Steer and Pace: Joint Safety Guidance for Text-to-Image Generation

Text-to-image (T2I) generative models produce high-quality images from free-form prompts, but can also generate unsafe content. Existing sampling-time safeguards often aggregate multiple unsafe categories into one condition or handle them one at a time, limiting their ability to control the contributions of different categories jointly. A further challenge is determining how to allocate correction strength across sampling stages that play different roles in image formation. To address these challenges, we propose SafeConductor, a training-free method that combines joint correction with phase-aware allocation for safe text-to-image generation. At each iteration, SafeConductor formulates joint correction as minimizing changes to the current guidance under separate non-positive projection constraints for all categories. The resulting high-dimensional problem reduces to a nonnegative quadratic program with one coefficient per category, accounting for interactions among unsafe directions. We approximately solve this problem using cyclic coordinate descent. The correction is then applied with a smooth sinusoidal allocation that emphasizes the intermediate stage and reduces intervention near the trajectory endpoints, aiming to preserve image composition and visual details. Extensive experiments across four T2I safety benchmarks demonstrate that SafeConductor achieves state-of-the-art harmful content mitigation on both SD1.5 and FLUX.2 while maintaining competitive prompt alignment and generation quality. Our code is available in the supplementary material.