This paper strives for image editing via generative models. Flow Matching is an emerging generative modeling technique that offers the advantage of simple and efficient training. Simultaneously, a new transformer-based U-ViT has recently been proposed to replace the commonly used UNet for better scalability and performance in generative modeling. Hence, Flow Matching with a transformer backbone offers the potential for scalable and high-quality generative modeling, but their latent structure and editing ability are as of yet unknown. Hence, we adopt this setting and explore how to edit images through latent space manipulation. We introduce an editing space, which we call $u$-space, that can be manipulated in a controllable, accumulative, and composable manner. Additionally, we propose a tailored sampling solution to enable sampling with the more efficient adaptive step-size ODE solvers. Lastly, we put forth a straightforward yet powerful method for achieving fine-grained and nuanced editing using text prompts. Our framework is simple and efficient, all while being highly effective at editing images while preserving the essence of the original content. Our code will be publicly available at https://taohu.me/lfm/

通过流匹配和变压器（U-ViT）的结合，实现了简单高效的图像编辑方法，大大提高了生成模型的可扩展性和性能，并提供了对潜在结构和编辑能力的探索。通过引入一个名为$u$-space的编辑空间以及针对ODE求解器的适应性抽样解决方案，实现了可控、累积和可组合的图像编辑。此外，使用文本提示的简洁而强大的方法实现细粒度和细致的图像编辑。这个框架既简单高效，同时又能保留图像原始内容的本质。

基于Transformer的流匹配中的潜变空间编辑