mlsanityData to 14 Oct 2026

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

arXiv 2305.03509 ↗First posted 4 May 2023cs.CL cs.AI cs.HCcited by 17

On the map: Diffusion models → Image generation, text-to-image and editing

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/.

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.

Builds on

14 of 52 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Best Prompts for Text-to-Image Models and How to Find Them23 Sep

Other references · 13

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  4. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  5. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  6. 2021Design Guidelines for Prompt Engineering Text-to-Image Generative Modelscited by 218
  7. 2020CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualizationcited by 57
  8. 2017ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Modelscited by 56
  9. 2022A Taxonomy of Prompt Modifiers for Text-To-Image Generationcited by 55
  10. 2018GAN Lab: Understanding Complex Deep Generative Models using Interactive Visual Experimentationcited by 41
  11. 2017Direct-Manipulation Visualization of Deep Networkscited by 28
  12. 2017Adversarial-Playground: A Visualization Suite Showing How Adversarial Examples Fool Deep Learningcited by 8
1 more
  1. 2022Acceleration AI Ethics, the Debate between Innovation and Safety, and Stability AI's Diffusion versus OpenAI's Dall-Ecited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 8; 2025: 4; 2026: 3.

17 papers.

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  1. 2023On the Design Fundamentals of Diffusion Models: A Surveycited by 60
  2. 2024Explainable and Interpretable Multimodal Large Language Models: A Comprehensive Surveycited by 52
  3. 2023Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiencescited by 21
  4. 2024Transformer Explainer: Learning LLM Transformers with Interactive Visual Explanation and Experimentationcited by 10
  5. 2024Global-Local Image Perceptual Score (GLIPS): Evaluating Photorealistic Quality of AI-Generated Imagescited by 7
  6. 2024Trustworthy Text-to-Image Diffusion Models: A Timely and Focused Surveycited by 6
  7. 2024ASAP: Interpretable Analysis and Summarization of AI-generated Image Patterns at Scalecited by 5
  8. 2025AI Generated Child Sexual Abuse Material -- What's the Harm?cited by 5
  9. 2024EvolvED: Evolutionary Embeddings to Understand the Generation Process of Diffusion Modelscited by 3
  10. 2024Mapping the Mind of an Instruction-based Image Editing using SMILEcited by 3
  11. 2025Scene Perceived Image Perceptual Score (SPIPS): combining global and local perception for image quality assessmentcited by 1
  12. 2025ChannelExplorer: Exploring Class Separability Through Activation Channel Visualizationcited by 1
5 more
  1. 2025Diffusion Explorer: Interactive Exploration of Diffusion Modelscited by 1
  2. 2024VAE Explainer: Supplement Learning Variational Autoencoders with Interactive Visualizationcited by 0
  3. 2026Attention Dynamics in Diffusion Models: A Visual Analytics Framework for Human-AI Collaborationcited by 0
  4. 2026ASAP: Visual Analytics for Identifying and Analyzing Image Patterns in AI-generated Imagescited by 0
  5. 2026Persistent Watermarking of Text-to-Image Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Interactive Visual Learning for Stable Diffusion
  2. 2024Explaining generative diffusion models via visual analysis for interpretable decision-making process
  3. 2023The Hidden Language of Diffusion Models
  4. 2024DiffusionPID: Interpreting Diffusion via Partial Information Decomposition
  5. 2025Emergence and Evolution of Interpretable Concepts in Diffusion Models
  6. 2025Blending Concepts with Text-to-Image Diffusion Models
  7. 2023Reverse Stable Diffusion: What prompt was used to generate this image?
  8. 2023Are Diffusion Models Vision-And-Language Reasoners?