# The Cultivated Practices of Text-to-Image Generation

arXiv: https://arxiv.org/abs/2306.11393 · HTML: https://mlsanity.com/paper/2306.11393/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 61 references that are in the map.
- First posted on arXiv 20 Jun 2023.

## Builds on (most cited first, 30 of 30)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Training Compute-Optimal Large Language Models](https://mlsanity.com/paper/2203.15556/) (2022)
- [LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairs](https://mlsanity.com/paper/2111.02114/) (2021)
- [Discovering Language Model Behaviors with Model-Written Evaluations](https://mlsanity.com/paper/2212.09251/) (2022)
- [Extracting Training Data from Diffusion Models](https://mlsanity.com/paper/2301.13188/) (2023)
- [Does Learning Require Memorization? A Short Tale about a Long Tail](https://mlsanity.com/paper/1906.05271/) (2019)
- [Large Language Models Struggle to Learn Long-Tail Knowledge](https://mlsanity.com/paper/2211.08411/) (2022)
- [Augmented Language Models: a Survey](https://mlsanity.com/paper/2302.07842/) (2023)
- [The Curse of Recursion: Training on Generated Data Makes Models Forget](https://mlsanity.com/paper/2305.17493/) (2023)
- [VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance](https://mlsanity.com/paper/2204.08583/) (2022)
- [Manipulating and Measuring Model Interpretability](https://mlsanity.com/paper/1802.07810/) (2018)
- [Pretraining Language Models with Human Preferences](https://mlsanity.com/paper/2302.08582/) (2023)
- [Accelerating Sparse Deep Neural Networks](https://mlsanity.com/paper/2104.08378/) (2021)
- [Design Guidelines for Prompt Engineering Text-to-Image Generative Models](https://mlsanity.com/paper/2109.06977/) (2021)
- [Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models](https://mlsanity.com/paper/2305.13873/) (2023)
- [The Creativity of Text-to-Image Generation](https://mlsanity.com/paper/2206.02904/) (2022)
- [Text-Guided Synthesis of Artistic Images with Retrieval-Augmented Diffusion Models](https://mlsanity.com/paper/2207.13038/) (2022)
- [Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors](https://mlsanity.com/paper/2105.02980/) (2021)
- [Level Playing Field for Million Scale Face Recognition](https://mlsanity.com/paper/1705.00393/) (2017)
- [The Birth of Bias: A case study on the evolution of gender bias in an English language model](https://mlsanity.com/paper/2207.10245/) (2022)
- [Using Text-to-Image Generation for Architectural Design Ideation](https://mlsanity.com/paper/2304.10182/) (2023)
- [When happy accidents spark creativity: Bringing collaborative speculation to life with generative AI](https://mlsanity.com/paper/2206.00533/) (2022)
- [Perceptions and Realities of Text-to-Image Generation](https://mlsanity.com/paper/2306.08363/) (2023)

## Built on by (most cited first, 3 of 3)

- [Mapping the Ethics of Generative AI: A Comprehensive Scoping Review](https://mlsanity.com/paper/2402.08323/) (2024)
- [An Exploration of Default Images in Text-to-Image Generation](https://mlsanity.com/paper/2505.09166/) (2025)
- [Artworks Reimagined: Exploring Human-AI Co-Creation through Body Prompting](https://mlsanity.com/paper/2408.05476/) (2024)

## Abstract (arXiv)

Humankind is entering a novel creative era in which anybody can synthesize digital information using generative artificial intelligence (AI). Text-to-image generation, in particular, has become vastly popular and millions of practitioners produce AI-generated images and AI art online. This chapter first gives an overview of the key developments that enabled a healthy co-creative online ecosystem around text-to-image generation to rapidly emerge, followed by a high-level description of key elements in this ecosystem. A particular focus is placed on prompt engineering, a creative practice that has been embraced by the AI art community. It is then argued that the emerging co-creative ecosystem constitutes an intelligent system on its own - a system that both supports human creativity, but also potentially entraps future generations and limits future development efforts in AI. The chapter discusses the potential risks and dangers of cultivating this co-creative ecosystem, such as the bias inherent in today's training data, potential quality degradation in future image generation systems due to synthetic data becoming common place, and the potential long-term effects of text-to-image generation on people's imagination, ambitions, and development.