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Backend-Agnostic Sparse Attention for Fast High-Resolution Visual Generation

Paper recorded by Signals 4 on 2026-10-06 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-06 on arXiv · recorded by Signals 4 on 2026-10-07

Category: cs.CV · 计算机视觉 · first seen 2026-10-07

Abstract

Diffusion Transformers (DiTs) have achieved strong performance in image and video generation, but the quadratic complexity of full attention makes high-resolution generation computationally expensive. Window attention offers an efficient alternative, yet existing methods face a practical trade-off: partitioned window attention typically achieves computational efficiency consistent with its theoret

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#339 most recent of 756 cs.CV papers we have recorded · ↑ newer: CtrlCache: Accelerating Interactive Video World Models with Control-Aw · ↓ older: Data Leakage in Patch-Based Hyperspectral Image Classification: Quanti
Cite this page: Backend-Agnostic Sparse Attention for Fast High-Resolution Visual Generation: the #339 most recent of 756 cs.CV papers we have recorded (as of 2026-10-06). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/backend-agnostic-sparse-attention-for-fast-high-resolution-visual-generation.html
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