  We present a framework for automating generative deep learning with a
specific focus on artistic applications. The framework provides opportunities
to hand over creative responsibilities to a generative system as targets for
automation. For the definition of targets, we adopt core concepts from
automated machine learning and an analysis of generative deep learning
pipelines, both in standard and artistic settings. To motivate the framework,
we argue that automation aligns well with the goal of increasing the creative
responsibility of a generative system, a central theme in computational
creativity research. We understand automation as the challenge of granting a
generative system more creative autonomy, by framing the interaction between
the user and the system as a co-creative process. The development of the
framework is informed by our analysis of the relationship between automation
and creative autonomy. An illustrative example shows how the framework can give
inspiration and guidance in the process of handing over creative
responsibility.
