Design of personalized creation model for cultural and creative products based on evolutionary adaptive network

In the realm of personalized cultural and creative product design, the capacity for nuanced semantic expression and refined style modulation in image content exerts a pivotal influence on user experience and the perceived creative value. Addressing the limitations of current generative models—particularly in maintaining stylistic coherence and accommodating individualized preferences—this article introduces a novel image synthesis framework grounded in a synergistic mechanism that integrates text-driven guidance, adaptive style modulation, and evolutionary optimization: Evolutionary Adaptive Generative Aesthetic Network (EAGAN). Anchored in the Stable Diffusion architecture, the model incorporates a semantic text encoder and a style transfer module that will realize the image style transfer, augmented by the Adaptive Instance Normalization (AdaIN) mechanism, to enable precise manipulation of stylistic attributes. Concurrently, it embeds an evolutionary optimization component that iteratively refines cue phrases, stylistic parameters, and latent noise vectors through a genetic algorithm, thereby enhancing the system’s responsiveness to dynamic user tastes. Empirical evaluations on benchmark datasets demonstrate that EAGAN surpasses prevailing approaches across a suite of metrics—including Fréchet inception distance (FID), CLIPScore, and Learned Perceptual Image Patch Similarity (LPIPS)—notably excelling in the harmonious alignment of semantic fidelity and stylistic expression. Ablation studies further underscore the critical contributions of the style control and evolutionary optimization modules to overall performance gains. This work delineates a robust and adaptable technological trajectory with substantial practical promise for the intelligent, personalized generation of cultural and creative content, thus fostering the digital and individualized evolution of the creative industries.