Text-guided image generation aimed to generate desired images conditioned on given texts, while text-guided image manipulation refers to semantically edit parts of a given image based on specified texts. For these two similar tasks, the key point is to ensure image fidelity as well as semantic consistency. Many previous approaches require complex multi-stage generation and adversarial training, while struggling to provide a unified framework for both tasks. In this work, we propose TextCLIP, a unified framework for text-guided image generation and manipulation without adversarial training. The proposed method accepts input from images or random noise corresponding to these two different tasks, and under the condition of the specific texts, a carefully designed mapping network that exploits the powerful generative capabilities of StyleGAN and the text image representation capabilities of Contrastive Language-Image Pre-training (CLIP) generates images of up to $1024\times1024$ resolution that can currently be generated. Extensive experiments on the Multi-modal CelebA-HQ dataset have demonstrated that our proposed method outperforms existing state-of-the-art methods, both on text-guided generation tasks and manipulation tasks.

提出了TextCLIP，这是一个统一的框架，用于无对抗训练的文本引导的图像生成和操作，通过对Contrastive Language-Image Pre-training (CLIP)的文本图像表示能力和StyleGAN的生成能力的结合，能够生成高达1024×1024分辨率的图像，并在Multi-modal CelebA-HQ数据集上取得了优于现有最先进方法的结果。

TextCLIP：无对抗训练的文本指导人脸图像生成与操作