2 papers
cs.CV2026
PEAK: Precise and Persistent Concept Erasure via k-Sparse Autoencoders
Man Jiang, Ouxiang Li, Weibao Xue +4
Erasing concepts from large-scale text-to-image (T2I) diffusion models has become increasingly crucial due to the growing concerns over copyright infringement, privacy violations,…
cs.CV2026
BeCARE: Budgeted Cache Refresh for Diffusion Transformer Acceleration
Yuhang Zhang, Junxiang Qiu, Huixia Ben +4
Training-free feature caching accelerates diffusion transformer (DiT) inference by reusing or forecasting intermediate features. However, fixed schedules make compute predictable b…