Synthesis and perceptual scaling of high-resolution naturalistic images using Stable Diffusion

Abstract Naturalistic scenes are of key interest for visual perception, but controlling their perceptual and semantic properties is challenging. Previous work on naturalistic scenes has frequently focused on collections of discrete images with considerable physical differences between stimuli. However, it is often desirable to assess representations of naturalistic images that vary along a continuum. Traditionally, perceptually continuous variations of naturalistic stimuli have been obtained by morphing a source image into a target image. This produces transitions driven mainly by low-level physical features and can result in semantically ambiguous outcomes. More recently, generative adversarial networks (GANs) have been used to generate continuous perceptual variations within a stimulus category. Here, we extend and generalize this approach using a different machine learning approach, a text-to-image diffusion model (Stable Diffusion XL), to generate a freely customizable stimulus set of photorealistic images that are characterized by gradual transitions, with each image representing a unique exemplar within a prompted category. We demonstrate the approach by generating a set of 108 object scenes from six categories. For each object scene, we generate ten variants that are ordered along a perceptual continuum. This ordering was first estimated using a machine learning model of perceptual similarity (LPIPS) and then subsequently validated with a large online sample of human participants. In a subsequent experiment, we show that this ordering is also predictive of stimulus confusability in a working memory task. Our image set is suited for studies investigating the graded encoding of naturalistic stimuli in visual perception, attention, and memory.