生成语法
图像(数学)
计算机科学
背景(考古学)
生成模型
扩散
航程(航空)
图像编辑
人工智能
物理
工程类
地理
热力学
航空航天工程
考古
作者
Chenshuang Zhang,Chaoning Zhang,Mengchun Zhang,In So Kweon
出处
期刊:Cornell University - arXiv
日期:2023-01-01
被引量:54
标识
DOI:10.48550/arxiv.2303.07909
摘要
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.
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