Papers › StyleNAT: Giving Each Head a New Perspective
StyleNAT: Giving Each Head a New Perspective
Steven Walton, Ali Hassani, Xingqian Xu, Zhangyang Wang, Humphrey Shi
Image generation has been a long sought-after but challenging task, and performing the generation task in an efficient manner is similarly difficult. Often researchers attempt to create a "one size fits all" generator, where there are few differences in the parameter space for drastically different datasets. Herein, we present a new transformer-based framework, dubbed StyleNAT, targeting high-quality image generation with superior efficiency and flexibility. At the core of our model, is a carefully designed framework that partitions attention heads to capture local and global information, which is achieved through using Neighborhood Attention (NA). With different heads able to pay attention to varying receptive fields, the model is able to better combine this information, and adapt, in a highly flexible manner, to the data at hand. StyleNAT attains a new SOTA FID score on FFHQ-256 with 2.046, beating prior arts with convolutional models such as StyleGAN-XL and transformers such as HIT and StyleSwin, and a new transformer SOTA on FFHQ-1024 with an FID score of 4.174. These results show a 6.4% improvement on FFHQ-256 scores when compared to StyleGAN-XL with a 28% reduction in the number of parameters and 56% improvement in sampling throughput. Code and models will be open-sourced at https://github.com/SHI-Labs/StyleNAT.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Generation | FFHQ 1024 x 1024 | StyleNAT | FID | 4.17 | #12 of 20 | Archive leaderboard | report |
| Image Generation | FFHQ 256 x 256 | StyleNAT | FID | 2.05 | #2 of 51 | Archive leaderboard | report |
| Image Generation | FFHQ 256 x 256 | StyleNAT (DINOv2) | FD | 229.72 | #42 of 51 | Archive leaderboard | report |
| Image Generation | FFHQ 256 x 256 | StyleNAT (DINOv2) | Precision | 0.79 | #42 of 51 | Archive leaderboard | report |
| Image Generation | FFHQ 256 x 256 | StyleNAT (DINOv2) | Recall | 0.41 | #42 of 51 | Archive leaderboard | report |
| Image Generation | LSUN Churches 256 x 256 | StyleNAT | FID | 3.4 | #8 of 27 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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