KV Inversion：面向文本条件真实图像动作编辑的 KV 嵌入学习
KV Inversion: KV Embeddings Learning for Text-Conditioned Real Image Action Editing
AI总结：
针对文本条件真实图像编辑中的动作语义匹配与身份纹理保持难题，提出无需训练 Stable Diffusion 或大规模数据训练的 KV Inversion 方法。
摘要：
文本条件图像编辑是一项近年出现且极具实用性的任务，其潜力不可估量。然而，目前大多数同类方法无法进行动作编辑，也就是说，它们不能生成既符合编辑提示的动作语义、又保留原始图像内容的结果。为解决动作编辑问题，我们提出 KV Inversion，这是一种能够取得令人满意的重建性能和动作编辑效果的方法，可解决两大问题：1）编辑结果能够匹配相应动作；2）被编辑对象能够保留原始真实图像的纹理和身份。此外，我们的方法既不需要训练 Stable Diffusion 模型本身，也不需要扫描大规模数据集来进行耗时的训练。
英文摘要：
Text-conditioned image editing is a recently emerged and highly practical task, and its potential is immeasurable. However, most of the concurrent methods are unable to perform action editing, i.e. they can not produce results that conform to the action semantics of the editing prompt and preserve the content of the original image. To solve the problem of action editing, we propose KV Inversion, a method that can achieve satisfactory reconstruction performance and action editing, which can solve two major problems: 1) the edited result can match the corresponding action, and 2) the edited object can retain the texture and identity of the original real image. In addition, our method does not require training the Stable Diffusion model itself, nor does it require scanning a large-scale dataset to perform time-consuming training.
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。
