  Large data-driven image models are extensively used to support creative and
artistic work. Under the currently predominant distribution-fitting paradigm, a
dataset is treated as ground truth to be approximated as closely as possible.
Yet, many creative applications demand a diverse range of output, and creators
often strive to actively diverge from a given data distribution. We argue that
an adjustment of modelling objectives, from pure mode coverage towards mode
balancing, is necessary to accommodate the goal of higher output diversity. We
present diversity weights, a training scheme that increases a model's output
diversity by balancing the modes in the training dataset. First experiments in
a controlled setting demonstrate the potential of our method. We discuss
connections of our approach to diversity, equity, and inclusion in generative
machine learning more generally, and computational creativity specifically. An
implementation of our algorithm is available at
https://github.com/sebastianberns/diversity-weights
