Fine-tuning diffusion models through personalized datasets is an acknowledged method for improving generation quality across downstream tasks, which, however, often inadvertently generates unintended concepts such as watermarks and QR codes, attributed to the limitations in image sources and collecting methods within specific downstream tasks. Existing solutions suffer from eliminating these unintentionally learned implicit concepts, primarily due to the dependency on the model's ability to recognize concepts that it actually cannot discern. In this work, we introduce \methodname, a novel approach that successfully removes the implicit concepts with either an additional accessible classifier or detector model to encode geometric information of these concepts into text domain. Moreover, we propose \textit{Implicit Concept}, a novel image-text dataset imbued with three implicit concepts (\ie, watermarks, QR codes, and text) for training and evaluation. Experimental results demonstrate that \methodname not only identifies but also proficiently eradicates implicit concepts, revealing a significant improvement over the existing methods. The integration of geometric information marks a substantial progression in the precise removal of implicit concepts in diffusion models.

翻译：通过个性化数据集微调扩散模型是提升下游任务生成质量的公认方法，然而由于特定下游任务中图像来源和采集方式的局限性，该方法常会无意生成水印、二维码等非目标概念。现有解决方案难以消除这些无意习得的隐性概念，主要原因是这类方法依赖于模型自身对无法辨识概念的识别能力。本文提出\textbf{\methodname}这一创新方法，通过引入额外可分类器或检测器编码概念几何信息并将其映射至文本域，成功实现隐性概念去除。此外，我们构建了包含水印、二维码和文字三种隐性概念的\textit{Implicit Concept}新型图文数据集用于训练与评估。实验结果表明，\methodname不仅能识别隐性概念，还可高效将其消除，显著优于现有方法。几何信息的整合标志着扩散模型隐性概念精准去除领域的重要进展。