Consistency Distillation

Consistency distillation is a machine learning technique focused on accelerating the inference speed of generative models, particularly diffusion models, while preserving or even improving output quality. Current research emphasizes efficient training strategies, often leveraging pre-trained models and incorporating techniques like adversarial learning or reward modeling to guide the distillation process, with applications spanning image generation, text-to-3D modeling, and even robotic control. This approach offers significant potential for deploying computationally expensive generative models on resource-constrained devices and for improving the efficiency of various AI applications.