Generative Image Model

Generative image models aim to create realistic and diverse images from various inputs, such as text descriptions or semantic layouts, using techniques like diffusion models, GANs, and VAEs. Current research focuses on improving controllability, mitigating biases (e.g., racial or cultural), enhancing efficiency (e.g., through token downsampling), and addressing privacy concerns related to data leakage and memorization. These advancements have significant implications for various fields, including art, design, medical imaging, and combating misinformation through techniques like watermarking and fake image detection.