Kling-Omni Technical Report
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.
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Kling Team
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Jialu Chen
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Yuanzheng Ci
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Xiangyu Du
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Zipeng Feng
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Kun Gai
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Sainan Guo
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Feng Han
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Jingbin He
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Kang He
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Xiao Hu
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Xiaohua Hu
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Boyuan Jiang
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Fangyuan Kong
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Hang Li
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Jie Li
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Qingyu Li
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Shen Li
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Xiaohan Li
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Yan Li
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Jiajun Liang
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Borui Liao
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Yiqiao Liao
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Weihong Lin
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Quande Liu
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Xiaokun Liu
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Yilun Liu
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Yuliang Liu
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Shun Lu
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Hangyu Mao
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Yunyao Mao
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Haodong Ouyang
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Wenyu Qin
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Wanqi Shi
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Xiaoyu Shi
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Lianghao Su
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Haozhi Sun
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Peiqin Sun
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Pengfei Wan
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Chao Wang
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Chenyu Wang
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Meng Wang
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Qiulin Wang
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Runqi Wang
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Xintao Wang
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Xuebo Wang
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Zekun Wang
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Min Wei
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Tiancheng Wen
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Guohao Wu
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Xiaoshi Wu
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Zhenhua Wu
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Da Xie
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Yingtong Xiong
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Yulong Xu
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Sile Yang
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Zikang Yang
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Weicai Ye
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Ziyang Yuan
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Shenglong Zhang
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Shuaiyu Zhang
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Yuanxing Zhang
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Yufan Zhang
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Wenzheng Zhao
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Ruiliang Zhou
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Yan Zhou
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Guosheng Zhu
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Yongjie Zhu
Kling-Omni Technical Report
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.
Kling-Omni is a versatile generative framework that synthesizes high-quality videos from multimodal inputs, integrating generation, editing, and reasoning into a unified system.
generative framework multimodal visual language inputs end-to-end video generation editing intelligent reasoning unified multimodal representation cinematic-quality efficient large-scale pre-training inference optimizations in-context generation reasoning-based editing multimodal instruction following multimodal world simulators