Facial images have extensive practical applications. Although the current large-scale text-image diffusion models exhibit strong generation capabilities, it is challenging to generate the desired facial images using only text prompt. Image prompts are a logical choice. However, current methods of this type generally focus on general domain. In this paper, we aim to optimize image makeup techniques to generate the desired facial images. Specifically, (1) we built a dataset of 4 million high-quality face image-text pairs (FaceCaptionHQ-4M) based on LAION-Face to train our Face-MakeUp model; (2) to maintain consistency with the reference facial image, we extract/learn multi-scale content features and pose features for the facial image, integrating these into the diffusion model to enhance the preservation of facial identity features for diffusion models. Validation on two face-related test datasets demonstrates that our Face-MakeUp can achieve the best comprehensive performance.All codes are available at:https://github.com/ddw2AIGROUP2CQUPT/Face-MakeUp

本研究解决了当前文本提示生成所需面部图像的困难，提出了一种优化图像化妆技术的新方法。通过构建一个包含400万个高质量面部图像-文本对的数据集，我们的Face-MakeUp模型在面部图像生成的一致性和性能上表现出显著优势，为文本到图像生成领域带来了重要的影响。

面部化妆：用于文本到图像生成的多模态面部提示