Attention mechanisms have been found effective for person re-identification (Re-ID). However, the learned “attentive” features are often not naturally uncorrelated or “diverse”, which compromises the retrieval performance based on the Euclidean distance. We advocate the com- plementary powers of attention and diversity for Re-ID, by proposing an Attentive but Diverse Network (ABD-Net). ABD-Net seamlessly integrates attention modules and diversity regularizations throughout the entire network to learn features that are representative, robust, and more dis- criminative. Specifically, we introduce a pair of comple- mentary attention modules, focusing on channel aggregation and position awareness, respectively. Then, we plug in a novel orthogonality constraint that efficiently enforces di- versity on both hidden activations and weights. Through an extensive set of ablation study, we verify that the attentive and diverse terms each contributes to the perfor- mance boosts of ABD-Net. It consistently outperforms exist- ing state-of-the-art methods on there popular person Re-ID benchmarks.