Semantically invariant conditioning of diffusion models: a unified framework for cross-model positive prompting

The purpose of research is development of a universal methodology of positive industrial engineering for image generation by diffusion models based on a deep linguistic and semantic analysis of Human-AI interaction and identification of cross-model invariants. Methods . Within the framework of this study, an interdisciplinary scientific approach was applied, combining methods of cognitive analysis and empirical verification. Results . The results of the study confirmed the high efficiency of the proposed universal methodology of positive industrial engineering, which significantly improved the quality of image generation by diffusion models. Experimental data have shown that promptas formed according to the developed structure and lexical optimization strategies provide better compliance with the specified characteristics and more stable results across different models, while statistically significantly exceeding the quality of unstructured promptas (p < 0,01). The use of a multi-level system of components and implicit control methods has made it possible to reduce the variability of unwanted artifacts, increase the accuracy of visual characteristics, and simplify the process of creating designs, making it more predictable, reproducible, and universal for various platforms. In general, the implementation of this methodology improves human interaction with AI, increases the stability and quality of visual results, and facilitates the adaptation of products to different models and tasks. Conclusion . The conducted research has confirmed the effectiveness of the proposed universal methodology of positive industrial engineering for image generation by diffusion models. The introduction of a structured approach and lexical optimization strategies can significantly improve the quality, stability and predictability of results, as well as reduce the number of unwanted artifacts. This approach promotes more manageable and universal human-AI interaction, making it easier to create high-quality images in various models and conditions. In the future, the use of the developed methodology can become the basis for improving the efficiency of automated visual content generation systems and expanding their practical capabilities.