Artistic Creation Inspiration Generation System Based on Diffusion Model
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Abstract
In traditional artistic creation, inspiration relies heavily on subjective experience and occasional associations, resulting in limited systematicity and controllability during the early conception stage. Similar challenges also exist in intelligent visual design and information presentation for engineering applications, where efficient human–machine interaction and semantic visualization are increasingly important for electromagnetic communication systems and digital design environments. This paper proposes a text-guided diffusion model for artistic inspiration generation, particularly suitable for visual creation tasks such as illustration and conceptual art while remaining applicable to product innovation fields. The proposed framework integrates CLIP semantic embedding with conditional control mechanisms into a customized UNet architecture based on Stable Diffusion, enabling user text prompts to guide image synthesis through multi-layer cross-attention. Classifier-free guidance (CFG) and a multi-scale noise scheduling strategy are further introduced to improve style controllability, detail representation, and generation stability while supporting iterative refinement. Experimental results demonstrate stable performance with high semantic consistency (CLIP similarity of 0.82), effective style control (0.76), and favorable image quality (FID of 18.5). User evaluations achieve scores of 4.33 for creative inspiration and 4.49 for overall satisfaction, confirming that the proposed system provides an efficient and expressive visual inspiration generation tool. The framework also offers a systematic approach for multimodal semantic visualization and intelligent content generation, which may provide useful references for visual information representation and human–machine collaborative design in advanced electromagnetic and communication-related applications.
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