Application of Diffusion Models in Complex Visual Communication Design and Their Enhancement of Design Efficiency
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Abstract
Diffusion models provide controllable and high-fidelity generative tools for complex visual communication design through progressive denoising and flexible conditional guidance. This study investigates their application from four perspectives: technical adaptability, workflow reconstruction, efficiency quantification, and large-scale deployment. By analyzing brand visual identity, multimodal interfaces, and cross-media narrative tasks, the study shows that diffusion models can compress iteration cycles, reduce designer cognitive load, and improve stylistic consistency across outputs. A task-process-oriented efficiency evaluation model is constructed to identify the mechanisms and boundaries of efficiency gains in requirement analysis, concept generation, prototype development, scheme selection, and delivery revision. The results also reveal limitations related to generative controllability, copyright uncertainty, and professional competency restructuring. The study positions diffusion models as intelligent visual signal-generation systems for human-machine co-design, with implications for multimodal interfaces, digital display communication, and design tasks involving complex semantic control.
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