Semantic Steering for Controllable Generation: Tuning-Free Concept Erasure in Multimodal Diffusion Transformers
This work proposes to erase concepts by directly manipulating the model's internal representations by operating exclusively on the sparse text-branch tokens and leveraging the straight sampling trajectory of rectified flow, achieving effective concept erasure with negligible overhead and without any training.