Diffusion-based generative models' impressive ability to create convincing images has garnered global attention. However, their complex internal structures and operations often pose challenges for non-experts to grasp. We introduce Diffusion Explainer, the first interactive visualization tool designed to elucidate how Stable Diffusion transforms text prompts into images. It tightly integrates a visual overview of Stable Diffusion's complex components with detailed explanations of their underlying operations. This integration enables users to fluidly transition between multiple levels of abstraction through animations and interactive elements. Offering real-time hands-on experience, Diffusion Explainer allows users to adjust Stable Diffusion's hyperparameters and prompts without the need for installation or specialized hardware. Accessible via users' web browsers, Diffusion Explainer is making significant strides in democratizing AI education, fostering broader public access. More than 7,200 users spanning 113 countries have used our open-sourced tool at https://poloclub.github.io/diffusion-explainer/. A video demo is available at https://youtu.be/MbkIADZjPnA.

翻译：基于扩散的生成模型在创建逼真图像方面展现出的卓越能力已引起全球关注。然而，其复杂的内部结构与运算机制常令非专业人士难以掌握。我们推出Diffusion Explainer——首个面向稳定扩散模型文本提示图像生成过程的可视化交互工具。该工具将稳定扩散复杂组件的视觉概览与其底层运算的详细解释紧密融合，通过动画与交互元素使用户能够流畅地在多个抽象层级间切换。凭借实时交互体验，用户无需安装任何软件或专用硬件即可调整稳定扩散的超参数与提示词。通过网页浏览器即可访问的Diffusion Explainer，正在推动人工智能教育的民主化进程，促进更广泛的公众参与。已有来自113个国家的7200余名用户通过https://poloclub.github.io/diffusion-explainer/使用我们的开源工具。视频演示见https://youtu.be/MbkIADZjPnA。