A Zipfian Analysis of Visual Token Distributions for AI-Generated Images

ACL ARR 2026 January Submission5691 Authors

Keywords: image generation, visual token, zipfs law, text-to-image generation, prompts

Abstract: The rapid evolution of text-to-image generation has blurred the perceptual boundary between natural and synthetic imagery. However, it remains questionable whether the statistical structure of generated visual content mirrors the information density of the physical visual world. Drawing upon principles from statistical linguistics, this study investigates the visual language of generative models through the lens of Zipfian dynamics. By analyzing a large-scale corpus of real and synthetic images, we uncover a fundamental divergence between visual syntax and semantics. We find that while generative models have successfully replicated the low-level physics of light, their high-level texture vocabulary exhibits distinct statistical signatures. Our analysis reveals a spectrum of entropy, identifying architectural fingerprints unique to each model. Furthermore, we investigate the relation ship between generated images and prompt complexity, and find that increasing the semantic specificity of text prompts systematically degrades the statistical realism of the generated output.

Paper Type: Short

Research Area: Multimodality and Language Grounding to Vision, Robotics and Beyond

Research Area Keywords: cross-modal content generation

Contribution Types: Model analysis & interpretability

Languages Studied: English

Submission Number: 5691