Hybrid sampling for diffusion models

A method comprising:

• obtaining a noisy image;
• generating a first denoised image based on the noisy image during a first reverse diffusion phase using a diffusion neural network;
• injecting noise into the first denoised image during the first reverse diffusion phase;
• and
• generating a second denoised image based on the first denoised image and the noise injection from the first reverse diffusion phase, wherein the second denoised image is generated during a second reverse diffusion phase using the diffusion neural network without injecting noise during the second reverse diffusion phase.

An apparatus comprising:

• at least one processor;
• at least one memory including instructions executable by the processor;
• the apparatus further comprising a diffusion neural network comprising parameters stored in the at least one memory, wherein the diffusion neural network is configured to compute noise of a noisy image;
• a first sampler configured to remove noise based on an output of the diffusion neural network during a first reverse diffusion phase;
• and
• a second sampler configured to remove noise based on an output of the diffusion neural network during a second reverse diffusion phase.