Traditional vision search follows the multi-stage cascading architecture (MCA) paradigm to balance efficiency and conversion, yet optimization objectives collide across stages, making it difficult to achieve Pareto optimality. Meanwhile, pronounced multi-view discrepancies of the same item undermine retrieval accuracy, as embedding-similarity methods fail to bridge large appearance variations. In this paper, we propose OneVision, an end-to-end generative framework that addresses both challenges. OneVision introduces VRQ, a vision-aligned residual quantization method that assigns consistent shallow-level codes to visually similar images of the same item while preserving fine-grained differences at deeper levels. It further employs a multi-stage training pipeline to retrieve all relevant items across views for a query image and incorporates user-specific signals for personalized preference modeling. In offline evaluations, OneVision performs on par with online MCA. Dynamic pruning further reduces its inference latency by 18% over the unpruned variant. In A/B tests, it achieves significant improvements: +3.61% item CTR, +2.43% CVR, and +5.89% order volume. The code will be publicly available.