Adversarial Diffusion Distillation

Adversarial Diffusion Distillation (ADD) is a technique accelerating inference in diffusion models, a class of generative models known for high-quality image and audio synthesis but hampered by slow generation times. Current research focuses on applying ADD to various modalities, including image super-resolution, voice conversion, and video generation, often leveraging techniques like progressive distillation and ControlNet to improve efficiency and quality. This approach significantly reduces the number of sampling steps needed, enabling real-time or near real-time generation while maintaining comparable fidelity to slower, multi-step methods, thus impacting both research and applications requiring fast generative capabilities.