Text-to-image generation systems have become increasingly popular for creating digital art. These systems allow anyone to create high-quality digital images by simply inputting natural language prompts. However, the question arises as to whether this process is truly creative. The traditional definition of creativity as a product-centered view may not fully capture the human creativity involved in text-to-image generation. A more comprehensive view, such as Rhodes’ conceptual model of the “4 P” of creativity, is needed to evaluate the full extent of human creativity in this context. Online communities also play an important role in the creative ecosystem around text-to-image generation.

Researchers have examined text-to-image art’s creativity and human creativity’s role through empirical studies, theoretical analyses, and critical reviews. Studies have shown that text-to-image art may be perceived as less creative than human-generated art but still shows signs of creativity. Theoretical analyses have explored whether creativity can be automated, while critical reviews have looked at the broader social and cultural implications of text-to-image art. Whether text-to-image art is truly creative and the role of human creativity in the process remains an active area of research and debate. 

Recently a researcher from the University of JyväskyläIs in Finland published an article to try to answer the question: Is text-to-image art truly creative, and what role does human creativity play in the process?

The main contribution of this paper is to explore and expound on the nature of human creativity involved in the text-to-image generation, specifically in the sub-culture of text-to-image art. The paper argues that human creativity in text-to-image synthesis lies not in the end product (i.e., the digital image), but arises from the interaction of humans with the AI and the resulting practices that evolve from this interaction (e.g., “prompt engineering” and curation).

To achieve this contribution, the paper uses Rhodes’ four P framework to explain the nature of human creativity involved in text-to-image generation, with a special focus on the iterative and interactive practice of prompt engineering and the online community of practitioners of this novel creative practice. The paper also highlights image-level and portfolio-level curation as two important creative practices involved in the creative process of text-to-image generation.

Furthermore, the paper emphasizes the growing importance of communities in the emerging ecosystem of text-to-image generation as a catalyst for creativity and learning and outlines five different roles taken by members of the AI art community. The paper also discusses the practical challenges of evaluating the creativity of images synthesized with text-to-image generation systems and provides opportunities for future research in the field of Human-Computer Interaction (HCI) and the broader implications of text-based co-creation with AI-based systems.

In conclusion, the article confirms that text-to-image art contributes to the digital creative economy by selling NFTs, but raises questions about the level of human creativity involved. The standard product-based definition of creativity may not fully capture the unique factors contributing to creative expression. Challenges also arise when assessing the creativity of text-to-image art.

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Mahmoud is a PhD researcher in machine learning. He also holds a
bachelor’s degree in physical science and a master’s degree in
telecommunications and networking systems. His current areas of
research concern computer vision, stock market prediction and deep
learning. He produced several scientific articles about person re-
identification and the study of the robustness and stability of deep