Exploiting Diffusion Prior for Real-World Image Super-Resolution
We present a novel approach to leverage prior knowledge encapsulated in
pre-trained text-to-image diffusion models for blind super-resolution (SR).
Specifically, by employing our time-aware encoder, we can achieve promising
restoration results without altering the pre-trained synthesis model, thereby
preserving the generative prior and minimizing training cost. To remedy the
loss of fidelity caused by the inherent stochasticity of diffusion models, we
introduce a controllable feature wrapping module that allows users to balance
quality and fidelity by simply adjusting a scalar value during the inference
process. Moreover, we develop a progressive aggregation sampling strategy to
overcome the fixed-size constraints of pre-trained diffusion models, enabling
adaptation to resolutions of any size. A comprehensive evaluation of our method
using both synthetic and real-world benchmarks demonstrates its superiority
over current state-of-the-art approaches.
645d994c7c7258d904e8977a
Jianyi Wang
claimed_verified
2023-05-16T09:55:56.457Z
63043db17373aacccd89f49d
https://cdn-avatars.huggingface.co/v1/production/uploads/63043db17373aacccd89f49d/jzP_fPCFXeYJvAD8uA_N7.jpeg
JIANYI WANG
user
Iceclear
645d994c7c7258d904e8977b
Zongsheng Yue
admin_assigned
2023-06-02T13:15:03.124Z
630ad0dd2ff113e0fb31c6b0
https://cdn-avatars.huggingface.co/v1/production/uploads/1671174653229-630ad0dd2ff113e0fb31c6b0.jpeg
Zongsheng Yue
user
OAOA
645d994c7c7258d904e8977c
Shangchen Zhou
admin_assigned
2023-06-02T13:15:12.029Z
62e57662ae9d3f10acbb1b1b
https://cdn-avatars.huggingface.co/v1/production/uploads/62e57662ae9d3f10acbb1b1b/lg58jdbNyv6LGH2LFnZDF.png
Shangchen Zhou
user
sczhou
645d994c7c7258d904e8977d
Kelvin C. K. Chan
645d994c7c7258d904e8977e
Chen Change Loy
Exploiting Diffusion Prior for Real-World Image Super-Resolution
We present a novel approach to leverage prior knowledge encapsulated in
pre-trained text-to-image diffusion models for blind super-resolution (SR).
Specifically, by employing our time-aware encoder, we can achieve promising
restoration results without altering the pre-trained synthesis model, thereby
preserving the generative prior and minimizing training cost. To remedy the
loss of fidelity caused by the inherent stochasticity of diffusion models, we
introduce a controllable feature wrapping module that allows users to balance
quality and fidelity by simply adjusting a scalar value during the inference
process. Moreover, we develop a progressive aggregation sampling strategy to
overcome the fixed-size constraints of pre-trained diffusion models, enabling
adaptation to resolutions of any size. A comprehensive evaluation of our method
using both synthetic and real-world benchmarks demonstrates its superiority
over current state-of-the-art approaches.
A novel method leverages pre-trained text-to-image diffusion models for blind super-resolution, using a time-aware encoder and a controllable feature wrapping module to enhance fidelity and adaptability to varying resolutions.
text-to-image diffusion models blind super-resolution time-aware encoder generative prior controllable feature wrapping module progressive aggregation sampling strategy