Visual design and digital transformation of imaging under artificial intelligence technology.

This study proposes a conditional hierarchical generative adversarial network (GAN) model for intelligent visual design, aiming to explore new modes of human-machine collaboration and promote digital transformation in the design field. The proposed model consists of three core modules: a multimodal conditional fusion encoder, a hierarchical generator, and a collaborative multi-discriminator architecture. It innovatively integrates multiple sources of design intent-including textual descriptions, hand-drawn sketches, spatial layouts, and brand guidelines-through a cross-modal attention mechanism. The model adopts a two-stage generation strategy of "structure first, details later" and introduces multi-objective adversarial training to simultaneously optimize generation quality, layout accuracy, and semantic consistency. Experiments on the Common Objects in Context with Stuff Annotations (COCO-Stuff) 164 K dataset show that the model achieves a Fréchet Inception Distance (FID) score of 18.37 and 92.8% layout control accuracy on the test set. These results demonstrate that the proposed model significantly outperforms mainstream baseline models in both generation quality and controllability. This study provides an innovative technical solution for professional visual design and offers theoretical and practical insights into the evolution of creative agency, human-machine collaboration mechanisms, and the pathways of digital transformation in the Artificial Intelligence era.