Controllable Generation

Controllable generation focuses on creating models that produce outputs (images, text, music, etc.) adhering to specific constraints or user-defined parameters. Current research emphasizes efficient methods for incorporating diverse control signals into existing generative models, such as diffusion models and autoregressive models, often leveraging techniques like prompt engineering, fine-tuning, and reparameterization to achieve this control. This field is significant because it enables more precise and tailored generation, improving applications ranging from autonomous driving and protein design to text summarization and artistic creation. The development of more efficient and robust controllable generation methods is driving progress across numerous scientific disciplines and technological applications.