SliderSpace: Decomposing the Visual Capabilities of Diffusion Models
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
67a20a822cf1b98052d941d1
Rohit Gandikota
claimed_verified
2025-02-04T17:11:21.938Z
636daf1b56c0762cfda074b5
/avatars/f44be5eb110acfa2efbd09de6b416239.svg
Rohit Gandikota
user
RohitGandikota
67a20a822cf1b98052d941d2
Zongze Wu
admin_assigned
2025-02-04T17:38:03.547Z
62f923adebd15ad7b5b22141
/avatars/3454aa0bbbe4f119c551f7e9b522afa8.svg
Zongze Wu
user
ZongzeWu
67a20a822cf1b98052d941d3
Richard Zhang
67a20a822cf1b98052d941d4
David Bau
admin_assigned
2025-02-04T17:38:13.109Z
6214d6c01e35c843d42d1f77
/avatars/ac208cd180b4f3ed1ec367e581facfcf.svg
David Bau
user
davidbau
67a20a822cf1b98052d941d5
Eli Shechtman
67a20a822cf1b98052d941d6
Nick Kolkin
SliderSpace: Decomposing the Visual Capabilities of Diffusion Models
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
SliderSpace is a framework that automatically finds multiple interpretable directions in diffusion models from a single text prompt, enabling better control and discovery of model capabilities across various applications.
diffusion models SliderSpace controllable directions text prompt low-rank adaptor latent space concept decomposition artistic style exploration diversity enhancement quantitative evaluation user studies