Rank-Factorized Implicit Neural Bias: Scaling Super-Resolution Transformer with FlashAttention
arXiv:2603.06738v2 Announce Type: replace Abstract: Recent Super-Resolution~(SR) methods mainly adopt Transformers for their strong long-range modeling capability and exceptional representational capacity. However, most SR Transformers rely heavily on relative positional bias~(RPB), which prevents them from leveraging hardware-efficient attention kernels such as FlashAttention. This limitation imposes a prohibitive computational burden during both training and inference, severely restricting attempts to scale SR Transformers by enlarging the training patch size or the self-attention window. Co
The paper addresses a critical computational bottleneck in Transformer-based Super-Resolution methods, which are becoming dominant due to their performance but are currently limited by hardware-inefficient attention mechanisms.
Improving the efficiency of Super-Resolution Transformers will enable larger models, higher resolution outputs, and faster processing, impacting fields reliant on high-quality image and video enhancement.
The ability to scale SR Transformers with hardware-efficient attention kernels like FlashAttention opens the door for more powerful and practical applications in computer vision without prohibitive computational costs.
- · AI hardware manufacturers
- · Computer vision researchers
- · Companies using Super-Resolution in products
- · Generative AI applications
- · Inferior Super-Resolution methods
- · Compute-constrained applications
Increased adoption and performance of Super-Resolution models for image and video upscaling.
Faster development and deployment of high-resolution generative AI models and media processing pipelines.
Reduced compute costs for high-fidelity visual AI, making advanced image/video generation and manipulation more accessible and widespread.
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Read at arXiv cs.LG