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.

中文翻译：

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为图像生成和编辑建模艺术工作流

人们通常通过遵循涉及整个设计的多个阶段的艺术工作流程来创作艺术。如果艺术家希望修改先前的决定，则可能需要进行大量工作才能将此新决定传播到最终艺术品。受上述观察的启发，我们提出了一个遵循给定艺术工作流程的生成模型，支持多阶段图像生成以及现有艺术作品的多阶段图像编辑。此外，对于编辑场景，我们引入了优化过程以及基于学习的正则化，以确保模型生成的编辑图像与原始提供的图像紧密对齐。