title: RectifiedHR: Enable Efficient High-Resolution Image Generation via
    Energy Rectification
summary: Diffusion models have achieved remarkable advances in various image
  generation tasks. However, their performance notably declines when generating
  images at resolutions higher than those used during the training period.
  Despite the existence of numerous methods for producing high-resolution images,
  they either suffer from inefficiency or are hindered by complex operations. In
  this paper, we propose RectifiedHR, an efficient and straightforward solution
  for training-free high-resolution image generation. Specifically, we introduce
  the noise refresh strategy, which theoretically only requires a few lines of
  code to unlock the model's high-resolution generation ability and improve
  efficiency. Additionally, we first observe the phenomenon of energy decay that
  may cause image blurriness during the high-resolution image generation process.
  To address this issue, we propose an Energy Rectification strategy, where
  modifying the hyperparameters of the classifier-free guidance effectively
  improves the generation performance. Our method is entirely training-free and
  boasts a simple implementation logic. Through extensive comparisons with
  numerous baseline methods, our RectifiedHR demonstrates superior effectiveness
  and efficiency.
paper_authors._id: 67c808936f68759bf37a8eb8
paper_authors.name: Zhen Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-05T10:11:01.696Z
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paper_authors.avatarUrl: /avatars/a77ea80784896502ae1cfa086a78ce66.svg
paper_authors.fullname: Zhen Yang
paper_authors.type: user
paper_authors.user: YZCS
paper_authors._id: 67c808936f68759bf37a8eb9
paper_authors.name: Guibao Shen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-05T11:20:13.342Z
paper_authors._id: 65d5aa45dca2a85f0fe895f3
paper_authors.avatarUrl: /avatars/a3cbcade6ea101e99f58641aa409fdfe.svg
paper_authors.fullname: Guibao SHEN
paper_authors.type: user
paper_authors.user: PaulSHEN1
paper_authors._id: 67c808936f68759bf37a8eba
paper_authors.name: Liang Hou
paper_authors._id: 67c808936f68759bf37a8ebb
paper_authors.name: Mushui Liu
paper_authors._id: 67c808936f68759bf37a8ebc
paper_authors.name: Luozhou Wang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-05T11:35:12.288Z
paper_authors._id: 6340333a733f9eef46913dc8
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paper_authors.fullname: luozhou wang
paper_authors.type: user
paper_authors.user: wileewang
paper_authors._id: 67c808936f68759bf37a8ebd
paper_authors.name: Xin Tao
paper_authors._id: 67c808936f68759bf37a8ebe
paper_authors.name: Pengfei Wan
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-17T14:42:45.111Z
paper_authors._id: 662f93942510ef5735d7ad00
paper_authors.avatarUrl: /avatars/dc9486db75869ce902d0a638eea126bd.svg
paper_authors.fullname: magicwpf
paper_authors.type: user
paper_authors.user: magicwpf
paper_authors._id: 67c808936f68759bf37a8ebf
paper_authors.name: Di Zhang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-05T11:35:23.909Z
paper_authors._id: 644c8324f02250233d0d67d9
paper_authors.avatarUrl: /avatars/feb39d281457c1750f3eada3c060a23e.svg
paper_authors.fullname: Di Zhang
paper_authors.type: user
paper_authors.user: dizhang
paper_authors._id: 67c808936f68759bf37a8ec0
paper_authors.name: Ying-Cong Chen
paper_title: RectifiedHR: Enable Efficient High-Resolution Image Generation via
    Energy Rectification
paper_summary: Diffusion models have achieved remarkable advances in various image
  generation tasks. However, their performance notably declines when generating
  images at resolutions higher than those used during the training period.
  Despite the existence of numerous methods for producing high-resolution images,
  they either suffer from inefficiency or are hindered by complex operations. In
  this paper, we propose RectifiedHR, an efficient and straightforward solution
  for training-free high-resolution image generation. Specifically, we introduce
  the noise refresh strategy, which theoretically only requires a few lines of
  code to unlock the model's high-resolution generation ability and improve
  efficiency. Additionally, we first observe the phenomenon of energy decay that
  may cause image blurriness during the high-resolution image generation process.
  To address this issue, we propose an Energy Rectification strategy, where
  modifying the hyperparameters of the classifier-free guidance effectively
  improves the generation performance. Our method is entirely training-free and
  boasts a simple implementation logic. Through extensive comparisons with
  numerous baseline methods, our RectifiedHR demonstrates superior effectiveness
  and efficiency.
paper_ai_keywords: diffusion models image generation high-resolution image generation noise refresh strategy energy decay Energy Rectification strategy classifier-free guidance