AI generates high-quality images 30 times faster in a single step

MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have introduced a new framework that simplifies the multi-step process of traditional diffusion models into a single step, addressing previous limitations. This is done through a type of teacher-student model: teaching a new computer model to mimic the behavior of more complicated, original models that generate images.

The approach, known as distribution matching distillation (DMD), retains the quality of the generated images and allows for much faster generation.

"Our work is a novel method that accelerates current diffusion models such as Stable Diffusion and DALLE-3 by 30 times," says Tianwei Yin, an MIT Ph.D. student in electrical engineering and computer science, CSAIL affiliate and the lead researcher on the DMD framework.

"Decreasing the number of iterations has been the Holy Grail in diffusion models since their inception," says Fredo Durand, MIT professor of electrical engineering and computer science, CSAIL principal investigator, and a lead author on the paper. "We are very excited to finally enable single-step image generation, which will dramatically reduce compute costs and accelerate the process."