SnapFusion optimizes the UNET architecture to improve inference speed.
The aim of the optimizations is to reduce the number of denoising steps and to increase the speed of the conditional diffusion model.
Diffusion models are the next big thing, but we need to tackle their complexity before applying them in practical applications.
If you want to use diffusion models on your mobile device, well, good luck with that, as it will be too demanding for the limited hardware power of the device itself.
Here’s an example: SnapFusion focuses on optimizing the UNet architecture, which is a bottleneck in the Conducible diffusion Model.
It optimizesThe UNet structure
and reduces the number de novoising steps to improve inferential speed.
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