Image stylization aims at applying a reference style to arbitrary input images. A common scenario is one-shot stylization, where only one example is available for each reference style. Recent approaches for one-shot stylization such as JoJoGAN fine-tune a pre-trained StyleGAN2 generator on a single style reference image. However, such methods cannot generate multiple stylizations without fine-tuning a new model for each style separately. In this work, we present a MultiStyleGAN method that is capable of producing multiple different stylizations at once by fine-tuning a single generator. The key component of our method is a learnable transformation module called Style Transformation Network. It takes latent codes as input, and learns linear mappings to different regions of the latent space to produce distinct codes for each style, resulting in a multistyle space. Our model inherently mitigates overfitting since it is trained on multiple styles, hence improving the quality of stylizations. Our method can learn upwards of $120$ image stylizations at once, bringing $8\times$ to $60\times$ improvement in training time over recent competing methods. We support our results through user studies and quantitative results that indicate meaningful improvements over existing methods.

翻译：图像风格化旨在将参考风格应用于任意输入图像。常见场景为一次性风格化，即每种参考风格仅有一个样本可用。近期一次性风格化方法（如JoJoGAN）通过在单个风格参考图像上微调预训练的StyleGAN2生成器实现。然而，此类方法无法在不分别为每种风格单独微调新模型的情况下生成多种风格化结果。本文提出MultiStyleGAN方法，通过微调单个生成器即可同时生成多种不同风格化结果。该方法的核心组件是一个名为风格变换网络的可学习变换模块。该模块以潜码为输入，学习潜空间不同区域的线性映射，从而为每种风格生成不同的码字，最终构建多风格空间。由于模型在多种风格上训练，其本质上可缓解过拟合问题，从而提升风格化质量。本方法可一次性学习多达120种图像风格化，训练时间较现有方法提升8倍至60倍。我们通过用户研究和定量实验结果证明，本方法相较现有方法具有显著改进。