title: ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models
summary: Recent advancement in text-to-image models (e.g., Stable Diffusion) and
  corresponding personalized technologies (e.g., DreamBooth and LoRA) enables
  individuals to generate high-quality and imaginative images. However, they
  often suffer from limitations when generating images with resolutions outside
  of their trained domain. To overcome this limitation, we present the Resolution
  Adapter (ResAdapter), a domain-consistent adapter designed for diffusion models
  to generate images with unrestricted resolutions and aspect ratios. Unlike
  other multi-resolution generation methods that process images of static
  resolution with complex post-process operations, ResAdapter directly generates
  images with the dynamical resolution. Especially, after learning a deep
  understanding of pure resolution priors, ResAdapter trained on the general
  dataset, generates resolution-free images with personalized diffusion models
  while preserving their original style domain. Comprehensive experiments
  demonstrate that ResAdapter with only 0.5M can process images with flexible
  resolutions for arbitrary diffusion models. More extended experiments
  demonstrate that ResAdapter is compatible with other modules (e.g., ControlNet,
  IP-Adapter and LCM-LoRA) for image generation across a broad range of
  resolutions, and can be integrated into other multi-resolution model (e.g.,
  ElasticDiffusion) for efficiently generating higher-resolution images. Project
  link is https://res-adapter.github.io
paper_authors._id: 65e73b6bfc737133d7e13494
paper_authors.name: Jiaxiang Cheng
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-05T20:43:44.089Z
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paper_authors.fullname: Jiaxiang Cheng
paper_authors.type: user
paper_authors.user: jiaxiangc
paper_authors._id: 65e73b6bfc737133d7e13495
paper_authors.name: Pan Xie
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/b4a7651de5dfd8a1c26e9ca559886b26.svg
paper_authors.fullname: Xie Pan
paper_authors.type: user
paper_authors.user: panxie
paper_authors._id: 65e73b6bfc737133d7e13496
paper_authors.name: Xin Xia
paper_authors.status: claimed_verified
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paper_authors.fullname: XinXia
paper_authors.type: user
paper_authors.user: XiaXin-Aloys
paper_authors._id: 65e73b6bfc737133d7e13497
paper_authors.name: Jiashi Li
paper_authors.status: admin_assigned
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paper_authors._id: 64fca5f28d50404bc42ca78a
paper_authors.avatarUrl: /avatars/ae01ac0296d6ce1277dacb6894f570b8.svg
paper_authors.fullname: Jiashi Li
paper_authors.type: user
paper_authors.user: Beginlner
paper_authors._id: 65e73b6bfc737133d7e13498
paper_authors.name: Jie Wu
paper_authors._id: 65e73b6bfc737133d7e13499
paper_authors.name: Yuxi Ren
paper_authors._id: 65e73b6bfc737133d7e1349a
paper_authors.name: Huixia Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-03-21T08:29:09.326Z
paper_authors._id: 6368b457e943416fc80619fb
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paper_authors.fullname: HXLee
paper_authors.type: user
paper_authors.user: HXLee
paper_authors._id: 65e73b6bfc737133d7e1349b
paper_authors.name: Xuefeng Xiao
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-03-05T20:46:00.809Z
paper_authors._id: 646b7f71df2609a541c1ab9f
paper_authors.avatarUrl: /avatars/48b82e5fd9b06f41ff825507c36816cd.svg
paper_authors.fullname: Xuefeng Xiao
paper_authors.type: user
paper_authors.user: xiaoxuefeng
paper_authors._id: 65e73b6bfc737133d7e1349c
paper_authors.name: Min Zheng
paper_authors._id: 65e73b6bfc737133d7e1349d
paper_authors.name: Lean Fu
paper_title: ResAdapter: Domain Consistent Resolution Adapter for Diffusion Models
paper_summary: Recent advancement in text-to-image models (e.g., Stable Diffusion) and
  corresponding personalized technologies (e.g., DreamBooth and LoRA) enables
  individuals to generate high-quality and imaginative images. However, they
  often suffer from limitations when generating images with resolutions outside
  of their trained domain. To overcome this limitation, we present the Resolution
  Adapter (ResAdapter), a domain-consistent adapter designed for diffusion models
  to generate images with unrestricted resolutions and aspect ratios. Unlike
  other multi-resolution generation methods that process images of static
  resolution with complex post-process operations, ResAdapter directly generates
  images with the dynamical resolution. Especially, after learning a deep
  understanding of pure resolution priors, ResAdapter trained on the general
  dataset, generates resolution-free images with personalized diffusion models
  while preserving their original style domain. Comprehensive experiments
  demonstrate that ResAdapter with only 0.5M can process images with flexible
  resolutions for arbitrary diffusion models. More extended experiments
  demonstrate that ResAdapter is compatible with other modules (e.g., ControlNet,
  IP-Adapter and LCM-LoRA) for image generation across a broad range of
  resolutions, and can be integrated into other multi-resolution model (e.g.,
  ElasticDiffusion) for efficiently generating higher-resolution images. Project
  link is https://res-adapter.github.io
paper_ai_summary: The Resolution Adapter (ResAdapter) enhances diffusion models to generate images in arbitrary resolutions and aspect ratios without extensive post-processing, preserving style consistency.
paper_ai_keywords: diffusion models Resolution Adapter ResAdapter resolution priors ControlNet IP-Adapter LCM-LoRA ElasticDiffusion