Abstract
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.
Abstract (translated)
基于扩散模型的文本到图像编辑面临在内容保留和编辑应用之间取得平衡以及处理真实图像编辑的挑战。为了解决这些问题，我们提出了LAMS-Edit方法，该方法利用了反转过程中产生的中间状态——这是真实图像编辑中的一个关键步骤。具体而言，在生成编辑后的图像时，该方法结合了来自这两个过程（即原始编辑和反转过程）在每一步的潜在表示和注意力图，并通过加权插值进行组合，其权重由调度器控制。这一技术被称为Latent and Attention Mixing with Schedulers (LAMS)，并与Prompt-to-Prompt (P2P)集成以形成LAMS-Edit——一个支持区域掩码精确编辑并可通过LoRA实现风格转换的可扩展框架。广泛的实验表明，LAMS-Edit在内容保留和编辑应用之间实现了有效的平衡。
URL
https://arxiv.org/abs/2601.02987
