In this work, we propose TediGAN, a novel framework for multi-modal image
generation and manipulation with textual descriptions. The proposed method
consists of three components: StyleGAN inversion module, visual-linguistic
similarity learning, and instance-level optimization. The inversion module maps
real images to the latent space of a well-trained StyleGAN. The
visual-linguistic similarity learns the text-image matching by mapping the
image and text into a common embedding space. The instance-level optimization
is for identity preservation in manipulation. Our model can produce diverse and
high-quality images with an unprecedented resolution at 1024. Using a control
mechanism based on style-mixing, our TediGAN inherently supports image
synthesis with multi-modal inputs, such as sketches or semantic labels, with or
without instance guidance. To facilitate text-guided multi-modal synthesis, we
propose the Multi-Modal CelebA-HQ, a large-scale dataset consisting of real
face images and corresponding semantic segmentation map, sketch, and textual
descriptions. Extensive experiments on the introduced dataset demonstrate the
superior performance of our proposed method. Code and data are available at
this https URL

本文提出 TediGAN 框架，用于多模态图像生成和操作。该方法包括三个组件：StyleGAN 反演模块，视觉 - 语言相似度学习和实例级优化。模型可使用多模态输入生成多样化，分辨率为 1024 的高质量图像，并提出 Multi-Modal CelebA-HQ 数据集以支持文本引导的多模态综合。经过广泛实验，本方法展现出优越性能。