The Cultivated Practices of Text-to-Image Generation

TL;DR

This paper explores the rise of text-to-image AI generation, emphasizing prompt engineering, its ecosystem, and potential societal risks including bias, quality degradation, and impacts on human imagination.

Contribution

It provides an overview of the development of text-to-image AI, highlights the role of prompt engineering, and discusses societal implications and risks of this emerging ecosystem.

Findings

The ecosystem supports human creativity and innovation.

Risks include bias, quality degradation, and societal impacts.

Prompt engineering is central to AI art creation.

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.