Concept erasure, which fine-tunes diffusion models to remove undesired or harmful visual concepts, has become a mainstream approach to mitigating unsafe or illegal image generation in text-to-image models.However, existing removal methods typically adopt a unidirectional erasure strategy by either suppressing the target concept or reinforcing safe alternatives, making it difficult to achieve a balanced trade-off between concept removal and generation quality. To address this limitation, we propose a novel Bidirectional Image-Guided Concept Erasure (Bi-Erasing) framework that performs concept suppression and safety enhancement simultaneously. Specifically, based on the joint representation of text prompts and corresponding images, Bi-Erasing introduces two decoupled image branches: a negative branch responsible for suppressing harmful semantics and a positive branch providing visual guidance for safe alternatives. By jointly optimizing these complementary directions, our approach achieves a balance between erasure efficacy and generation usability. In addition, we apply mask-based filtering to the image branches to prevent interference from irrelevant content during the erasure process. Across extensive experiment evaluations, the proposed Bi-Erasing outperforms baseline methods in balancing concept removal effectiveness and visual fidelity.

翻译：概念擦除通过微调扩散模型以移除不良或有害的视觉概念，已成为缓解文本到图像模型中不安全或非法图像生成的主流方法。然而，现有移除方法通常采用单向擦除策略，即抑制目标概念或强化安全替代方案，难以在概念移除与生成质量之间实现平衡权衡。为克服这一局限，本文提出一种新颖的双向图像引导概念擦除框架，可同时执行概念抑制与安全增强。具体而言，基于文本提示与对应图像的联合表示，该框架引入两个解耦的图像分支：负责抑制有害语义的负向分支，以及为安全替代方案提供视觉引导的正向分支。通过联合优化这些互补方向，本方法在擦除效能与生成可用性之间达到平衡。此外，我们在图像分支中应用基于掩码的过滤机制，以防止擦除过程中无关内容的干扰。大量实验评估表明，所提双向擦除框架在平衡概念移除效果与视觉保真度方面优于基线方法。