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In computer science and mathematics, solving complex problems often involves using equations to model how something behaves over time or space. In the realm of generative processes, which help create new data or samples, two kinds of equations are commonly used: Ordinary Differential Equations (ODE) and Stochastic Differential Equations (SDE).
ODE-based samplers are fast but have limitations on how good the results can be.
SDE-based samplers give better results but take longer to do it.
The difference in performance is attributed to how these methods handle errors. ODE has less error due to simplifying assumptions, whereas SDE reduces error through randomness.
To get the best of both worlds, a new method called "Restart" has been developed. This new method aims to be both fast and accurate. In tests, Restart has shown to be better than both ODE and SDE methods in terms of speed and quality of results. Specifically, it's much faster on datasets like CIFAR-10 and ImageNet, and it provides better quality samples.
Also, in text-to-image generation, Restart proves to be more balanced in maintaining both image quality and diversity compared to previous methods.
So, Restart offers a new, balanced way to generate high-quality data quickly, surpassing older methods in both speed and quality.
Restart is the best sampler by far, how or when we can use it in Fooocus?
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