title: FouriScale: A Frequency Perspective on Training-Free High-Resolution
    Image Synthesis
summary: In this study, we delve into the generation of high-resolution images from
  pre-trained diffusion models, addressing persistent challenges, such as
  repetitive patterns and structural distortions, that emerge when models are
  applied beyond their trained resolutions. To address this issue, we introduce
  an innovative, training-free approach FouriScale from the perspective of
  frequency domain analysis. We replace the original convolutional layers in
  pre-trained diffusion models by incorporating a dilation technique along with a
  low-pass operation, intending to achieve structural consistency and scale
  consistency across resolutions, respectively. Further enhanced by a
  padding-then-crop strategy, our method can flexibly handle text-to-image
  generation of various aspect ratios. By using the FouriScale as guidance, our
  method successfully balances the structural integrity and fidelity of generated
  images, achieving an astonishing capacity of arbitrary-size, high-resolution,
  and high-quality generation. With its simplicity and compatibility, our method
  can provide valuable insights for future explorations into the synthesis of
  ultra-high-resolution images. The code will be released at
  https://github.com/LeonHLJ/FouriScale.
paper_authors._id: 65fa4422becc987834315f70
paper_authors.name: Linjiang Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-22T08:38:16.184Z
paper_authors._id: 65fc7c824d36be78e66ba92d
paper_authors.avatarUrl: /avatars/d4a55c820cae533f91724e062427516a.svg
paper_authors.fullname: Linjiang Huang
paper_authors.type: user
paper_authors.user: LjHuang
paper_authors._id: 65fa4422becc987834315f71
paper_authors.name: Rongyao Fang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-21T13:08:52.385Z
paper_authors._id: 65b8724123d948d884b379b1
paper_authors.avatarUrl: /avatars/ce189d1d8d688c17912f9b869035b2d0.svg
paper_authors.fullname: Rongyao Fang
paper_authors.type: user
paper_authors.user: LucasFang
paper_authors._id: 65fa4422becc987834315f72
paper_authors.name: Aiping Zhang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-26T08:26:24.916Z
paper_authors._id: 66f4cf499af27c74e9ac6201
paper_authors.avatarUrl: /avatars/745be730ac988d4137bed0c2f51e2066.svg
paper_authors.fullname: zhangaip
paper_authors.type: user
paper_authors.user: zhangap
paper_authors._id: 65fa4422becc987834315f73
paper_authors.name: Guanglu Song
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-21T13:09:34.955Z
paper_authors._id: 636a2efffbc31ee68fee5c1c
paper_authors.avatarUrl: /avatars/21e119f23e1e6fe926567409e74d1a89.svg
paper_authors.fullname: Song
paper_authors.type: user
paper_authors.user: Guanglu
paper_authors._id: 65fa4422becc987834315f74
paper_authors.name: Si Liu
paper_authors._id: 65fa4422becc987834315f75
paper_authors.name: Yu Liu
paper_authors._id: 65fa4422becc987834315f76
paper_authors.name: Hongsheng Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-21T13:10:00.385Z
paper_authors._id: 65c04e9c27a5fdca81abcbd9
paper_authors.avatarUrl: /avatars/12a155683c824fa23da4a9e2bed4f64e.svg
paper_authors.fullname: Hongsheng LI
paper_authors.type: user
paper_authors.user: hsli-cuhk
paper_title: FouriScale: A Frequency Perspective on Training-Free High-Resolution
    Image Synthesis
paper_summary: In this study, we delve into the generation of high-resolution images from
  pre-trained diffusion models, addressing persistent challenges, such as
  repetitive patterns and structural distortions, that emerge when models are
  applied beyond their trained resolutions. To address this issue, we introduce
  an innovative, training-free approach FouriScale from the perspective of
  frequency domain analysis. We replace the original convolutional layers in
  pre-trained diffusion models by incorporating a dilation technique along with a
  low-pass operation, intending to achieve structural consistency and scale
  consistency across resolutions, respectively. Further enhanced by a
  padding-then-crop strategy, our method can flexibly handle text-to-image
  generation of various aspect ratios. By using the FouriScale as guidance, our
  method successfully balances the structural integrity and fidelity of generated
  images, achieving an astonishing capacity of arbitrary-size, high-resolution,
  and high-quality generation. With its simplicity and compatibility, our method
  can provide valuable insights for future explorations into the synthesis of
  ultra-high-resolution images. The code will be released at
  https://github.com/LeonHLJ/FouriScale.
paper_ai_summary: FouriScale, a frequency domain technique, improves high-resolution image generation from pre-trained diffusion models by enhancing structural and scale consistency across different resolutions.
paper_ai_keywords: diffusion models frequency domain convolutional layers dilation low-pass operation padding-then-crop strategy text-to-image generation structural integrity fidelity