4 papers · 1 filter
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…
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…
Customizing Text-to-Image Models with a Single Image Pair
Maxwell Jones, Sheng-Yu Wang, Nupur Kumari +2
Art reinterpretation is the practice of creating a variation of a reference work, making a paired artwork that exhibits a distinct artistic style. We ask if such an image pair can…
Unified Concept Editing in Diffusion Models
Rohit Gandikota, Hadas Orgad, Yonatan Belinkov +2
Text-to-image models suffer from various safety issues that may limit their suitability for deployment. Previous methods have separately addressed individual issues of bias, copyri…