A change to make diffusion models 80% faster

New discovery: leaner, meaner diffusion

“I am speed” - Sanic, while running a diffusion model 80% faster than before

There's no shortage of excitement around diffusion models like DALL-E 2 and Stable Diffusion, and for good reason. These AI systems can generate stunning images from mere text descriptions, enabling everything from digital art to computer-aided design. But beneath the surface, there's a dirty secret: diffusion models are notoriously slow and computationally intensive.

Examples and results table from the paper

It sounds too good to be true, but that's exactly what a team from Oxford, Cambridge, Imperial College London, and King's College London are claiming in their latest paper. By completely rethinking a core component of diffusion models, they've achieved a seemingly impossible trifecta: faster, lighter, and better. They claim they can make models up to 80% faster while using 75% fewer parameters and a fraction of the memory.

If their approach pans out, it could be a watershed moment for diffusion models. Suddenly, the most powerful image and video synthesis models would be practical for a much wider range of applications, from mobile apps to real-time creative tools to robotic movement planning (yep!). And that's just the beginning.

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Now, let’s dig in…