Application of the Uncertainty Principle in Text-to-Image Generation: Collaborative Efforts of Technology and Users to Reduce Spatial Uncertainty

Abstract

In recent years, Text-to-Image Generation (T2I) technology has made significant progress and entered a stage of large-scale application, widely serving fields such as artistic creation, design, advertising, and education. Although early studies (e.g., Ramesh et al., 2021) have demonstrated its immense potential, limitations in training data and structural modeling capabilities have resulted in persistent deficiencies in the system's ability to construct spatial relationships. As of April 2025, the issue of spatial structure distortion in image generation has yet to be fundamentally resolved. This paper proposes a novel perspective: drawing an analogy between the "Uncertainty Principle" in quantum physics and the spatial uncertainty phenomenon in text-to-image generation systems. The study suggests that the technical approach can optimize model structures by introducing pre-training mechanisms for spatial relationships, while the user approach can, based on achieving deep semantic resonance with AI, supplement spatial parameters through natural language and adopt "Spatial Anchoring" strategies to generate prompts with greater spatial clarity. The collaborative mechanism involving both technology and users significantly mitigates the structural uncertainty caused by the disconnection in semantic-spatial mapping. Through specific experiments, this paper validates the effectiveness of the aforementioned strategies in enhancing the precision of spatial expression in images, providing theoretical foundations and empirical support for the optimization of text-to-image systems and the expansion of human-AI co-creation pathways.

Keywords: Uncertainty Principle, Spatial Uncertainty, Text-to-Image Generation, Spatial Anchoring, Prompt Optimization, Human-AI Resonance, Quantum Physics