mlsanityData to 7 Oct 2026

No Longer Trending on Artstation: Prompt Analysis of Generative AI Art

arXiv 2401.14425 ↗First posted 24 Jan 2024cs.HC cs.AI cs.CVcited by 7

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

Abstract (arXiv)

Image generation using generative AI is rapidly becoming a major new source of visual media, with billions of AI generated images created using diffusion models such as Stable Diffusion and Midjourney over the last few years. In this paper we collect and analyse over 3 million prompts and the images they generate. Using natural language processing, topic analysis and visualisation methods we aim to understand collectively how people are using text prompts, the impact of these systems on artists, and more broadly on the visual cultures they promote. Our study shows that prompting focuses largely on surface aesthetics, reinforcing cultural norms, popular conventional representations and imagery. We also find that many users focus on popular topics (such as making colouring books, fantasy art, or Christmas cards), suggesting that the dominant use for the systems analysed is recreational rather than artistic.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 16 of its 40 references that are in the map. First posted on arXiv 24 Jan 2024.

Builds on

16 of 40 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Models26 Oct
  2. 2022Best Prompts for Text-to-Image Models and How to Find Them23 Sep

Other references · 14

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2022Generative Adversarial Networkscited by 5,603
  3. 2022BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generationcited by 4,310
  4. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  5. 2020MPNet: Masked and Permuted Pre-training for Language Understandingcited by 973
  6. 2016Bag of Tricks for Efficient Text Classificationcited by 869
  7. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
  8. 2022Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scalecited by 262
  9. 2021Design Guidelines for Prompt Engineering Text-to-Image Generative Modelscited by 216
  10. 2023Social Biases through the Text-to-Image Generation Lenscited by 126
  11. 2022How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?cited by 92
  12. 2023A Prompt Log Analysis of Text-to-Image Generation Systemscited by 24
2 more
  1. 2023Navigating Cultural Chasms: Exploring and Unlocking the Cultural POV of Text-To-Image Modelscited by 16
  2. 2023Is Writing Prompts Really Making Art?cited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 1.

7 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2022Language Does More Than Describe: On The Lack Of Figurative Speech in Text-To-Image Models
  2. 2025Style Transfer: A Decade Survey
  3. 2023PromptPaint: Steering Text-to-Image Generation Through Paint Medium-like Interactions
  4. 2026The Silent Brush: Evaluating Artistic Style Leakage in AI Art Generation
  5. 2025The Cow of Rembrandt - Analyzing Artistic Prompt Interpretation in Text-to-Image Models
  6. 2024Equivalence: An analysis of artists' roles with Image Generative AI from Conceptual Art perspective through an interactive installation design practice
  7. 2025Stable diffusion models reveal a persisting human and AI gap in visual creativity
  8. 2023From paintbrush to pixel: A review of deep neural networks in AI-generated art