Text-conditional image editing is a very useful task that has recently
emerged with immeasurable potential. Most current real image editing methods
first need to complete the reconstruction of the image, and then editing is
carried out by various methods based on the reconstruction. Most methods use
DDIM Inversion for reconstruction, however, DDIM Inversion often fails to
guarantee reconstruction performance, i.e., it fails to produce results that
preserve the original image content. To address the problem of reconstruction
failure, we propose FEC, which consists of three sampling methods, each
designed for different editing types and settings. Our three methods of FEC
achieve two important goals in image editing task: 1) ensuring successful
reconstruction, i.e., sampling to get a generated result that preserves the
texture and features of the original real image. 2) these sampling methods can
be paired with many editing methods and greatly improve the performance of
these editing methods to accomplish various editing tasks. In addition, none of
our sampling methods require fine-tuning of the diffusion model or
time-consuming training on large-scale datasets. Hence the cost of time as well
as the use of computer memory and computation can be significantly reduced.

文本条件图像编辑是一项非常有用且具有巨大潜力的任务。我们提出的 FEC 通过三种采样方法解决了重构失败的问题，从而实现了成功重构并提升了图像编辑任务的性能。

FEC: 提升真实图像编辑一致性的三种无微调方法

FEC: Three Finetuning-free Methods to Enhance Consistency for Real Image  Editing

Recently, different methods have been proposed to address the inconsistency
issue of DDIM inversion to enable image editing, such as EDICT [36] and
Null-text inversion [22]. However, the above methods introduce considerable
computational overhead. In this paper, we propose a new technique, named
bi-directional integration approximation (BDIA), to perform exact diffusion
inversion with neglible computational overhead. Suppose we would like to
estimate the next diffusion state $\boldsymbol{z}_{i-1}$ at timestep $t_i$ with
the historical information $(i,\boldsymbol{z}_i)$ and
$(i+1,\boldsymbol{z}_{i+1})$. We first obtain the estimated Gaussian noise
$\hat{\boldsymbol{\epsilon}}(\boldsymbol{z}_i,i)$, and then apply the DDIM
update procedure twice for approximating the ODE integration over the next
time-slot $[t_i, t_{i-1}]$ in the forward manner and the previous time-slot
$[t_i, t_{t+1}]$ in the backward manner. The DDIM step for the previous
time-slot is used to refine the integration approximation made earlier when
computing $\boldsymbol{z}_i$. One nice property with BDIA-DDIM is that the
update expression for $\boldsymbol{z}_{i-1}$ is a linear combination of
$(\boldsymbol{z}_{i+1}, \boldsymbol{z}_i,
\hat{\boldsymbol{\epsilon}}(\boldsymbol{z}_i,i))$. This allows for exact
backward computation of $\boldsymbol{z}_{i+1}$ given $(\boldsymbol{z}_i,
\boldsymbol{z}_{i-1})$, thus leading to exact diffusion inversion. Experiments
on both image reconstruction and image editing were conducted, confirming our
statement.
BDIA can also be applied to improve the performance of other ODE solvers in
addition to DDIM. In our work, it is found that applying BDIA to the EDM
sampling procedure produces slightly better FID score over CIFAR10.

提出了一种新技术，名为双向积分逼近（BDIA），以实现可忽略的计算开销的精确扩散反演。该技术在图像编辑方面具有重要意义，同时还可以改善其他 ODE 求解器的性能。

双向积分逼近下的精确扩散反演

Exact Diffusion Inversion via Bi-directional Integration Approximation

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。