Guohao Wu

Kling Team, Jialu Chen, Yuanzheng Ci et al.

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.

Yuliang Liu, Guohao Wu, Shenglong Zhang et al.

The efficient distributed training of Large Language Models (LLMs) is severely hampered by the extreme variance in context lengths. This data heterogeneity, amplified by conventional packing strategies and asymmetric forward-backward costs, leads to critical inefficiencies such as cascading workload imbalances and severe hardware underutilization. Existing solutions attempt to mitigate these challenges, but often at the expense of memory or communication efficiency. To address these challenges, we introduce SlimPack, a framework that fundamentally rethinks data packing and scheduling by decomposing samples into fine-grained slices. This slice-level decomposition immediately mitigates critical memory and communication bottlenecks by transforming large, volatile workloads into a stream of smaller, manageable units. This flexibility is then harnessed for our core innovation, Asymmetric Partitioning, which assembles balanced scheduling units uniquely optimized for the different demands of the forward and backward passes. Orchestrated by a two-phase solver and a high-fidelity simulator, SlimPack holistically resolves imbalances across all parallel dimensions. Extensive experiments demonstrate that SlimPack achieves up to a $2.8\times$ training throughput improvement over baselines, breaking the conventional trade-off by delivering both superior balance and high resource efficiency.