Algorithm Design of High Resolution AI Illustration Generation Based on GANs

With the development of deep learning technology, GANS (Generate Adversarial Networks) has made remarkable achievements in the field of image generation. This research is devoted to designing a GAN model specially designed for high-resolution AI illustration. Compared with the traditional image generation technology, illustration generation requires not only high resolution and details of images, but also unique artistic style and emotional expression. Therefore, we optimized the basic structure of GANs and introduced multi-scale structure and attention mechanism to better capture the details and styles of illustrations. The results show that the accuracy of this model on data sets has reached the highest value of 96%, while the highest value of traditional model is 92%. Based on the above analysis, it is verified that this model is effective and can reconstruct images with higher detail quality. It can be seen that the model in this paper shows superiority in evaluation index, and can generate illustrations with high quality and unique style. This study provides a new perspective and an effective technical solution for the field of AI illustration generation.