Text-to-Image editing using diffusion models faces challenges in balancing content preservation with edit application and handling real-image editing. To address these, we propose LAMS-Edit, leveraging intermediate states from the inversion process--an essential step in real-image editing--during edited image generation. Specifically, latent representations and attention maps from both processes are combined at each step using weighted interpolation, controlled by a scheduler. This technique, Latent and Attention Mixing with Schedulers (LAMS), integrates with Prompt-to-Prompt (P2P) to form LAMS-Edit--an extensible framework that supports precise editing with region masks and enables style transfer via LoRA. Extensive experiments demonstrate that LAMS-Edit effectively balances content preservation and edit application.

翻译：基于扩散模型的文本到图像编辑在平衡内容保持与编辑应用以及处理真实图像编辑方面面临挑战。为解决这些问题，我们提出LAMS-Edit方法，该方法在编辑图像生成过程中利用反转过程（真实图像编辑的关键步骤）的中间状态。具体而言，通过调度器控制的加权插值，在每一步将两个过程的潜在表示和注意力图进行融合。这种潜在表示与注意力混合调度（LAMS）技术与Prompt-to-Prompt（P2P）相结合，形成了LAMS-Edit——一个支持通过区域掩码进行精确编辑、并能够借助LoRA实现风格迁移的可扩展框架。大量实验表明，LAMS-Edit能有效平衡内容保持与编辑应用。