Controllable Image Synthesis

Controllable image synthesis aims to generate images with precise control over their content and style, guided by various inputs like text descriptions, sketches, or semantic masks. Current research heavily utilizes diffusion models and generative adversarial networks (GANs), focusing on improving fine-grained control through latent space manipulation, multi-modal input integration, and efficient tuning methods. This field is significant for expanding data augmentation techniques in areas like medical image analysis and industrial defect detection, and for enabling more creative and intuitive image editing and generation tools.