# Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion

arXiv: https://arxiv.org/abs/2305.03509 · HTML: https://mlsanity.com/paper/2305.03509/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 14 of its 52 references that are in the map.
- First posted on arXiv 4 May 2023.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 14 of 14)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://mlsanity.com/paper/2109.06977/) (2021)
- [CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization](https://mlsanity.com/paper/2004.15004/) (2020)
- [ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models](https://mlsanity.com/paper/1704.01942/) (2017)
- [A Taxonomy of Prompt Modifiers for Text-To-Image Generation](https://mlsanity.com/paper/2204.13988/) (2022)
- [GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentation](https://mlsanity.com/paper/1809.01587/) (2018)
- [Best Prompts for Text-to-Image Models and How to Find Them](https://mlsanity.com/paper/2209.11711/) (2022)
- [Direct-Manipulation Visualization of Deep Networks](https://mlsanity.com/paper/1708.03788/) (2017)
- [Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learning](https://mlsanity.com/paper/1708.00807/) (2017)
- [Acceleration AI Ethics, the Debate between Innovation and Safety, and Stability AI's Diffusion versus OpenAI's Dall-E](https://mlsanity.com/paper/2212.01834/) (2022)

## Built on by (most cited first, 17 of 17)

- [On the Design Fundamentals of Diffusion Models: A Survey](https://mlsanity.com/paper/2306.04542/) (2023)
- [Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2412.02104/) (2024)
- [Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences](https://mlsanity.com/paper/2310.04621/) (2023)
- [Transformer Explainer: Learning LLM Transformers with Interactive Visual Explanation and Experimentation](https://mlsanity.com/paper/2408.04619/) (2024)
- [Global-Local Image Perceptual Score (GLIPS): Evaluating Photorealistic Quality of AI-Generated Images](https://mlsanity.com/paper/2405.09426/) (2024)
- [Trustworthy Text-to-Image Diffusion Models: A Timely and Focused Survey](https://mlsanity.com/paper/2409.18214/) (2024)
- [ASAP: Interpretable Analysis and Summarization of AI-generated Image Patterns at Scale](https://mlsanity.com/paper/2404.02990/) (2024)
- [AI Generated Child Sexual Abuse Material -- What's the Harm?](https://mlsanity.com/paper/2510.02978/) (2025)
- [EvolvED: Evolutionary Embeddings to Understand the Generation Process of Diffusion Models](https://mlsanity.com/paper/2406.17462/) (2024)
- [Mapping the Mind of an Instruction-based Image Editing using SMILE](https://mlsanity.com/paper/2412.16277/) (2024)
- [Scene Perceived Image Perceptual Score (SPIPS): combining global and local perception for image quality assessment](https://mlsanity.com/paper/2504.17234/) (2025)
- [ChannelExplorer: Exploring Class Separability Through Activation Channel Visualization](https://mlsanity.com/paper/2505.04647/) (2025)
- [Diffusion Explorer: Interactive Exploration of Diffusion Models](https://mlsanity.com/paper/2507.01178/) (2025)
- [VAE Explainer: Supplement Learning Variational Autoencoders with Interactive Visualization](https://mlsanity.com/paper/2409.09011/) (2024)
- [Attention Dynamics in Diffusion Models: A Visual Analytics Framework for Human-AI Collaboration](https://mlsanity.com/paper/2607.02563/) (2026)
- [ASAP: Visual Analytics for Identifying and Analyzing Image Patterns in AI-generated Images](https://mlsanity.com/paper/2609.27371/) (2026)
- [Persistent Watermarking of Text-to-Image Models](https://mlsanity.com/paper/2609.39024/) (2026)

## Abstract (arXiv)

Diffusion-based generative models' impressive ability to create convincing images has garnered global attention. However, their complex structures and operations often pose challenges for non-experts to grasp. We present Diffusion Explainer, the first interactive visualization tool that explains how Stable Diffusion transforms text prompts into images. Diffusion Explainer tightly integrates a visual overview of Stable Diffusion's complex structure with explanations of the underlying operations. By comparing image generation of prompt variants, users can discover the impact of keyword changes on image generation. A 56-participant user study demonstrates that Diffusion Explainer offers substantial learning benefits to non-experts. Our tool has been used by over 10,300 users from 124 countries at https://poloclub.github.io/diffusion-explainer/.