Diffusion Models Having Improved Accuracy and Reduced Consumption of Computational Resources

One or more non-transitory, computer-readable media collectively storing at least a noising model of a diffusion model, the diffusion model comprising:

• the noising model comprising a plurality of noising stages, the noising model configured to introduce noise to input data according to a noise schedule to produce intermediate data;
• and
• a denoising model configured to reconstruct output data from the intermediate data;
• wherein the noise schedule is a learned noise schedule that comprises one or more learned parameter values.

A computer-implemented method for training a diffusion model while consuming fewer computational resources, the method comprising:

• obtaining, by a computing system comprising one or more computing devices, training data, the training data comprising one or more channels;
• providing, by the computing system, the training data to a machine-learned diffusion model, the machine-learned diffusion model comprising:
• a noising model comprising a plurality of noising stages, the noising model configured to introduce noise to receive the training data and produce intermediate data in response to receipt of the training data;
• and
• a denoising model configured to reconstruct output data from the intermediate data;
• and
• determining, by the computing system, a training loss based at least in part on use of the machine-learned diffusion model with the training data, wherein the diffusion model is trained by optimizing parameters of the machine-learned diffusion model towards an evidence lower bound, wherein the evidence lower bound comprises a continuous-time loss.