# The Creativity of Text-to-Image Generation

arXiv: https://arxiv.org/abs/2206.02904 · HTML: https://mlsanity.com/paper/2206.02904/

## Key facts

- Cited by 73 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2022.

## Built on by (most cited first, 30 of 73)

- [Concept Decomposition for Visual Exploration and Inspiration](https://mlsanity.com/paper/2305.18203/) (2023)
- [Will Large-scale Generative Models Corrupt Future Datasets?](https://mlsanity.com/paper/2211.08095/) (2022)
- [Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?](https://mlsanity.com/paper/2302.03495/) (2023)
- [Vitron: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editing](https://mlsanity.com/paper/2412.19806/) (2024)
- [Dynamic Prompt Optimizing for Text-to-Image Generation](https://mlsanity.com/paper/2404.04095/) (2024)
- [A Survey on Decentralized Federated Learning](https://mlsanity.com/paper/2308.04604/) (2023)
- [A Prompt Log Analysis of Text-to-Image Generation Systems](https://mlsanity.com/paper/2303.04587/) (2023)
- [DreamVideo: High-Fidelity Image-to-Video Generation with Image Retention and Text Guidance](https://mlsanity.com/paper/2312.03018/) (2023)
- [Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalization](https://mlsanity.com/paper/2404.00710/) (2024)
- [ABScribe: Rapid Exploration & Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Models](https://mlsanity.com/paper/2310.00117/) (2023)
- [Using Text-to-Image Generation for Architectural Design Ideation](https://mlsanity.com/paper/2304.10182/) (2023)
- [Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospects](https://mlsanity.com/paper/2311.02332/) (2023)
- [Measuring Diversity in Co-creative Image Generation](https://mlsanity.com/paper/2403.13826/) (2024)
- [CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive Survey](https://mlsanity.com/paper/2504.14280/) (2025)
- [T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Concepts](https://mlsanity.com/paper/2412.04300/) (2024)
- [From Efficient Multimodal Models to World Models: A Survey](https://mlsanity.com/paper/2407.00118/) (2024)
- [Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Model](https://mlsanity.com/paper/2505.04522/) (2025)
- [EasyControl: Transfer ControlNet to Video Diffusion for Controllable Generation and Interpolation](https://mlsanity.com/paper/2408.13005/) (2024)
- ["An Adapt-or-Die Type of Situation": Perception, Adoption, and Use of Text-To-Image-Generation AI by Game Industry Professionals](https://mlsanity.com/paper/2302.12601/) (2023)
- [Exploring Perspectives on the Impact of Artificial Intelligence on the Creativity of Knowledge Work: Beyond Mechanised Plagiarism and Stochastic Parrots](https://mlsanity.com/paper/2307.10751/) (2023)
- [Diffusion for Natural Image Matting](https://mlsanity.com/paper/2312.05915/) (2023)
- [Emerging Synergies in Causality and Deep Generative Models: A Survey](https://mlsanity.com/paper/2301.12351/) (2023)
- [Vision + Language Applications: A Survey](https://mlsanity.com/paper/2305.14598/) (2023)
- [Foundation Models and Transformers for Anomaly Detection: A Survey](https://mlsanity.com/paper/2507.15905/) (2025)
- [What Does DALL-E 2 Know About Radiology?](https://mlsanity.com/paper/2209.13696/) (2022)
- [JoPA:Explaining Large Language Model's Generation via Joint Prompt Attribution](https://mlsanity.com/paper/2405.20404/) (2024)
- [pOps: Photo-Inspired Diffusion Operators](https://mlsanity.com/paper/2406.01300/) (2024)
- [A Critical Assessment of Modern Generative Models' Ability to Replicate Artistic Styles](https://mlsanity.com/paper/2502.15856/) (2025)
- [Piece it Together: Part-Based Concepting with IP-Priors](https://mlsanity.com/paper/2503.10365/) (2025)
- [POET: Supporting Prompting Creativity and Personalization with Automated Expansion of Text-to-Image Generation](https://mlsanity.com/paper/2504.13392/) (2025)

## Abstract (arXiv)

Text-guided synthesis of images has made a giant leap towards becoming a mainstream phenomenon. With text-to-image generation systems, anybody can create digital images and artworks. This provokes the question of whether text-to-image generation is creative. This paper expounds on the nature of human creativity involved in text-to-image art (so-called "AI art") with a specific focus on the practice of prompt engineering. The paper argues that the current product-centered view of creativity falls short in the context of text-to-image generation. A case exemplifying this shortcoming is provided and the importance of online communities for the creative ecosystem of text-to-image art is highlighted. The paper provides a high-level summary of this online ecosystem drawing on Rhodes' conceptual four P model of creativity. Challenges for evaluating the creativity of text-to-image generation and opportunities for research on text-to-image generation in the field of Human-Computer Interaction (HCI) are discussed.