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Интеллектуальная Система Тематического Исследования НАукометрических данных |
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NABLA: Neighborhood Adaptive Block-Level Attentionстатья
- Авторы: Mikhailov Dmitrii, Letunovskiy Aleksey, Kovaleva Maria, Arkhipkin Vladimir, Korviakov Vladimir, Polovnikov Vladimir, Vasilev Viacheslav, Sidorova Evelina, Dimitrov Denis
- Журнал: arXive e-print
- Год издания: 2025
- DOI: 10.48550/ARXIV.2507.13546
- Аннотация: 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.
- Добавил в систему: Половников Владимир Сергеевич