385 citations · 603 across the 7 of their papers we have counts for
11 papers · 1 filter
SliderSpace: Decomposing the Visual Capabilities of Diffusion Models
Rohit Gandikota, Zongze Wu, Richard Zhang +3
We present SliderSpace, a framework for automatically decomposing the visual capabilities of diffusion models into controllable and human-understandable directions. Unlike existing…
Opt-In Art: Learning Art Styles Only from Few Examples
Hui Ren, Joanna Materzynska, Rohit Gandikota +2
We explore whether pre-training on datasets with paintings is necessary for a model to learn an artistic style with only a few examples. To investigate this, we train a text-to-ima…
Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models
Rohit Gandikota, Joanna Materzynska, Tingrui Zhou +2
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-ra…
Toward a Visual Concept Vocabulary for GAN Latent Space
Sarah Schwettmann, Evan Hernandez, David Bau +3
A large body of recent work has identified transformations in the latent spaces of generative adversarial networks (GANs) that consistently and interpretably transform generated im…
Sketch Your Own GAN
Sheng-Yu Wang, David Bau, Jun-Yan Zhu
Can a user create a deep generative model by sketching a single example? Traditionally, creating a GAN model has required the collection of a large-scale dataset of exemplars and s…
Understanding the Role of Individual Units in a Deep Neural Network
David Bau, Jun-Yan Zhu, Hendrik Strobelt +3
Deep neural networks excel at finding hierarchical representations that solve complex tasks over large data sets. How can we humans understand these learned representations? In thi…