| dc.description.abstract |
Diffusion models have achieved remarkable success in generative tasks across various domains. However, the increasing demand for content moderation and the removal of specific concepts from these models has introduced the challenge of \textit{unlearning}. In this work, we present a suite of robust methodologies that significantly enhance the unlearning process by employing advanced loss functions within knowledge distillation frameworks. Specifically, we utilize the Cramer-Wold distance and Jensen-Shannon (JS) divergence to facilitate more efficient and versatile concept removal. Although current non-learning techniques are effective in certain scenarios, they are typically limited to specific categories such as identity, nudity, or artistic style. In contrast, our proposed methods demonstrate robust versatility, seamlessly adapting to and performing effectively across a wide range of concept erasure categories. Our approach outperforms existing techniques, achieving consistent results across different unlearning categories and showcasing its broad applicability. Through extensive experiments, we show that our method not only surpasses previous benchmarks but also addresses key limitations of current unlearning techniques, paving the way for more responsible use of text-to-image diffusion models. |
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