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Computer Science > Human-Computer Interaction

arXiv:2404.16069 (cs)
[Submitted on 22 Apr 2024]

Title:Interactive Visual Learning for Stable Diffusion

Authors:Seongmin Lee, Benjamin Hoover, Hendrik Strobelt, Zijie J. Wang, ShengYun Peng, Austin Wright, Kevin Li, Haekyu Park, Haoyang Yang, Polo Chau
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Abstract: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 this https URL. A video demo is available at this https URL.
Comments: 4 pages, 3 figures. arXiv admin note: substantial text overlap with arXiv:2305.03509
Subjects: Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
Cite as: arXiv:2404.16069 [cs.HC]
  (or arXiv:2404.16069v1 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2404.16069

Submission history

From: Seongmin Lee [view email]
[v1] Mon, 22 Apr 2024 23:23:45 UTC (10,061 KB)
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