title: Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models
summary: We present a method to create interpretable concept sliders that enable
  precise control over attributes in image generations from diffusion models. Our
  approach identifies a low-rank parameter direction corresponding to one concept
  while minimizing interference with other attributes. A slider is created using
  a small set of prompts or sample images; thus slider directions can be created
  for either textual or visual concepts. Concept Sliders are plug-and-play: they
  can be composed efficiently and continuously modulated, enabling precise
  control over image generation. In quantitative experiments comparing to
  previous editing techniques, our sliders exhibit stronger targeted edits with
  lower interference. We showcase sliders for weather, age, styles, and
  expressions, as well as slider compositions. We show how sliders can transfer
  latents from StyleGAN for intuitive editing of visual concepts for which
  textual description is difficult. We also find that our method can help address
  persistent quality issues in Stable Diffusion XL including repair of object
  deformations and fixing distorted hands. Our code, data, and trained sliders
  are available at https://sliders.baulab.info/
paper_authors._id: 655d7ef3d00cca1e3abf4abe
paper_authors.name: Rohit Gandikota
paper_authors.status: admin_assigned
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paper_authors.fullname: Rohit Gandikota
paper_authors.type: user
paper_authors.user: RohitGandikota
paper_authors._id: 655d7ef3d00cca1e3abf4abf
paper_authors.name: Joanna Materzynska
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-22T12:46:41.855Z
paper_authors._id: 60ef074b89938e400b470da8
paper_authors.avatarUrl: /avatars/c914070df46fb2dce9067d0eafc88238.svg
paper_authors.fullname: Joanna Materzynska
paper_authors.type: user
paper_authors.user: jomat
paper_authors._id: 655d7ef3d00cca1e3abf4ac0
paper_authors.name: Tingrui Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-11-29T14:55:08.555Z
paper_authors._id: 6566f1b177d8a948ac929ff3
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paper_authors.fullname: Tingrui Zhou
paper_authors.type: user
paper_authors.user: teshu0
paper_authors._id: 655d7ef3d00cca1e3abf4ac1
paper_authors.name: Antonio Torralba
paper_authors._id: 655d7ef3d00cca1e3abf4ac2
paper_authors.name: David Bau
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-11-22T12:47:33.229Z
paper_authors._id: 6214d6c01e35c843d42d1f77
paper_authors.avatarUrl: /avatars/ac208cd180b4f3ed1ec367e581facfcf.svg
paper_authors.fullname: David Bau
paper_authors.type: user
paper_authors.user: davidbau
paper_title: Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models
paper_summary: We present a method to create interpretable concept sliders that enable
  precise control over attributes in image generations from diffusion models. Our
  approach identifies a low-rank parameter direction corresponding to one concept
  while minimizing interference with other attributes. A slider is created using
  a small set of prompts or sample images; thus slider directions can be created
  for either textual or visual concepts. Concept Sliders are plug-and-play: they
  can be composed efficiently and continuously modulated, enabling precise
  control over image generation. In quantitative experiments comparing to
  previous editing techniques, our sliders exhibit stronger targeted edits with
  lower interference. We showcase sliders for weather, age, styles, and
  expressions, as well as slider compositions. We show how sliders can transfer
  latents from StyleGAN for intuitive editing of visual concepts for which
  textual description is difficult. We also find that our method can help address
  persistent quality issues in Stable Diffusion XL including repair of object
  deformations and fixing distorted hands. Our code, data, and trained sliders
  are available at https://sliders.baulab.info/
paper_ai_keywords: diffusion models concept sliders low-rank parameter direction prompts sample images textual concepts visual concepts slider compositions latents StyleGAN Stable Diffusion XL object deformations distorted hands