Generating synthetic images for training machine learning models

A method for training a diffusion model, which can be used to iteratively generate a synthetic image from noise in conjunction with a specified conditioning, the synthetic image being consistent with the conditioning, the method comprising the following steps:

• specifying a style that the synthetically generated images should have;
• providing a set of training images that match the specified style to varying degrees;
• successively applying noise to each respective training image of the training images in a specified number of iterations, so that respective noised versions are created;
• drawing samples from the respective noised versions;
• processing each of the drawn samples by the diffusion model in conjunction with the specified conditioning to produce predictions for a previous noised version in each case;
• evaluating a correspondence between the predictions and the noised versions using a specified cost function;
• and
• optimizing parameters that characterize a behavior of the diffusion model with an aim of improving the evaluation that uses the cost function during further processing of training images and samples generated from the training images;
• wherein, when drawing the samples, and/or when evaluating the predictions generated from the drawn samples using the cost function, those samples that still reflect the style of the respective training image are represented more strongly, the more closely the respective training image matches the specified style.

A non-transitory machine-readable data carrier on which is stred a computer program including machine-readable instructions for training a diffusion model, which can be used to iteratively generate a synthetic image from noise in conjunction with a specified conditioning, the synthetic image being consistent with the conditioning, the instructions, when executed by one or more computers and/or compute instances, cause the one or more computers and/or compute instances to perform the following steps:

• specifying a style that the synthetically generated images should have;
• providing a set of training images that match the specified style to varying degrees;
• successively applying noise to each respective training image of the training images in a specified number of iterations, so that respective noised versions are created;
• drawing samples from the respective noised versions;
• processing each of the drawn samples by the diffusion model in conjunction with the specified conditioning to produce predictions for a previous noised version in each case;
• evaluating a correspondence between the predictions and the noised versions using a specified cost function;
• and
• optimizing parameters that characterize a behavior of the diffusion model with an aim of improving the evaluation that uses the cost function during further processing of training images and samples generated from the training images;
• wherein, when drawing the samples, and/or when evaluating the predictions generated from the drawn samples using the cost function, those samples that still reflect the style of the respective training image are represented more strongly, the more closely the respective training image matches the specified style.

One or more computers and/or compute instances including a non-transitory machine-readable data carrier on which is stred a computer program including machine-readable instructions for training a diffusion model, which can be used to iteratively generate a synthetic image from noise in conjunction with a specified conditioning, the synthetic image being consistent with the conditioning, the instructions, when executed by the one or more computers and/or compute instances, cause the one or more computers and/or compute instances to perform the following steps:

• specifying a style that the synthetically generated images should have;
• providing a set of training images that match the specified style to varying degrees;
• successively applying noise to each respective training image of the training images in a specified number of iterations, so that respective noised versions are created;
• drawing samples from the respective noised versions;
• processing each of the drawn samples by the diffusion model in conjunction with the specified conditioning to produce predictions for a previous noised version in each case;
• evaluating a correspondence between the predictions and the noised versions using a specified cost function;
• and
• optimizing parameters that characterize a behavior of the diffusion model with an aim of improving the evaluation that uses the cost function during further processing of training images and samples generated from the training images;
• wherein, when drawing the samples, and/or when evaluating the predictions generated from the drawn samples using the cost function, those samples that still reflect the style of the respective training image are represented more strongly, the more closely the respective training image matches the specified style.