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 Geom-Erasing, 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 Implicit Concept, a novel image-text dataset imbued with three implicit concepts (i.e., watermarks, QR codes, and text) for training and evaluation. Experimental results demonstrate that Geom-Erasing 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：基于个性化数据集对扩散模型进行微调已被公认为提升下游任务生成质量的有效方法，然而这一过程常因特定下游任务中图像来源与采集方法的限制，意外生成水印、二维码等非预期概念。现有方法难以消除这类非意图习得的隐式概念，根本原因在于其需要依赖模型对实际上无法辨识的概念进行识别。本文提出的Geom-Erasing方法通过引入额外可访问的分类器或检测器，将隐式概念的几何信息编码至文本域，成功实现此类概念的消除。此外，我们构建了包含水印、二维码和文本三种隐式概念的新型图文数据集Implicit Concept，用于模型训练与评估。实验结果表明，Geom-Erasing不仅能精准识别更可有效消除隐式概念，较现有方法展现出显著优势。几何信息的融合标志着扩散模型中隐式概念精确移除技术的重大进展。