title: Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic
    Image Restoration In the Wild
summary: We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image
  restoration method that harnesses generative prior and the power of model
  scaling up. Leveraging multi-modal techniques and advanced generative prior,
  SUPIR marks a significant advance in intelligent and realistic image
  restoration. As a pivotal catalyst within SUPIR, model scaling dramatically
  enhances its capabilities and demonstrates new potential for image restoration.
  We collect a dataset comprising 20 million high-resolution, high-quality images
  for model training, each enriched with descriptive text annotations. SUPIR
  provides the capability to restore images guided by textual prompts, broadening
  its application scope and potential. Moreover, we introduce negative-quality
  prompts to further improve perceptual quality. We also develop a
  restoration-guided sampling method to suppress the fidelity issue encountered
  in generative-based restoration. Experiments demonstrate SUPIR's exceptional
  restoration effects and its novel capacity to manipulate restoration through
  textual prompts.
paper_authors._id: 65b1ef73b4225e3e4235abd9
paper_authors.name: Fanghua Yu
paper_authors._id: 65b1ef73b4225e3e4235abda
paper_authors.name: Jinjin Gu
paper_authors._id: 65b1ef73b4225e3e4235abdb
paper_authors.name: Zheyuan Li
paper_authors._id: 65b1ef73b4225e3e4235abdc
paper_authors.name: Jinfan Hu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-09T14:02:28.165Z
paper_authors._id: 65324b8aaed617662c96d5ae
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paper_authors.fullname: Jinfan_Hu
paper_authors.type: user
paper_authors.user: jfhu
paper_authors._id: 65b1ef73b4225e3e4235abdd
paper_authors.name: Xiangtao Kong
paper_authors._id: 65b1ef73b4225e3e4235abde
paper_authors.name: Xintao Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-01-25T08:19:01.494Z
paper_authors._id: 60e272ca6c78a8c122b12127
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paper_authors.fullname: Xintao Wang
paper_authors.type: user
paper_authors.user: Xintao
paper_authors._id: 65b1ef73b4225e3e4235abdf
paper_authors.name: Jingwen He
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-11T15:01:43.704Z
paper_authors._id: 66a49f9b2b460286b05646b8
paper_authors.avatarUrl: /avatars/5123cf7c3d00b5bc47a86011afd6abe8.svg
paper_authors.fullname: jwhe
paper_authors.type: user
paper_authors.user: jwhejwhe
paper_authors._id: 65b1ef73b4225e3e4235abe0
paper_authors.name: Yu Qiao
paper_authors._id: 65b1ef73b4225e3e4235abe1
paper_authors.name: Chao Dong
paper_title: Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic
    Image Restoration In the Wild
paper_summary: We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image
  restoration method that harnesses generative prior and the power of model
  scaling up. Leveraging multi-modal techniques and advanced generative prior,
  SUPIR marks a significant advance in intelligent and realistic image
  restoration. As a pivotal catalyst within SUPIR, model scaling dramatically
  enhances its capabilities and demonstrates new potential for image restoration.
  We collect a dataset comprising 20 million high-resolution, high-quality images
  for model training, each enriched with descriptive text annotations. SUPIR
  provides the capability to restore images guided by textual prompts, broadening
  its application scope and potential. Moreover, we introduce negative-quality
  prompts to further improve perceptual quality. We also develop a
  restoration-guided sampling method to suppress the fidelity issue encountered
  in generative-based restoration. Experiments demonstrate SUPIR's exceptional
  restoration effects and its novel capacity to manipulate restoration through
  textual prompts.
paper_ai_keywords: SUPIR generative prior model scaling multi-modal techniques high-resolution high-quality images descriptive text annotations textual prompts negative-quality prompts perceptual quality restoration-guided sampling method generative-based restoration restoration effects