nablaNABLA: Neighborhood Adaptive Block-Level Attention
Authors: name: Dmitrii Mikhailov, name: Aleksey Letunovskiy, name: Maria Kovaleva, name: Vladimir Arkhipkin, name: Vladimir Korviakov, name: Vladimir Polovnikov, name: Viacheslav Vasilev, name: Evelina Sidorova, name: Denis Dimitrov

Recent progress in transformer-based architectures has demonstrated
remarkable success in video generation tasks. However, the quadratic complexity
of full attention mechanisms remains a critical bottleneck, particularly for
high-resolution and long-duration video sequences. In this paper, we propose
NABLA, a novel Neighborhood Adaptive Block-Level Attention mechanism that
dynamically adapts to sparsity patterns in video diffusion transformers (DiTs).
By leveraging block-wise attention with adaptive sparsity-driven threshold,
NABLA reduces computational overhead while preserving generative quality. Our
method does not require custom low-level operator design and can be seamlessly
integrated with PyTorch's Flex Attention operator. Experiments demonstrate that
NABLA achieves up to 2.7x faster training and inference compared to baseline
almost without compromising quantitative metrics (CLIP score, VBench score,
human evaluation score) and visual quality drop. The code and model weights are
available here: https://github.com/gen-ai-team/Wan2.1-NABLA