BEM: Learning Bregman Divergences for Concept Erasure in Flow Matching Models
Concept erasure removes harmful, private, or copyrighted concepts from text-to-image models while preserving their other abilities. Leading models such as FLUX and Stable Diffusion 3 are trained with flow matching, and erasure methods for them fine-tune the model toward a guided teacher's velocities under a Euclidean loss. Yet the squared distance is only one member of a larger family: Bregman divergences are exactly the losses under which flow matching recovers the correct velocity field, and each member shapes differently what is erased and what is preserved. We introduce BEM (Bregman erasure matching), which is, to our knowledge, the first to learn this divergence through bi-level opti...
| Authors | Mohamed-Hicham Leghettas, Markus Püschel |
|---|---|
| Primary category | cs.LG |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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