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An important milestone for AI is the development of algorithms that canproduce drawings that are indistinguishable from those of humans. Here, weadapt the 'diversity vs. recognizability' scoring framework from Boutin et al,2022 and find that one-shot diffusion models have indeed started to close thegap between humans and machines. However, using a finer-grained measure of theoriginality of individual samples, we show that strengthening the guidance ofdiffusion models helps improve the humanness of their drawings, but they stillfall short of approximating the originality and recognizability of humandrawings. Comparing human category diagnostic features, collected through anonline psychophysics experiment, against those derived from diffusion modelsreveals that humans rely on fewer and more localized features. Overall, ourstudy suggests that diffusion models have significantly helped improve thequality of machine-generated drawings; however, a gap between humans andmachines remains -- in part explainable by discrepancies in visual strategies.

Diffusion Models as Artists: Are we Closing the Gap between Humans and Machines?