nablaNABLA: Neighborhood Adaptive Block-Level Attention
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
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Dmitrii Mikhailov
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2025-07-28T08:45:41.907Z
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Dmitrii Mikhailov
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Botsman11
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Aleksey Letunovskiy
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2025-07-28T08:45:54.849Z
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Alexey Letunovskiy
user
AlexeyLetunovskiy
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Maria Kovaleva
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Vladimir Arkhipkin
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Vladimir Korviakov
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2025-07-25T13:36:18.497Z
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Vladimir Korviakov
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korviakov
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Vladimir Polovnikov
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Viacheslav Vasilev
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Evelina Sidorova
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Denis Dimitrov
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2025-07-25T13:36:20.344Z
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Denis Dimitrov
user
dendimitrov
nablaNABLA: Neighborhood Adaptive Block-Level Attention
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
NABLA, a Neighborhood Adaptive Block-Level Attention mechanism, enhances video diffusion transformers by reducing computational overhead without significantly impacting generative quality or visual fidelity.
transformer-based architectures video generation full attention mechanisms quadratic complexity Neighborhood Adaptive Block-Level Attention video diffusion transformers block-wise attention adaptive sparsity-driven threshold Flex Attention operator CLIP score VBench score human evaluation score