Applying Guidance in a Limited Interval Improves Sample and Distribution
  Quality in Diffusion Models
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.
661898995267ebbf9096cf86
Tuomas Kynkäänniemi
admin_assigned
2024-04-18T12:29:49.813Z
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Tuomas Kynkäänniemi
user
kynkaat
661898995267ebbf9096cf87
Miika Aittala
admin_assigned
2024-04-18T12:29:57.828Z
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Miika Aittala
user
miika
661898995267ebbf9096cf88
Tero Karras
claimed_verified
2024-05-31T06:53:31.082Z
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/avatars/cb95f524dd1c80aa6ec8af977c5f2606.svg
Tero Karras
user
tkarras
661898995267ebbf9096cf89
Samuli Laine
claimed_verified
2025-02-13T08:27:26.981Z
67ac68cda78e0ee095322704
/avatars/7cc9fbd7ba8c02e48b01e56bbccea01e.svg
Samuli Laine
user
slaine2
661898995267ebbf9096cf8a
Timo Aila
661898995267ebbf9096cf8b
Jaakko Lehtinen
Applying Guidance in a Limited Interval Improves Sample and Distribution
  Quality in Diffusion Models
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.
Applying guidance only within a specific noise level interval in image-generating diffusion models improves inference speed and result quality across various settings.
diffusion models guidance sampling chain noise levels FID ImageNet-512 sampler parameters network architectures Stable Diffusion XL