title: Exploiting Diffusion Prior for Real-World Image Super-Resolution
summary: 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.
paper_authors._id: 645d994c7c7258d904e8977a
paper_authors.name: Jianyi Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-16T09:55:56.457Z
paper_authors._id: 63043db17373aacccd89f49d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63043db17373aacccd89f49d/jzP_fPCFXeYJvAD8uA_N7.jpeg
paper_authors.fullname: JIANYI WANG
paper_authors.type: user
paper_authors.user: Iceclear
paper_authors._id: 645d994c7c7258d904e8977b
paper_authors.name: Zongsheng Yue
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-02T13:15:03.124Z
paper_authors._id: 630ad0dd2ff113e0fb31c6b0
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1671174653229-630ad0dd2ff113e0fb31c6b0.jpeg
paper_authors.fullname: Zongsheng Yue
paper_authors.type: user
paper_authors.user: OAOA
paper_authors._id: 645d994c7c7258d904e8977c
paper_authors.name: Shangchen Zhou
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-02T13:15:12.029Z
paper_authors._id: 62e57662ae9d3f10acbb1b1b
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62e57662ae9d3f10acbb1b1b/lg58jdbNyv6LGH2LFnZDF.png
paper_authors.fullname: Shangchen Zhou
paper_authors.type: user
paper_authors.user: sczhou
paper_authors._id: 645d994c7c7258d904e8977d
paper_authors.name: Kelvin C. K. Chan
paper_authors._id: 645d994c7c7258d904e8977e
paper_authors.name: Chen Change Loy
paper_title: Exploiting Diffusion Prior for Real-World Image Super-Resolution
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: text-to-image diffusion models blind super-resolution time-aware encoder generative prior controllable feature wrapping module progressive aggregation sampling strategy