Conditional Image Synthesis

Conditional image synthesis aims to generate images based on user-specified conditions, such as text descriptions, sketches, or segmentation maps. Current research heavily utilizes diffusion models, often incorporating techniques like time-decoupled training for efficiency and mixture-of-experts for handling diverse instructions. This field is crucial for advancing various applications, including image editing, 3D modeling, and data augmentation, by enabling more precise control over image generation and improving the quality and diversity of synthetic images. Furthermore, efforts are underway to develop more explainable evaluation metrics for these models.