This survey reviews text-to-image diffusion models in the context that diffusion models have emerged to be popular for a wide range of generative tasks. As a self-contained work, this survey starts with a brief introduction of how a basic diffusion model works for image synthesis, followed by how condition or guidance improves learning. Based on that, we present a review of state-of-the-art methods on text-conditioned image synthesis, i.e., text-to-image. We further summarize applications beyond text-to-image generation: text-guided creative generation and text-guided image editing. Beyond the progress made so far, we discuss existing challenges and promising future directions.

翻译：本综述回顾了文本到图像扩散模型，背景是扩散模型已在广泛的生成任务中广受欢迎。作为一篇独立完整的综述，本文首先简要介绍基本扩散模型如何用于图像合成，随后探讨条件或引导如何改善学习过程。在此基础上，我们评述了文本条件图像合成（即文本到图像）的最先进方法。我们进一步总结了文本到图像生成之外的应用：文本引导的创意生成和文本引导的图像编辑。除了迄今为止取得的进展，我们还讨论了现有挑战和未来有前景的研究方向。