Kling-Omni Technical Report
Authors: name: Kling Team, name: Jialu Chen, name: Yuanzheng Ci, name: Xiangyu Du, name: Zipeng Feng, name: Kun Gai, name: Sainan Guo, name: Feng Han, name: Jingbin He, name: Kang He, name: Xiao Hu, name: Xiaohua Hu, name: Boyuan Jiang, name: Fangyuan Kong, name: Hang Li, name: Jie Li, name: Qingyu Li, name: Shen Li, name: Xiaohan Li, name: Yan Li, name: Jiajun Liang, name: Borui Liao, name: Yiqiao Liao, name: Weihong Lin, name: Quande Liu, name: Xiaokun Liu, name: Yilun Liu, name: Yuliang Liu, name: Shun Lu, name: Hangyu Mao, name: Yunyao Mao, name: Haodong Ouyang, name: Wenyu Qin, name: Wanqi Shi, name: Xiaoyu Shi, name: Lianghao Su, name: Haozhi Sun, name: Peiqin Sun, name: Pengfei Wan, name: Chao Wang, name: Chenyu Wang, name: Meng Wang, name: Qiulin Wang, name: Runqi Wang, name: Xintao Wang, name: Xuebo Wang, name: Zekun Wang, name: Min Wei, name: Tiancheng Wen, name: Guohao Wu, name: Xiaoshi Wu, name: Zhenhua Wu, name: Da Xie, name: Yingtong Xiong, name: Yulong Xu, name: Sile Yang, name: Zikang Yang, name: Weicai Ye, name: Ziyang Yuan, name: Shenglong Zhang, name: Shuaiyu Zhang, name: Yuanxing Zhang, name: Yufan Zhang, name: Wenzheng Zhao, name: Ruiliang Zhou, name: Yan Zhou, name: Guosheng Zhu, name: Yongjie Zhu
Organization: Kling Team

We present Kling-Omni, a generalist generative framework designed to synthesize high-fidelity videos directly from multimodal visual language inputs. Adopting an end-to-end perspective, Kling-Omni bridges the functional separation among diverse video generation, editing, and intelligent reasoning tasks, integrating them into a holistic system. Unlike disjointed pipeline approaches, Kling-Omni supports a diverse range of user inputs, including text instructions, reference images, and video contexts, processing them into a unified multimodal representation to deliver cinematic-quality and highly-intelligent video content creation. To support these capabilities, we constructed a comprehensive data system that serves as the foundation for multimodal video creation. The framework is further empowered by efficient large-scale pre-training strategies and infrastructure optimizations for inference. Comprehensive evaluations reveal that Kling-Omni demonstrates exceptional capabilities in in-context generation, reasoning-based editing, and multimodal instruction following. Moving beyond a content creation tool, we believe Kling-Omni is a pivotal advancement toward multimodal world simulators capable of perceiving, reasoning, generating and interacting with the dynamic and complex worlds.