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The Cultivated Practices of Text-to-Image Generation

Jonas Oppenlaender

TL;DR

The chapter investigates how text-to-image generation is reshaping creative practice by enabling broad participation and new collaborative workflows. It analyzes the ecosystem around prompts, data, models, and online communities, with a focus on prompt engineering as a central practice. It argues that this co-creative ecosystem forms an intelligent system that can both augment human creativity and potentially constrain future AI development, highlighting risks such as data bias, quality degradation from synthetic data, and broader effects on imagination. The work offers governance and design principles to promote sustainable, inclusive, and responsible growth of AI-driven creativity.

Abstract

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.

The Cultivated Practices of Text-to-Image Generation

TL;DR

The chapter investigates how text-to-image generation is reshaping creative practice by enabling broad participation and new collaborative workflows. It analyzes the ecosystem around prompts, data, models, and online communities, with a focus on prompt engineering as a central practice. It argues that this co-creative ecosystem forms an intelligent system that can both augment human creativity and potentially constrain future AI development, highlighting risks such as data bias, quality degradation from synthetic data, and broader effects on imagination. The work offers governance and design principles to promote sustainable, inclusive, and responsible growth of AI-driven creativity.

Abstract

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.
Paper Structure (10 sections, 2 theorems, 3 equations, 2 figures, 1 table)

This paper contains 10 sections, 2 theorems, 3 equations, 2 figures, 1 table.

Table of Contents

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Key Result

theorem 1

Theorem text goes here.

Figures (2)

  • Figure 1: If the width of the figure is less than 7.8 cm use the sidecapion command to flush the caption on the left side of the page. If the figure is positioned at the top of the page, align the sidecaption with the top of the figure -- to achieve this you simply need to use the optional argument [t] with the sidecaption command
  • Figure 2: If the width of the figure is less than 7.8 cm use the sidecapion command to flush the caption on the left side of the page. If the figure is positioned at the top of the page, align the sidecaption with the top of the figure -- to achieve this you simply need to use the optional argument [t] with the sidecaption command

Theorems & Definitions (6)

  • theorem 1
  • definition 1
  • proof
  • theorem 2
  • definition 2
  • proof