MIT Unveils AI Tool Generating High-Quality Images 30 Times Faster
Cambridge, MA, 21st March 2025 – An Artificial Intelligence tool that can generate high-quality images 30 times faster has just been created by researchers at the Massachusetts Institute of Technology (MIT).
High-level image processing often uses the distillation process in two stages. With the use of the DMD (distribution matching distillation) framework, image-processing tasks take less time because it follows a simpler process.
The teacher-student approach allows the AI to learn complex image generation processes which involve mimicking to accelerate image generation.
Tianwei Yin, an electric engineering and computer science PhD student at MIT and one of the lead researchers of the DMD framework remarks.
“Our work is a novel method that accelerates current diffusion models such as Stable Diffusion and DALL-E-3 by 30 times. This advancement does not only save a huge amount computational time, but as well affects significantly the quality of visual content to be generated.”
Combining the two paradigms in one step is the essence of the DMD framework as it assimilates GANs and diffusion models in visual content generation which is the opposite of what the paradigm shift proposes.
The implementation of further machine learning techniques greatly enhances functions in both industrial and biomedical applications for faster and accurate results.
“Decreasing the number of iterations has been the Holy Grail in diffusion models since their inception.”
Said Fredo Durand, MIT professor and head of the Computer Science and Artificial Intelligence Laboratory (CSAIL).
“I am so happy that we can now implement single-step image generation. This will save on compute costs and speed up the process significantly.”
Along with MIT’s William T. Freeman, Adobe’s Gharbi, Zhang, Shechtman and G. Park will be representing the research team at the upcoming Conference on Computer Vision and Pattern Recognition in June.
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