Diffusion LM

Diffusion Language Models (Diffusion LMs) are a class of generative models leveraging diffusion processes to produce text and other data types, aiming to improve upon the limitations of autoregressive approaches. Current research focuses on enhancing model architectures like UNets and Vision Transformers, exploring efficient training strategies (including incorporating metric functions and leveraging pre-trained models), and developing methods for improved controllability and faster inference. This rapidly evolving field holds significant promise for various applications, including synthetic data generation, image captioning, and analog circuit design optimization, by offering improved generation quality, control, and efficiency compared to existing techniques.