Low light enhancement has gained increasing importance with the rapid development of visual creation and editing. However, most existing enhancement algorithms are designed to homogeneously increase the brightness of images to a pre-defined extent, limiting the user experience. To address this issue, we propose Controllable Light Enhancement Diffusion Model, dubbed CLE Diffusion, a novel diffusion framework to provide users with rich controllability. Built with a conditional diffusion model, we introduce an illumination embedding to let users control their desired brightness level. Additionally, we incorporate the Segment-Anything Model (SAM) to enable user-friendly region controllability, where users can click on objects to specify the regions they wish to enhance. Extensive experiments demonstrate that CLE Diffusion achieves competitive performance regarding quantitative metrics, qualitative results, and versatile controllability. Project page: https://yuyangyin.github.io/CLEDiffusion/

翻译：随着视觉创作与编辑技术的快速发展，低光照增强技术日益重要。然而，现有的大多数增强算法都将图像亮度统一增强至预设程度，限制了用户的个性化体验。为解决这一问题，我们提出了可控光增强扩散模型（简称CLE Diffusion），一种新型扩散框架，可为用户提供丰富的可控性。该模型基于条件扩散模型构建，通过引入光照嵌入（illumination embedding）使用户能够控制所需的亮度级别。此外，我们集成了Segment-Anything Model（SAM）以实现友好的区域可控性——用户只需点击物体即可指定需要增强的区域。大量实验表明，CLE Diffusion在量化指标、定性结果和多功能可控性方面均展现出具有竞争力的性能。项目页面：https://yuyangyin.github.io/CLEDiffusion/