AdaptiveLoad: Towards Efficient Video Diffusion Transformer Training
In video generation models, particularly world models, training large-scale video diffusion Transformers (such as DiT and MMDiT) poses significant computational challenges due to the extreme variance in sequence lengths within mixed-mode datasets. Existing bucket-based data loading strategies typically rely on "equal token length" constraints. This approach fails to account for the quadratic complexity of self-attention mechanisms, leading to severe load imbalance and underutilization of GPU resources. This paper proposes \textit{AdaptiveLoad}, an integrated optimization framework consisting of two core components: (1) A dual-constraint adaptive load balancing system, which eliminates lon...
| Authors | Yucheng Guo, Yongjian Guo, Zhong Guan et al. |
|---|---|
| Primary category | cs.DC |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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