AdaptiveSliders: User-aligned Semantic Slider-based Editing of Text-to-Image Model Output

Precise editing of text-to-image model outputs remains challenging. Slider-based editing is a recent approach wherein the image’s semantic attributes are manipulated via sliders. However, it has significant user-centric issues. First, slider variations are often inconsistent across the sliding range. Second, the optimal slider range is unpredictable, with default values often being too large or small depending on the prompt and attribute. Third, manipulating one attribute can unintentionally alter others due to the complex entanglement of latent spaces. We introduce AdaptiveSliders, a tool that addresses these challenges by adapting to the specific attributes and prompts, generating consistent slider variations and optimal bounds while minimizing unintended changes. AdaptiveSliders also suggests initial attributes and generates initial images more aligned with prompt semantics. Through three validation studies and one end-to-end user study, we demonstrate that AdaptiveSliders significantly improves user control and experience, enabling semantic slider-based editing aligned with user needs and expectations.