Generative model for multi-modality outputs from a single input

A method comprising:

• generating, by a processing device, a plurality of different images from a single input using a generative adversarial network (GAN) machine learning model that includes a generator portion and a discriminator portion by:
• causing each of a plurality of different branches of the generator portion to generate one of the plurality of different images;
• and
• causing a consistency discriminator of the discriminator portion to ensure that the plurality of different images are visually similar to one another by processing concatenated outputs from the plurality of different branches of the generator portion.

A method comprising:

• generating, by a processing device, a trained generative adversarial network (GAN) machine learning model to output a plurality of different images from a single input by:
• causing each of a plurality of different branches of a generator portion of the GAN machine learning model to generate a different image from the single input;
• causing, for each of the plurality of different branches of the generator portion, a corresponding one of a plurality of fidelity discriminators of the GAN machine learning model to output a judgment indicating whether the branch of the generator portion output a visually realistic image;
• causing a consistency discriminator of the GAN machine learning model to output a judgment indicating whether the plurality of different images are visually similar to one another by processing concatenated outputs from the plurality of different branches of the generator portion;
• computing a loss function based on judgments output by the plurality of fidelity discriminators and the consistency discriminator;
• and
• updating at least one internal weight of the GAN machine learning model based on the loss function.