Large-scale diffusion models, known for their impressive image generation
capabilities, have raised concerns among researchers regarding social impacts,
such as the imitation of copyrighted artistic styles. In response, existing
approaches turn to machine unlearning techniques to eliminate unsafe concepts
from pre-trained models. However, these methods compromise the generative
performance and neglect the coupling among multi-concept erasures, as well as
the concept restoration problem. To address these issues, we propose a
Separable Multi-concept Eraser (SepME), which mainly includes two parts: the
generation of concept-irrelevant representations and the weight decoupling. The
former aims to avoid unlearning substantial information that is irrelevant to
forgotten concepts. The latter separates optimizable model weights, making each
weight increment correspond to a specific concept erasure without affecting
generative performance on other concepts. Specifically, the weight increment
for erasing a specified concept is formulated as a linear combination of
solutions calculated based on other known undesirable concepts. Extensive
experiments indicate the efficacy of our approach in eliminating concepts,
preserving model performance, and offering flexibility in the erasure or
recovery of various concepts.

我们提出了一种可分离的多概念擦除（SepME）方法，包括概念无关表示的生成和权重解耦，用于解决大规模扩散模型中的版权问题和概念恢复问题。通过其他已知不良概念计算的解决方案的线性组合，我们的方法在去除概念、保留模型性能以及灵活擦除或恢复各种概念方面具有良好的效果。