Recent large-scale text-guided diffusion models provide powerful
image-generation capabilities. Currently, a significant effort is given to
enable the modification of these images using text only as means to offer
intuitive and versatile editing. However, editing proves to be difficult for
these generative models due to the inherent nature of editing techniques, which
involves preserving certain content from the original image. Conversely, in
text-based models, even minor modifications to the text prompt frequently
result in an entirely distinct result, making attaining one-shot generation
that accurately corresponds to the users intent exceedingly challenging. In
addition, to edit a real image using these state-of-the-art tools, one must
first invert the image into the pre-trained models domain - adding another
factor affecting the edit quality, as well as latency. In this exploratory
report, we propose LEDITS - a combined lightweight approach for real-image
editing, incorporating the Edit Friendly DDPM inversion technique with Semantic
Guidance, thus extending Semantic Guidance to real image editing, while
harnessing the editing capabilities of DDPM inversion as well. This approach
achieves versatile edits, both subtle and extensive as well as alterations in
composition and style, while requiring no optimization nor extensions to the
architecture.

LEDITS 是一种轻量级的图像编辑方法，结合了 Edit Friendly DDPM 反演技术和语义引导，能够实现对真实图像的细微和广泛编辑，而无需优化和扩展架构。

使用 DDPM 反演和语义指导的实际图像编辑 LEDITS

LEDITS: Real Image Editing with DDPM Inversion and Semantic Guidance

We address the problem of reposing an image of a human into any desired novel
pose. This conditional image-generation task requires reasoning about the 3D
structure of the human, including self-occluded body parts. Most prior works
are either based on 2D representations or require fitting and manipulating an
explicit 3D body mesh. Based on the recent success in deep learning-based
volumetric representations, we propose to implicitly learn a dense feature
volume from human images, which lends itself to simple and intuitive
manipulation through explicit geometric warping. Once the latent feature volume
is warped according to the desired pose change, the volume is mapped back to
RGB space by a convolutional decoder. Our state-of-the-art results on the
DeepFashion and the iPER benchmarks indicate that dense volumetric human
representations are worth investigating in more detail.

本研究基于深度学习的体积表示方法，提出利用人体图片，通过几何变形的方式隐式学习稠密特征体积，并通过卷积解码器将特征体积映射回 RGB 空间，实现了人体图像的任意变形，取得了着装识别和人像表情生成方面的最新研究成果。