Text-to-image synthesis has recently seen significant progress thanks to large pretrained language models, large-scale training data, and the introduction of scalable model families such as diffusion and autoregressive models. However, the best-performing models require iterative evaluation to generate a single sample. In contrast, generative adversarial networks (GANs) only need a single forward pass. They are thus much faster, but they currently remain far behind the state-of-the-art in large-scale text-to-image synthesis. This paper aims to identify the necessary steps to regain competitiveness. Our proposed model, StyleGAN-T, addresses the specific requirements of large-scale text-to-image synthesis, such as large capacity, stable training on diverse datasets, strong text alignment, and controllable variation vs. text alignment tradeoff. StyleGAN-T significantly improves over previous GANs and outperforms distilled diffusion models - the previous state-of-the-art in fast text-to-image synthesis - in terms of sample quality and speed.

该论文旨在使用生成对抗网络改进大规模文本到图像合成，提出了StyleGAN-T模型，它在大规模文本到图像合成中具有大容量、稳定的训练、强文本对齐和可控变化与文本对齐平衡等特点，并在样本质量和速度方面显著优于以前的生成对抗网络和蒸馏扩散模型。

StyleGAN-T: 用于快速大规模文本到图像合成的GAN强化技术