ParallelEdits：高效的多对象图像编辑
ParallelEdits: Efficient Multi-object Image Editing
AI总结：
本文提出ParallelEdits方法，通过注意力分配机制和多分支设计实现高效的多对象多属性同时图像编辑，并推出PIE-Bench++基准数据集以支持多对象编辑评估。
摘要：
随着扩散模型的发展，文本驱动的图像合成取得了显著进展，改变了从文本提示生成视觉内容的方式。尽管取得了这些进展，文本驱动的图像编辑作为计算机图形学中的一个关键领域，仍面临独特挑战。一个主要挑战是同时跨多个对象或属性进行编辑。将这些方法按顺序应用于多属性编辑会增加计算需求并降低效率。在本文中，我们以重要贡献应对这些挑战。我们的主要贡献是开发了ParallelEdits，一种无缝管理跨多个属性同时编辑的方法。与先前方法相比，ParallelEdits不仅保持了单属性编辑的质量，还显著提升了多任务编辑的性能。这是通过创新的注意力分配机制和在多个处理头上运行的多分支设计实现的。此外，我们引入了PIE-Bench++数据集，它是原始PIE-Bench数据集的扩展，以更好地支持评估同时涉及多个对象和属性的图像编辑任务。该数据集是在多方面场景中评估文本驱动图像编辑方法的基准。
英文摘要：
Text-driven image synthesis has made significant advancements with the development of diffusion models, transforming how visual content is generated from text prompts. Despite these advances, text-driven image editing, a key area in computer graphics, faces unique challenges. A major challenge is making simultaneous edits across multiple objects or attributes. Applying these methods sequentially for multi-attribute edits increases computational demands and efficiency losses. In this paper, we address these challenges with significant contributions. Our main contribution is the development of ParallelEdits, a method that seamlessly manages simultaneous edits across multiple attributes. In contrast to previous approaches, ParallelEdits not only preserves the quality of single attribute edits but also significantly improves the performance of multitasking edits. This is achieved through innovative attention distribution mechanism and multi-branch design that operates across several processing heads. Additionally, we introduce the PIE-Bench++ dataset, an expansion of the original PIE-Bench dataset, to better support evaluating image-editing tasks involving multiple objects and attributes simultaneously. This dataset is a benchmark for evaluating text-driven image editing methods in multifaceted scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
