  People often create art by following an artistic workflow involving multiple
stages that inform the overall design. If an artist wishes to modify an earlier
decision, significant work may be required to propagate this new decision
forward to the final artwork. Motivated by the above observations, we propose a
generative model that follows a given artistic workflow, enabling both
multi-stage image generation as well as multi-stage image editing of an
existing piece of art. Furthermore, for the editing scenario, we introduce an
optimization process along with learning-based regularization to ensure the
edited image produced by the model closely aligns with the originally provided
image. Qualitative and quantitative results on three different artistic
datasets demonstrate the effectiveness of the proposed framework on both image
generation and editing tasks.
