Methods and Tool Optimization for Similarity Avoidance in AI-Generated Graphic Design Content

Abstract

With the widespread application of generative artificial intelligence in the field of graphic design, the high production efficiency it brings is accompanied by significant risks of content homogenization and stylistic convergence. The fundamental nature of AI models, trained on large-scale datasets, often results in similarities in the composition, elements, and style of their output, severely constraining the originality and commercial distinctiveness of designs. This research focuses on the core challenge of avoiding similarity in AI-generated graphic design content, aiming to develop a systematic solution encompassing both generation methods and tool optimization.The study first provides an in-depth analysis of the technical root causes of similarity, including training data bias, the limitations of prompts, inherent model patterns, and the convergence of generation parameters. Building on this foundation, the paper proposes and explores multi-dimensional avoidance methods. At the technical level, it advocates for a "hybrid generation" strategy that combines different modal models (e.g., image-text, video generation models) for cross-inspiration and re-creation. It also introduces mechanisms for controllable randomness and noise injection to disrupt the model's inherent output patterns, alongside developing deep style transfer and element recombination algorithms based on semantic understanding. At the management and strategic level, it emphasizes building high-quality, diverse, and domain-specific refined datasets and promotes an iterative "human-machine collaboration" workflow. This workflow deeply integrates designers' aesthetic judgment and creative intervention at critical nodes within the generation chain.To effectively implement these methods, the research further outlines pathways for tool optimization. The focus is on developing intelligent "de-homogenization" plugins and platform features. These would integrate modules for multi-model comparative generation, style entropy analysis, and element deconstruction and fusion suggestions, providing designers with real-time similarity assessment and decision support for differentiation adjustments. The goal of tool optimization is to simplify the complex technical process of avoiding similarity into a visual, controllable, and user-friendly design assistant. This aims to ensure and stimulate the uniqueness and innovativeness of designs while enhancing efficiency. This study offers theoretical and practical references for the evolution of AI-assisted design from a mere "production tool" to a "creative partner," contributing positively to the healthy and diversified development of the design industry.Keywords: AI-generated design; content similarity; de-homogenization; method avoidance; tool optimization; human-machine collaboration

Keywords: AI-generated Design, Content Similarity, De-homogenization, Method Avoidance, Tool Optimization, Human-machine Collaboration

DOI: 10.54941/ahfe1007322