title: DreamCache: Finetuning-Free Lightweight Personalized Image Generation
    via Feature Caching
summary: Personalized image generation requires text-to-image generative models that
  capture the core features of a reference subject to allow for controlled
  generation across different contexts. Existing methods face challenges due to
  complex training requirements, high inference costs, limited flexibility, or a
  combination of these issues. In this paper, we introduce DreamCache, a scalable
  approach for efficient and high-quality personalized image generation. By
  caching a small number of reference image features from a subset of layers and
  a single timestep of the pretrained diffusion denoiser, DreamCache enables
  dynamic modulation of the generated image features through lightweight, trained
  conditioning adapters. DreamCache achieves state-of-the-art image and text
  alignment, utilizing an order of magnitude fewer extra parameters, and is both
  more computationally effective and versatile than existing models.
paper_authors._id: 6748216e80aebd7436f21d92
paper_authors.name: Emanuele Aiello
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-28T11:32:51.961Z
paper_authors._id: 6515ac7f60757b8c8f6b8d31
paper_authors.avatarUrl: /avatars/ed033f30f9f78ed68da976e6d01af0ea.svg
paper_authors.fullname: Emanuele Aiello
paper_authors.type: user
paper_authors.user: Ema97x
paper_authors._id: 6748216e80aebd7436f21d93
paper_authors.name: Umberto Michieli
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-28T11:32:31.539Z
paper_authors._id: 646f1879c261dc4133830dd6
paper_authors.avatarUrl: /avatars/f4134a8fb171f93c098a43f1c7a94eb6.svg
paper_authors.fullname: Umberto Michieli
paper_authors.type: user
paper_authors.user: u-michieli
paper_authors._id: 6748216e80aebd7436f21d94
paper_authors.name: Diego Valsesia
paper_authors._id: 6748216e80aebd7436f21d95
paper_authors.name: Mete Ozay
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-28T11:32:12.229Z
paper_authors._id: 65bced24c63d6a8d7f400b8b
paper_authors.avatarUrl: /avatars/91212d7f39eba91158d7a0d0c823bc45.svg
paper_authors.fullname: Mete Ozay
paper_authors.type: user
paper_authors.user: mosams
paper_authors._id: 6748216e80aebd7436f21d96
paper_authors.name: Enrico Magli
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-28T11:32:06.161Z
paper_authors._id: 65e58cd65df8e81bed738067
paper_authors.avatarUrl: /avatars/dd6eb8f9d2e0ae2c43727b9fe884f6e1.svg
paper_authors.fullname: Enrico Magli
paper_authors.type: user
paper_authors.user: emagli
paper_title: DreamCache: Finetuning-Free Lightweight Personalized Image Generation
    via Feature Caching
paper_summary: Personalized image generation requires text-to-image generative models that
  capture the core features of a reference subject to allow for controlled
  generation across different contexts. Existing methods face challenges due to
  complex training requirements, high inference costs, limited flexibility, or a
  combination of these issues. In this paper, we introduce DreamCache, a scalable
  approach for efficient and high-quality personalized image generation. By
  caching a small number of reference image features from a subset of layers and
  a single timestep of the pretrained diffusion denoiser, DreamCache enables
  dynamic modulation of the generated image features through lightweight, trained
  conditioning adapters. DreamCache achieves state-of-the-art image and text
  alignment, utilizing an order of magnitude fewer extra parameters, and is both
  more computationally effective and versatile than existing models.
paper_ai_keywords: DreamCache diffusion denoiser conditioning adapters text-to-image generative models image and text alignment