion models sampling chain noise levels high noise levels low noise levels inference speed result quality FID ImageNet-512 pipeline Stable Diffusion XL hyperparameter