We present VinJ, an efficient automated tool for large-scale diverse vulnerability data generation. VinJ automatically generates vulnerability data by injecting vulnerabilities into given programs, based on knowledge learned from the existing vulnerability data. VinJ is built on the collaboration of pattern-based and deep learning (DL)-based approaches. More importantly, VinJ is efficient and scalable with the parallel clustering and the pre-ranked vulnerability-injection patterns, which supports large-scale vulnerability data generation. VinJ is able to generate diverse vulnerability data covering 18 CWEs with 69% success rate and generate 686k vulnerability samples in 74 hours (i.e., 0.4 seconds per sample), indicating it is efficient. The gen- erated data is able to improve existing DL-based vulnerability detection, localization, and repair models significantly. The demo video of VinJ can be found at https://youtu.be/-oKoUqBbxD4. The tool website can be found at https://github.com/NewGillig/VInj. We also release the generated large-scale vulnerability dataset, which can be found at https://zenodo.org/records/10574446.