Arbitrary-steps Image Super-resolution via Diffusion Inversion
This study presents a new image super-resolution (SR) technique based on
diffusion inversion, aiming at harnessing the rich image priors encapsulated in
large pre-trained diffusion models to improve SR performance. We design a
Partial noise Prediction strategy to construct an intermediate state of the
diffusion model, which serves as the starting sampling point. Central to our
approach is a deep noise predictor to estimate the optimal noise maps for the
forward diffusion process. Once trained, this noise predictor can be used to
initialize the sampling process partially along the diffusion trajectory,
generating the desirable high-resolution result. Compared to existing
approaches, our method offers a flexible and efficient sampling mechanism that
supports an arbitrary number of sampling steps, ranging from one to five. Even
with a single sampling step, our method demonstrates superior or comparable
performance to recent state-of-the-art approaches. The code and model are
publicly available at https://github.com/zsyOAOA/InvSR.
675bc39259bc9663aeea8ab0
Zongsheng Yue
admin_assigned
2024-12-16T11:05:19.850Z
630ad0dd2ff113e0fb31c6b0
https://cdn-avatars.huggingface.co/v1/production/uploads/1671174653229-630ad0dd2ff113e0fb31c6b0.jpeg
Zongsheng Yue
user
OAOA
675bc39259bc9663aeea8ab1
Kang Liao
claimed_verified
2025-04-14T09:47:53.390Z
65bc98383b879593a5a2f5e5
https://cdn-avatars.huggingface.co/v1/production/uploads/65bc98383b879593a5a2f5e5/p2ZtoTFN6tW-QkcPJf7YT.jpeg
Kang Liao
user
KangLiao
675bc39259bc9663aeea8ab2
Chen Change Loy
admin_assigned
2024-12-16T11:06:11.897Z
67459d997a49660f7f62452f
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/X25yTdxC_hcLVxpx4voZo.png
Chen Change Loy
user
cavanloy
Arbitrary-steps Image Super-resolution via Diffusion Inversion
This study presents a new image super-resolution (SR) technique based on
diffusion inversion, aiming at harnessing the rich image priors encapsulated in
large pre-trained diffusion models to improve SR performance. We design a
Partial noise Prediction strategy to construct an intermediate state of the
diffusion model, which serves as the starting sampling point. Central to our
approach is a deep noise predictor to estimate the optimal noise maps for the
forward diffusion process. Once trained, this noise predictor can be used to
initialize the sampling process partially along the diffusion trajectory,
generating the desirable high-resolution result. Compared to existing
approaches, our method offers a flexible and efficient sampling mechanism that
supports an arbitrary number of sampling steps, ranging from one to five. Even
with a single sampling step, our method demonstrates superior or comparable
performance to recent state-of-the-art approaches. The code and model are
publicly available at https://github.com/zsyOAOA/InvSR.
A novel image super-resolution technique uses diffusion inversion with a noise predictor to enhance performance.
diffusion inversion image priors diffusion models Partial noise Prediction deep noise predictor sampling process diffusion trajectory image super-resolution