Popular artificial intelligence powered image generators can run up to 30 times faster thanks to a technique that condenses an entire 100-stage process into one step, new research shows.
"Our work is a novel method that accelerates current diffusion models such as Stable Diffusion and DALLE-3 by 30 times," study co-lead author Tianwei Yin, a doctoral student in electrical engineering and computer science at MIT, said in a statement."This advancement not only significantly reduces computational time but also retains, if not surpasses, the quality of the generated visual content.
In practice, these models work by taking a random image and encoding it with a field of random noise so it is destroyed, explained AI scientist Jay Alammar in a blog post. This is called"forward diffusion," and is a key step in the training process. Next, the image undergoes up to 100 steps to clear up the noise, known as"reverse diffusion" to produce a clear image based on the text prompt.
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