Existing unlearning algorithms in text-to-image generative models often fail to preserve knowledge of semantically related concepts when removing specific target concepts—a challenge known as \textit{adjacency}. To address this, we propose \textbf{FADE} (\textit{Fine-grained Attenuation for Diffusion Erasure}), introducing adjacency-aware unlearning in diffusion models. FADE comprises two components: (1) the \textbf{Concept Lattice}, which identifies an adjacency set of related concepts, and (2) \textbf{Mesh Modules}, employing a structured combination of Expungement, Adjacency, and Guidance loss components. These enable precise erasure of target concepts while preserving fidelity across related and unrelated concepts. Evaluated on datasets like Stanford Dogs, Oxford Flowers, CUB, I2P, Imagenette, and ImageNet-1k, FADE effectively removes target concepts with minimal impact on correlated concepts, achieving at least a \textbf{12\% improvement in retention performance} over state-of-the-art methods.