LayerDiffusion：使用扩散模型进行分层可控图像编辑
LayerDiffusion: Layered Controlled Image Editing with Diffusion Models
AI总结：
针对多操作图像编辑中主体与背景一致性难题，提出 LayerDiffusion 分层可控方法，结合分层扩散训练与迭代引导，实现高质量文本引导编辑并优于现有方法。
摘要：
文本引导的图像编辑近来经历了快速发展。然而，在单张图像上同时执行多种编辑操作（例如背景替换和特定主体属性更改），同时保持主体与背景之间的一致性，仍然具有挑战性。在本文中，我们提出了 LayerDiffusion，这是一种基于语义的分层可控图像编辑方法。我们的方法能够对特定主体进行非刚性编辑和属性修改，同时保留其独特特征，并将它们无缝融合到新背景中。我们利用大规模文本到图像模型，并采用分层可控优化策略，结合分层扩散训练。在扩散过程中，使用迭代引导策略生成与文本描述相符的最终图像。实验结果证明了我们的方法在生成与给定文本描述高度一致的高连贯性图像方面的有效性。经过编辑的图像与输入图像的特征保持高度相似，并且性能超越了当前领先的图像编辑方法。LayerDiffusion 为可控图像编辑开辟了新的可能性。
英文摘要：
Text-guided image editing has recently experienced rapid development. However, simultaneously performing multiple editing actions on a single image, such as background replacement and specific subject attribute changes, while maintaining consistency between the subject and the background remains challenging. In this paper, we propose LayerDiffusion, a semantic-based layered controlled image editing method. Our method enables non-rigid editing and attribute modification of specific subjects while preserving their unique characteristics and seamlessly integrating them into new backgrounds. We leverage a large-scale text-to-image model and employ a layered controlled optimization strategy combined with layered diffusion training. During the diffusion process, an iterative guidance strategy is used to generate a final image that aligns with the textual description. Experimental results demonstrate the effectiveness of our method in generating highly coherent images that closely align with the given textual description. The edited images maintain a high similarity to the features of the input image and surpass the performance of current leading image editing methods. LayerDiffusion opens up new possibilities for controllable image editing.
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。
