SliderSpace: Decomposing the Visual Capabilities of Diffusion Models
Authors: name: Rohit Gandikota, name: Zongze Wu, name: Richard Zhang, name: David Bau, name: Eli Shechtman, name: Nick Kolkin

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