Despite impressive recent advances in text-to-image diffusion models,
obtaining high-quality images often requires prompt engineering by humans who
have developed expertise in using them. In this work, we present NeuroPrompts,
an adaptive framework that automatically enhances a user's prompt to improve
the quality of generations produced by text-to-image models. Our framework
utilizes constrained text decoding with a pre-trained language model that has
been adapted to generate prompts similar to those produced by human prompt
engineers. This approach enables higher-quality text-to-image generations and
provides user control over stylistic features via constraint set specification.
We demonstrate the utility of our framework by creating an interactive
application for prompt enhancement and image generation using Stable Diffusion.
Additionally, we conduct experiments utilizing a large dataset of
human-engineered prompts for text-to-image generation and show that our
approach automatically produces enhanced prompts that result in superior image
quality. We make our code, a screencast video demo and a live demo instance of
NeuroPrompts publicly available.

通过使用预先训练的语言模型生成与人类专家设计相似的提示语，NeuroPrompts 自适应框架可以改善文本到图像模型生成结果的质量，并提供用户对风格特征的控制。研究通过创建一个与 Stable Diffusion 相关的互动应用程序，展示了该框架的实用性。此外，通过利用大型数据集的人工设计提示，在文本到图像生成过程中，自动产生的改进提示可获得更高质量的图像。