Automatic generation of graphic designs has recently received considerable attention. However, the state-of-the-art approaches are complex and rely on proprietary datasets, which creates reproducibility barriers. In this paper, we propose an open framework for automatic graphic design called OpenCOLE, where we build a modified version of the pioneering COLE and train our model exclusively on publicly available datasets. Based on GPT4V evaluations, our model shows promising performance comparable to the original COLE. We release the pipeline and training results to encourage open development.

翻译：自动生成图形设计近来受到广泛关注。然而，当前最先进的方法复杂且依赖专有数据集，这造成了可复现性的障碍。本文提出一个名为OpenCOLE的开放式自动图形设计框架，我们构建了开创性COLE的改进版本，并仅使用公开数据集训练模型。基于GPT4V评估，我们的模型展现出与原始COLE相当的有前景的性能。我们公开了流程和训练结果，以促进开放性发展。