Data to 14 Oct 2026

The Creativity of Text-to-Image Generation

arXiv 2206.02904 ↗First posted 13 May 2022cs.HC cs.GRcited by 73

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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.

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

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Citing papers per year: 2022: 3; 2023: 21; 2024: 24; 2025: 17; 2026: 8.

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2023Concept Decomposition for Visual Exploration and Inspirationcited by 61
2022Will Large-scale Generative Models Corrupt Future Datasets?cited by 59
2023Can ChatGPT Write a Good Boolean Query for Systematic Review Literature Search?cited by 54
2024Vitron: A Unified Pixel-level Vision LLM for Understanding, Generating, Segmenting, Editingcited by 49
2024Dynamic Prompt Optimizing for Text-to-Image Generationcited by 45
2023A Survey on Decentralized Federated Learningcited by 27
2023A Prompt Log Analysis of Text-to-Image Generation Systemscited by 24
2023DreamVideo: High-Fidelity Image-to-Video Generation with Image Retention and Text Guidancecited by 20
2024Unknown Prompt, the only Lacuna: Unveiling CLIP's Potential for Open Domain Generalizationcited by 20
2023ABScribe: Rapid Exploration & Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Modelscited by 17
2023Using Text-to-Image Generation for Architectural Design Ideationcited by 16
2023Multimodal Machine Learning in Image-Based and Clinical Biomedicine: Survey and Prospectscited by 15

38 more

2024Measuring Diversity in Co-creative Image Generationcited by 15
2025CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive Surveycited by 15
2024T2I-FactualBench: Benchmarking the Factuality of Text-to-Image Models with Knowledge-Intensive Conceptscited by 14
2024From Efficient Multimodal Models to World Models: A Surveycited by 13
2025Text2CT: Towards 3D CT Volume Generation from Free-text Descriptions Using Diffusion Modelcited by 13
2024EasyControl: Transfer ControlNet to Video Diffusion for Controllable Generation and Interpolationcited by 12
2023"An Adapt-or-Die Type of Situation": Perception, Adoption, and Use of Text-To-Image-Generation AI by Game Industry Professionalscited by 11
2023Exploring Perspectives on the Impact of Artificial Intelligence on the Creativity of Knowledge Work: Beyond Mechanised Plagiarism and Stochastic Parrotscited by 11
2023Diffusion for Natural Image Mattingcited by 11
2023Emerging Synergies in Causality and Deep Generative Models: A Surveycited by 10
2023Vision + Language Applications: A Surveycited by 8
2025Foundation Models and Transformers for Anomaly Detection: A Surveycited by 8
2022What Does DALL-E 2 Know About Radiology?cited by 7
2024JoPA:Explaining Large Language Model's Generation via Joint Prompt Attributioncited by 6
2024pOps: Photo-Inspired Diffusion Operatorscited by 6
2025A Critical Assessment of Modern Generative Models' Ability to Replicate Artistic Stylescited by 6
2025Piece it Together: Part-Based Concepting with IP-Priorscited by 6
2025POET: Supporting Prompting Creativity and Personalization with Automated Expansion of Text-to-Image Generationcited by 6
2023Perceptions and Realities of Text-to-Image Generationcited by 5
2023Towards Generalizable Data Protection With Transferable Unlearnable Examplescited by 4
2024Algorithmic Ways of Seeing: Using Object Detection to Facilitate Art Explorationcited by 4
2025Expertise elevates AI usage: experimental evidence comparing laypeople and professional artistscited by 4
2025CartoAgent: a multimodal large language model-powered multi-agent cartographic framework for map style transfer and evaluationcited by 4
2023The Cultivated Practices of Text-to-Image Generationcited by 3
2024Towards a Science Exocortexcited by 3
2024CAD-Prompted Generative Models: A Pathway to Feasible and Novel Engineering Designscited by 3
2025GenColor: Generative Color-Concept Association in Visual Designcited by 3
2025CraftMesh: High-Fidelity Generative Mesh Manipulation via Poisson Seamless Fusioncited by 3
2026JANUS: A Lightweight Framework for Jailbreaking Text-to-Image Models via Distribution Optimizationcited by 3
2023Affect-Conditioned Image Generationcited by 2
2023Mapping the Challenges of HCI: An Application and Evaluation of ChatGPT for Mining Insights at Scalecited by 2
2024SOWing Information: Cultivating Contextual Coherence with MLLMs in Image Generationcited by 2
2025TRIZ Agents: A Multi-Agent LLM Approach for TRIZ-Based Innovationcited by 2
2022The Impact of Generative AI on the Future of Visual Content Marketingcited by 1
2023On Feature Scaling of Recursive Feature Machinescited by 1
2024Human Aesthetic Preference-Based Large Text-to-Image Model Personalization: Kandinsky Generation as an Examplecited by 1
2024Investigating the diversity and stylization of contemporary user generated visual arts in the complexity entropy planecited by 1
2024Disentangling Mean Embeddings for Better Diagnostics of Image Generatorscited by 1

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