The field of image generation and manipulation is witnessing significant advancements, particularly in the areas of content preservation, style control, and domain adaptation. Recent developments have focused on enhancing the precision and flexibility of generative models, enabling more controlled and diverse outputs. Innovations in content-aware image generation have introduced frameworks that integrate advanced encoding techniques to ensure desired content is preserved while allowing stylistic variations. In the realm of style-conditioned image generation, there is a growing emphasis on creating more user-friendly and shareable style codes, which simplify the process of controlling image styles without compromising quality. Additionally, the integration of pre-trained models with CLIP space via hypernetworks is pushing the boundaries of domain adaptation and text-guided image manipulation, offering unprecedented flexibility and superior performance. These advancements not only improve the quality of generated images but also broaden the applications of generative models in various domains, including unmanned aircraft systems trajectory prediction.