Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors
Diffusion-based image super-resolution (SR) methods have achieved remarkable
success by leveraging large pre-trained text-to-image diffusion models as
priors. However, these methods still face two challenges: the requirement for
dozens of sampling steps to achieve satisfactory results, which limits
efficiency in real scenarios, and the neglect of degradation models, which are
critical auxiliary information in solving the SR problem. In this work, we
introduced a novel one-step SR model, which significantly addresses the
efficiency issue of diffusion-based SR methods. Unlike existing fine-tuning
strategies, we designed a degradation-guided Low-Rank Adaptation (LoRA) module
specifically for SR, which corrects the model parameters based on the
pre-estimated degradation information from low-resolution images. This module
not only facilitates a powerful data-dependent or degradation-dependent SR
model but also preserves the generative prior of the pre-trained diffusion
model as much as possible. Furthermore, we tailor a novel training pipeline by
introducing an online negative sample generation strategy. Combined with the
classifier-free guidance strategy during inference, it largely improves the
perceptual quality of the super-resolution results. Extensive experiments have
demonstrated the superior efficiency and effectiveness of the proposed model
compared to recent state-of-the-art methods.
66f4de6a634015dd130d67e9
Aiping Zhang
claimed_verified
2024-09-30T08:07:18.756Z
66f4cf499af27c74e9ac6201
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zhangaip
user
zhangap
66f4de6a634015dd130d67ea
Zongsheng Yue
admin_assigned
2024-09-26T10:23:32.856Z
630ad0dd2ff113e0fb31c6b0
https://cdn-avatars.huggingface.co/v1/production/uploads/1671174653229-630ad0dd2ff113e0fb31c6b0.jpeg
Zongsheng Yue
user
OAOA
66f4de6a634015dd130d67eb
Renjing Pei
66f4de6a634015dd130d67ec
Wenqi Ren
admin_assigned
2024-09-26T10:23:46.442Z
65ca318d5a072a440424211d
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wenqi ren 
user
Akowang
66f4de6a634015dd130d67ed
Xiaochun Cao
admin_assigned
2024-09-26T10:23:57.418Z
64ddc5ea83645d0f98599d6e
/avatars/99a148c29cfbff77ed6150cc279a47c0.svg
caoxiaochun
user
cxc361461518
Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors
Diffusion-based image super-resolution (SR) methods have achieved remarkable
success by leveraging large pre-trained text-to-image diffusion models as
priors. However, these methods still face two challenges: the requirement for
dozens of sampling steps to achieve satisfactory results, which limits
efficiency in real scenarios, and the neglect of degradation models, which are
critical auxiliary information in solving the SR problem. In this work, we
introduced a novel one-step SR model, which significantly addresses the
efficiency issue of diffusion-based SR methods. Unlike existing fine-tuning
strategies, we designed a degradation-guided Low-Rank Adaptation (LoRA) module
specifically for SR, which corrects the model parameters based on the
pre-estimated degradation information from low-resolution images. This module
not only facilitates a powerful data-dependent or degradation-dependent SR
model but also preserves the generative prior of the pre-trained diffusion
model as much as possible. Furthermore, we tailor a novel training pipeline by
introducing an online negative sample generation strategy. Combined with the
classifier-free guidance strategy during inference, it largely improves the
perceptual quality of the super-resolution results. Extensive experiments have
demonstrated the superior efficiency and effectiveness of the proposed model
compared to recent state-of-the-art methods.
The proposed one-step super-resolution model enhances efficiency and quality by using a degradation-guided LoRA module and an online negative sample generation strategy in combination with classifier-free guidance.
diffusion-based super-resolution text-to-image diffusion models sampling steps degradation models one-step SR model Low-Rank Adaptation (LoRA) pre-estimated degradation information data-dependent generative prior online negative sample generation classifier-free guidance perceptual quality