Consistency Model

Consistency models are a novel class of generative models designed to produce high-quality samples with significantly faster inference times than traditional diffusion models. Current research focuses on improving training efficiency, exploring applications across diverse fields (including image generation, robotic manipulation, and inverse problem solving), and developing novel architectures like those based on diffusion model distillation or direct noise-to-data mapping. This work holds significant promise for accelerating various applications requiring fast and high-fidelity sample generation, impacting fields ranging from computer vision and robotics to medical imaging and speech synthesis.