title: FreSca: Unveiling the Scaling Space in Diffusion Models
summary: Diffusion models offer impressive controllability for image tasks, primarily
  through noise predictions that encode task-specific information and
  classifier-free guidance enabling adjustable scaling. This scaling mechanism
  implicitly defines a ``scaling space'' whose potential for fine-grained
  semantic manipulation remains underexplored. We investigate this space,
  starting with inversion-based editing where the difference between
  conditional/unconditional noise predictions carries key semantic information.
  Our core contribution stems from a Fourier analysis of noise predictions,
  revealing that its low- and high-frequency components evolve differently
  throughout diffusion. Based on this insight, we introduce FreSca, a
  straightforward method that applies guidance scaling independently to different
  frequency bands in the Fourier domain. FreSca demonstrably enhances existing
  image editing methods without retraining. Excitingly, its effectiveness extends
  to image understanding tasks such as depth estimation, yielding quantitative
  gains across multiple datasets.
paper_authors._id: 67efe4bde065b7e7675be166
paper_authors.name: Chao Huang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-21T09:05:18.753Z
paper_authors._id: 67257ee0938e718957c9c100
paper_authors.avatarUrl: /avatars/db1f792ee1a5d860ea4e98c11a016a1b.svg
paper_authors.fullname: Chao Huang
paper_authors.type: user
paper_authors.user: ChaoHuangCS
paper_authors._id: 67efe4bde065b7e7675be167
paper_authors.name: Susan Liang
paper_authors._id: 67efe4bde065b7e7675be168
paper_authors.name: Yunlong Tang
paper_authors._id: 67efe4bde065b7e7675be169
paper_authors.name: Li Ma
paper_authors._id: 67efe4bde065b7e7675be16a
paper_authors.name: Yapeng Tian
paper_authors._id: 67efe4bde065b7e7675be16b
paper_authors.name: Chenliang Xu
paper_title: FreSca: Unveiling the Scaling Space in Diffusion Models
paper_summary: Diffusion models offer impressive controllability for image tasks, primarily
  through noise predictions that encode task-specific information and
  classifier-free guidance enabling adjustable scaling. This scaling mechanism
  implicitly defines a ``scaling space'' whose potential for fine-grained
  semantic manipulation remains underexplored. We investigate this space,
  starting with inversion-based editing where the difference between
  conditional/unconditional noise predictions carries key semantic information.
  Our core contribution stems from a Fourier analysis of noise predictions,
  revealing that its low- and high-frequency components evolve differently
  throughout diffusion. Based on this insight, we introduce FreSca, a
  straightforward method that applies guidance scaling independently to different
  frequency bands in the Fourier domain. FreSca demonstrably enhances existing
  image editing methods without retraining. Excitingly, its effectiveness extends
  to image understanding tasks such as depth estimation, yielding quantitative
  gains across multiple datasets.
paper_ai_summary: FreSca enhances image editing and understanding tasks by applying independent guidance scaling to different frequency bands in diffusion models.
paper_ai_keywords: diffusion models noise predictions scaling space inversion-based editing Fourier analysis frequency bands FreSca image editing image understanding depth estimation quantitative gains