Despite recent advances in inversion-based editing, text-guided image
manipulation remains challenging for diffusion models. The primary bottlenecks
include 1) the time-consuming nature of the inversion process; 2) the struggle
to balance consistency with accuracy; 3) the lack of compatibility with
efficient consistency sampling methods used in consistency models. To address
the above issues, we start by asking ourselves if the inversion process can be
eliminated for editing. We show that when the initial sample is known, a
special variance schedule reduces the denoising step to the same form as the
multi-step consistency sampling. We name this Denoising Diffusion Consistent
Model (DDCM), and note that it implies a virtual inversion strategy without
explicit inversion in sampling. We further unify the attention control
mechanisms in a tuning-free framework for text-guided editing. Combining them,
we present inversion-free editing (InfEdit), which allows for consistent and
faithful editing for both rigid and non-rigid semantic changes, catering to
intricate modifications without compromising on the image's integrity and
explicit inversion. Through extensive experiments, InfEdit shows strong
performance in various editing tasks and also maintains a seamless workflow
(less than 3 seconds on one single A40), demonstrating the potential for
real-time applications. Project Page: this https URL

对于扩散模型，尽管反演性编辑取得了一定进展，但基于文本的图像编辑仍面临困难。本研究提出了一种无需显式反演的编辑方法（InfEdit），通过引入特殊方差调度和统一的注意力控制机制，实现了对图像的稳定编辑和真实还原，且在各种编辑任务中表现出强大的性能和快速的实时应用潜力。

无需反转的图像编辑与自然语言

Inversion-Free Image Editing with Natural Language

Text-guided diffusion models have revolutionized image generation and
editing, offering exceptional realism and diversity. Specifically, in the
context of diffusion-based editing, where a source image is edited according to
a target prompt, the process commences by acquiring a noisy latent vector
corresponding to the source image via the diffusion model. This vector is
subsequently fed into separate source and target diffusion branches for
editing. The accuracy of this inversion process significantly impacts the final
editing outcome, influencing both essential content preservation of the source
image and edit fidelity according to the target prompt. Prior inversion
techniques aimed at finding a unified solution in both the source and target
diffusion branches. However, our theoretical and empirical analyses reveal that
disentangling these branches leads to a distinct separation of responsibilities
for preserving essential content and ensuring edit fidelity. Building on this
insight, we introduce "Direct Inversion," a novel technique achieving optimal
performance of both branches with just three lines of code. To assess image
editing performance, we present PIE-Bench, an editing benchmark with 700 images
showcasing diverse scenes and editing types, accompanied by versatile
annotations and comprehensive evaluation metrics. Compared to state-of-the-art
optimization-based inversion techniques, our solution not only yields superior
performance across 8 editing methods but also achieves nearly an order of
speed-up.

文本引导扩散模型通过分离源图像和目标扩散分支，实现图像编辑的内容保留和编辑保真度优化，通过三行代码实现的直接反演技术在编辑基准测试中表现出卓越的性能和极大的加速。