Papers › Styleformer: Transformer based Generative Adversarial Networks with Style Vector

Styleformer: Transformer based Generative Adversarial Networks with Style Vector

13 Jun 2021CVPR 2022 1arXiv:2106.07023archive 2025-07-28

Jeeseung Park, Younggeun Kim

We propose Styleformer, which is a style-based generator for GAN architecture, but a convolution-free transformer-based generator. In our paper, we explain how a transformer can generate high-quality images, overcoming the disadvantage that convolution operations are difficult to capture global features in an image. Furthermore, we change the demodulation of StyleGAN2 and modify the existing transformer structure (e.g., residual connection, layer normalization) to create a strong style-based generator with a convolution-free structure. We also make Styleformer lighter by applying Linformer, enabling Styleformer to generate higher resolution images and result in improvements in terms of speed and memory. We experiment with the low-resolution image dataset such as CIFAR-10, as well as the high-resolution image dataset like LSUN-church. Styleformer records FID 2.82 and IS 9.94 on CIFAR-10, a benchmark dataset, which is comparable performance to the current state-of-the-art and outperforms all GAN-based generative models, including StyleGAN2-ADA with fewer parameters on the unconditional setting. We also both achieve new state-of-the-art with FID 15.17, IS 11.01, and FID 3.66, respectively on STL-10 and CelebA. We release our code at https://github.com/Jeeseung-Park/Styleformer.

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file_ext Jeeseung-Park/Styleformer/dataset_tool.py official repository ran · our draft was wrong fingerprinted MIT (permissive) · a2b45afa097b55b6 · report
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Tasks

Image Generation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Generation STL-10 Styleformer FID 15.17 #12 of 31 Archive leaderboard report
Image Generation STL-10 Styleformer Inception score 11.01 #12 of 31 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

AttentionConvolutionDense ConnectionsLayer NormalizationLinear LayerLinformerMulti-Head Linear AttentionPath Length RegularizationR1 RegularizationResidual ConnectionSoftmaxWeight Demodulation

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