Diffusion serving

Diffusion models are a class of generative models that have gained popularity for their ability to generate high-quality images. They work by gradually adding noise to an image and then learning to reverse this process, effectively denoising the image step by step.

Milestone models

Stable Diffusion

Stable video Diffusion

ControlNet

Diffusion transformer

Main challenges

Computation burdens

Heterogeneous hardware

Memory demands

Inference engines

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