In the field of image editing, Null-text Inversion (NTI) enables fine-grained
editing while preserving the structure of the original image by optimizing null
embeddings during the DDIM sampling process. However, the NTI process is
time-consuming, taking more than two minutes per image. To address this, we
introduce an innovative method that maintains the principles of the NTI while
accelerating the image editing process. We propose the WaveOpt-Estimator, which
determines the text optimization endpoint based on frequency characteristics.
Utilizing wavelet transform analysis to identify the image's frequency
characteristics, we can limit text optimization to specific timesteps during
the DDIM sampling process. By adopting the Negative-Prompt Inversion (NPI)
concept, a target prompt representing the original image serves as the initial
text value for optimization. This approach maintains performance comparable to
NTI while reducing the average editing time by over 80% compared to the NTI
method. Our method presents a promising approach for efficient, high-quality
image editing based on diffusion models.

通过使用 WaveOpt-Estimator 和 Negative-Prompt Inversion 等创新方法以及波形变换分析，本研究提出了一种在保持图像结构的同时加速图像编辑过程的方法，相对于传统 Null-text Inversion 方法平均编辑时间减少超过 80%，为基于扩散模型的高效、高质量图像编辑研究提供了有希望的方法。

基于小波引导的扩散图像编辑中的文本反转加速

Wavelet-Guided Acceleration of Text Inversion in Diffusion-Based Image  Editing

DDIM inversion has revealed the remarkable potential of real image editing
within diffusion-based methods. However, the accuracy of DDIM reconstruction
degrades as larger classifier-free guidance (CFG) scales being used for
enhanced editing. Null-text inversion (NTI) optimizes null embeddings to align
the reconstruction and inversion trajectories with larger CFG scales, enabling
real image editing with cross-attention control. Negative-prompt inversion
(NPI) further offers a training-free closed-form solution of NTI. However, it
may introduce artifacts and is still constrained by DDIM reconstruction
quality. To overcome these limitations, we propose Proximal Negative-Prompt
Inversion (ProxNPI), extending the concepts of NTI and NPI. We enhance NPI with
a regularization term and reconstruction guidance, which reduces artifacts
while capitalizing on its training-free nature. Our method provides an
efficient and straightforward approach, effectively addressing real image
editing tasks with minimal computational overhead.

本文提出了 ProxNPI 方法，通过将 NTI 和 NPI 的概念扩展为一种正则化术语和重建指导来优化 NPI，从而实现对真实图像进行编辑的任务，并在保持训练免费的同时降低了人工伪影。

通过近端引导改进负倒装

Improving Negative-Prompt Inversion via Proximal Guidance

In image editing employing diffusion models, it is crucial to preserve the
reconstruction quality of the original image while changing its style. Although
existing methods ensure reconstruction quality through optimization, a drawback
of these is the significant amount of time required for optimization. In this
paper, we propose negative-prompt inversion, a method capable of achieving
equivalent reconstruction solely through forward propagation without
optimization, thereby enabling much faster editing processes. We experimentally
demonstrate that the reconstruction quality of our method is comparable to that
of existing methods, allowing for inversion at a resolution of 512 pixels and
with 50 sampling steps within approximately 5 seconds, which is more than 30
times faster than null-text inversion. Reduction of the computation time by the
proposed method further allows us to use a larger number of sampling steps in
diffusion models to improve the reconstruction quality with a moderate increase
in computation time.

本文提出一种负激励反演方法，通过前向传播实现等效重建来加速处理图像编辑中的扩散模型，实验证明与现有方法相比，我们的方法的重建质量是可比的，更快，可用于改善扩散模型的重建质量。