Semantic Steering via Hyperbolic Geometry

As modern text-to-image models approach photorealism, reliable semantic steering becomes essential to guardrail generation. Steering methods operate through linear manipulations in Euclidean spaces, which often provide limited stability and locality under strong interventions. We introduce HypSteer, a steering framework operating in hyperbolic space that controls semantic attributes through geometry-aware directions in a structured representation space. Using a state-of-the-art hyperbolic vision–language encoder, HypSteer defines semantic steering directions from positive and negative prompt sets and applies them to arbitrary inputs via parallel transport, yielding geodesic edits that follow the semantic organization of the space. A lightweight adapter connects these hyperbolic representations to frozen off-the-shelf T2I backbones. Across four safety benchmarks and four generative backbones, HypSteer achieves state-of-the-art safety results, retaining image quality, showing that hyperbolic steering is a practical and flexible alternative.