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Evaluating and comparing text-to-image models is a challenging problem.Significant advances in the field have recently been made, piquing interest ofvarious industrial sectors. As a consequence, a gold standard in the fieldshould cover a variety of tasks and application contexts. In this paper a novelevaluation approach is experimented, on the basis of: (i) a curated data set,made by high-quality royalty-free image-text pairs, divided into tencategories; (ii) a quantitative metric, the CLIP-score, (iii) a humanevaluation task to distinguish, for a given text, the real and the generatedimages. The proposed method has been applied to the most recent models, i.e.,DALLE2, Latent Diffusion, Stable Diffusion, GLIDE and Craiyon. Earlyexperimental results show that the accuracy of the human judgement is fullycoherent with the CLIP-score. The dataset has been made available to thepublic.

TeTIm-Eval: a novel curated evaluation data set for comparing text-to-image models