title: SliderSpace: Decomposing the Visual Capabilities of Diffusion Models
summary: We present SliderSpace, a framework for automatically decomposing the visual
  capabilities of diffusion models into controllable and human-understandable
  directions. Unlike existing control methods that require a user to specify
  attributes for each edit direction individually, SliderSpace discovers multiple
  interpretable and diverse directions simultaneously from a single text prompt.
  Each direction is trained as a low-rank adaptor, enabling compositional control
  and the discovery of surprising possibilities in the model's latent space.
  Through extensive experiments on state-of-the-art diffusion models, we
  demonstrate SliderSpace's effectiveness across three applications: concept
  decomposition, artistic style exploration, and diversity enhancement. Our
  quantitative evaluation shows that SliderSpace-discovered directions decompose
  the visual structure of model's knowledge effectively, offering insights into
  the latent capabilities encoded within diffusion models. User studies further
  validate that our method produces more diverse and useful variations compared
  to baselines. Our code, data and trained weights are available at
  https://sliderspace.baulab.info
paper_authors._id: 67a20a822cf1b98052d941d1
paper_authors.name: Rohit Gandikota
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-02-04T17:11:21.938Z
paper_authors._id: 636daf1b56c0762cfda074b5
paper_authors.avatarUrl: /avatars/f44be5eb110acfa2efbd09de6b416239.svg
paper_authors.fullname: Rohit Gandikota
paper_authors.type: user
paper_authors.user: RohitGandikota
paper_authors._id: 67a20a822cf1b98052d941d2
paper_authors.name: Zongze Wu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-04T17:38:03.547Z
paper_authors._id: 62f923adebd15ad7b5b22141
paper_authors.avatarUrl: /avatars/3454aa0bbbe4f119c551f7e9b522afa8.svg
paper_authors.fullname: Zongze Wu
paper_authors.type: user
paper_authors.user: ZongzeWu
paper_authors._id: 67a20a822cf1b98052d941d3
paper_authors.name: Richard Zhang
paper_authors._id: 67a20a822cf1b98052d941d4
paper_authors.name: David Bau
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-02-04T17:38:13.109Z
paper_authors._id: 6214d6c01e35c843d42d1f77
paper_authors.avatarUrl: /avatars/ac208cd180b4f3ed1ec367e581facfcf.svg
paper_authors.fullname: David Bau
paper_authors.type: user
paper_authors.user: davidbau
paper_authors._id: 67a20a822cf1b98052d941d5
paper_authors.name: Eli Shechtman
paper_authors._id: 67a20a822cf1b98052d941d6
paper_authors.name: Nick Kolkin
paper_title: SliderSpace: Decomposing the Visual Capabilities of Diffusion Models
paper_summary: We present SliderSpace, a framework for automatically decomposing the visual
  capabilities of diffusion models into controllable and human-understandable
  directions. Unlike existing control methods that require a user to specify
  attributes for each edit direction individually, SliderSpace discovers multiple
  interpretable and diverse directions simultaneously from a single text prompt.
  Each direction is trained as a low-rank adaptor, enabling compositional control
  and the discovery of surprising possibilities in the model's latent space.
  Through extensive experiments on state-of-the-art diffusion models, we
  demonstrate SliderSpace's effectiveness across three applications: concept
  decomposition, artistic style exploration, and diversity enhancement. Our
  quantitative evaluation shows that SliderSpace-discovered directions decompose
  the visual structure of model's knowledge effectively, offering insights into
  the latent capabilities encoded within diffusion models. User studies further
  validate that our method produces more diverse and useful variations compared
  to baselines. Our code, data and trained weights are available at
  https://sliderspace.baulab.info
paper_ai_keywords: diffusion models controllable human-understandable directions text prompt low-rank adaptor compositional control latent space concept decomposition artistic style exploration diversity enhancement visual structure latent capabilities