Generative models have demonstrated remarkable potential in generating
visually impressive content from textual descriptions. However, training these
models on unfiltered internet data poses the risk of learning and subsequently
propagating undesirable concepts, such as copyrighted or unethical content. In
this paper, we propose a novel method to remove undesirable concepts from
text-to-image generative models by incorporating a learnable prompt into the
cross-attention module. This learnable prompt acts as additional memory to
transfer the knowledge of undesirable concepts into it and reduce the
dependency of these concepts on the model parameters and corresponding textual
inputs. Because of this knowledge transfer into the prompt, erasing these
undesirable concepts is more stable and has minimal negative impact on other
concepts. We demonstrate the effectiveness of our method on the Stable
Diffusion model, showcasing its superiority over state-of-the-art erasure
methods in terms of removing undesirable content while preserving other
unrelated elements.

我们提出了一种新的方法，通过将可学习提示引入交叉注意力模块，从文本到图像生成模型中去除不良概念。这种可学习提示作为额外的记忆来传递不良概念的知识，并减少这些概念对模型参数和对应文本输入的依赖。通过这种知识传递，擦除这些不良概念更稳定，并对其他概念的负面影响较小。我们在稳定扩散模型上展示了我们方法的有效性，展示了它在去除不良内容同时保留其他无关元素方面的优越性。