It's a Feature, Not a Bug: Measuring Creative Fluidity in Image Generators

TL;DR

This paper defines and empirically measures the creative fluidity of AI image generators by analyzing how they interpret prompts across chained generations, revealing insights into their creative behavior.

Contribution

It introduces a formal definition of fluidity, creates prompt-image chains, and develops metrics to quantify and analyze the fluidity in popular image generators.

Findings

AI image generators exhibit measurable fluidity in prompt interpretation.

Fluidity varies significantly across different generators.

The study provides a framework for assessing creativity in AI art.

Abstract

With the rise of freely available image generators, AI-generated art has become the centre of a series of heated debates, one of which concerns the concept of human creativity. Can an image generation AI exhibit ``creativity'' of the same type that artists do, and if so, how does that manifest? Our paper attempts to define and empirically measure one facet of creative behavior in AI, by conducting an experiment to quantify the "fluidity of prompt interpretation", or just "fluidity", in a series of selected popular image generators. To study fluidity, we (1) introduce a clear definition for it, (2) create chains of auto-generated prompts and images seeded with an initial "ground-truth: image, (3) measure these chains' breakage points using preexisting visual and semantic metrics, and (4) use both statistical tests and visual explanations to study these chains and determine whether the image generators used to produce them exhibit significant fluidity.