title: Scale-wise Distillation of Diffusion Models
summary: We present SwD, a scale-wise distillation framework for diffusion models
  (DMs), which effectively employs next-scale prediction ideas for
  diffusion-based few-step generators. In more detail, SwD is inspired by the
  recent insights relating diffusion processes to the implicit spectral
  autoregression. We suppose that DMs can initiate generation at lower data
  resolutions and gradually upscale the samples at each denoising step without
  loss in performance while significantly reducing computational costs. SwD
  naturally integrates this idea into existing diffusion distillation methods
  based on distribution matching. Also, we enrich the family of distribution
  matching approaches by introducing a novel patch loss enforcing finer-grained
  similarity to the target distribution. When applied to state-of-the-art
  text-to-image diffusion models, SwD approaches the inference times of two full
  resolution steps and significantly outperforms the counterparts under the same
  computation budget, as evidenced by automated metrics and human preference
  studies.
paper_authors._id: 67dd1227046f2c38458e9588
paper_authors.name: Nikita Starodubcev
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-24T08:53:21.020Z
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paper_authors.fullname: nikita
paper_authors.type: user
paper_authors.user: quickjkee
paper_authors._id: 67dd1227046f2c38458e9589
paper_authors.name: Denis Kuznedelev
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-21T12:59:13.018Z
paper_authors._id: 629cf0475a13ba8233dd18c9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1654452258405-noauth.jpeg
paper_authors.fullname: Denis Kuznedelev
paper_authors.type: user
paper_authors.user: SpiridonSunRotator
paper_authors._id: 67dd1227046f2c38458e958a
paper_authors.name: Artem Babenko
paper_authors._id: 67dd1227046f2c38458e958b
paper_authors.name: Dmitry Baranchuk
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-21T12:59:23.033Z
paper_authors._id: 62b6cc49752323892323bc04
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paper_authors.fullname: Dmitry Baranchuk
paper_authors.type: user
paper_authors.user: dbaranchuk
paper_title: Scale-wise Distillation of Diffusion Models
paper_summary: We present SwD, a scale-wise distillation framework for diffusion models
  (DMs), which effectively employs next-scale prediction ideas for
  diffusion-based few-step generators. In more detail, SwD is inspired by the
  recent insights relating diffusion processes to the implicit spectral
  autoregression. We suppose that DMs can initiate generation at lower data
  resolutions and gradually upscale the samples at each denoising step without
  loss in performance while significantly reducing computational costs. SwD
  naturally integrates this idea into existing diffusion distillation methods
  based on distribution matching. Also, we enrich the family of distribution
  matching approaches by introducing a novel patch loss enforcing finer-grained
  similarity to the target distribution. When applied to state-of-the-art
  text-to-image diffusion models, SwD approaches the inference times of two full
  resolution steps and significantly outperforms the counterparts under the same
  computation budget, as evidenced by automated metrics and human preference
  studies.
paper_ai_summary: A scale-wise distillation framework for diffusion models reduces computational costs and improves inference times by incorporating next-scale predictions and enhancing distribution matching methods.
paper_ai_keywords: diffusion models scale-wise distillation next-scale prediction implicit spectral autoregression denoising computational costs distribution matching patch loss inference times text-to-image diffusion models