Interactive Visual Learning for Stable Diffusion
<|reference_start|>Interactive Visual Learning for Stable Diffusion: 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.<|reference_end|>
@article{lee2024interactive,
    title={Interactive Visual Learning for Stable Diffusion},
    author={Seongmin Lee, Benjamin Hoover, Hendrik Strobelt, Zijie J. Wang,
  ShengYun Peng, Austin Wright, Kevin Li, Haekyu Park, Haoyang Yang, Polo Chau},
    journal={arXiv preprint arXiv:2404.16069},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.16069},
    primaryClass={cs.HC cs.AI}
}