EasyInv: Toward Fast and Better DDIM Inversion
Authors: Ziyue Zhang
Mingbao Lin
Shuicheng YAN
Rongrong Ji
Venue: ICML 2025 poster (2025)
Keywords: Inversion
Diffusion
Primary area: deep_learning->generative_models_and_autoencoders
TL;DR: We propose a novel and efficient inversion method that achieves strong performance without extra computation, and can be seamlessly integrated into nearly all existing frameworks with just three lines of code.

Abstract: This paper introduces EasyInv, an easy yet novel approach that significantly advances the field of DDIM Inversion by addressing the inherent inefficiencies and performance limitations of traditional iterative optimization methods. At the core of our EasyInv is a refined strategy for approximating inversion noise, which is pivotal for enhancing the accuracy and reliability of the inversion process. By prioritizing the initial latent state, which encapsulates rich information about the original images, EasyInv steers clear of the iterative refinement of noise items. Instead, we introduce a methodical aggregation of the latent state from the preceding time step with the current state, effectively increasing the influence of the initial latent state and mitigating the impact of noise. We illustrate that EasyInv is capable of delivering results that are either on par with or exceed those of the conventional DDIM Inversion approach, especially under conditions where the model's precision is limited or computational resources are scarce. Concurrently, our EasyInv offers an approximate threefold enhancement regarding inference efficiency over off-the-shelf iterative optimization techniques. It can be easily combined with most existing inversion methods by only four lines of code. See code at https://github.com/potato-kitty/EasyInv.

EasyInv: Toward Fast and Better DDIM Inversion
Ziyue Zhang" Mingbao Lin
Shuicheng Yan 32
Rongrong Ji
Abstract
This paper introduces Easylnv, an easy yet novel approach that significantly advances the field of DDIM Inversion by addressing the inherent in- efficiencies and performance limitations of tradi- tional iterative optimization methods. At the core of our EasyInv is a refined strategy for approximat- ing inversion noise, which pivotal for enhancing the accuracy and reliability of the inversion pro- cess. By prioritizing the initial latent state, which encapsulates rich information about the original images, Easylnv steers clear of the iterative re- finement of noise items. Instead, we introduce a methodical aggregation the latent state from the preceding time step with the current state, ef- fectively increasing the influence of the initial latent state and mitigating the impact of noise. We illustrate that EasyInv is capable of delivering results that are either on with or exceed those of the conventional DDIM Inversion approach, especially under conditions where the model's precision is limited or computational resources are scarce. Concurrently, our Easylnv offers an approximate threefold enhancement regarding in- ference efficiency over off-the-shelf iterative op- timization techniques. See code at https: github. com/ pwtp:totttoooooo Kitty/Eattttyttt Inv.
40F undn
Figure I. Comparison of inversion methods including vanilla DDIM Inversion (Couairon et al., 2023), Fixed-Point lteration (Pan et al., 2023), ReNoise (Garibi et al., 2024) and Easylnv.
model gaining popularity is DALL-E 3 (Betker et al., 2023), which offers users access to its API and the ability to inter- act with it through platforms like ChatGPT (Openai, 2024). These models have transformed the visual arts industry and have attracted substantial attention from the research com- munity. While renowned generative diffusion models have made strides, prevalent limitation is their reliance tex- tual prompts for input. This approach becomes restrictive when users seek to iteratively refine an image, as the sole reliance on prompts hinders ficxibility. Although solutions such as ObjectAdd (Zhang et al., 2024) and P2P (Hertz et al., 2022) have been proposed address image editing challenges, they still confined to the realm of prompted image manipulation. Given that diffusson models generate images from noise inputs, potential breakthrough lies in identifying the corresponding noise for any given image. This would enable the diffusion model to initiate the genera- tion process from a known starting point, thereby allowing for precise control over the final output. The recent innova- tion of DDIM Inversion (Couairon al., 2023) overcomes this by reversing the denoising process to introduce noise. This technique retrieves the initial noise configuration after series of reference steps, thereby preserving the integrity of the original image while affording the user the ability to manipulate the output by adjusting the denoising parameters. With DDIM inversion, the generative process becomes more adaptable, facilitating the creation and subsequent editing of images with greater precision and control. For example, MasaCtrl (Cao et al., 2023) first transforms real image into noise representation and then identifies the arrangement of objects during the denoising phase. Portrait Diffusion (Liu et al., 2023) inverts both the source and target images. merges their respective Q, K and V values for mixups.
1. Introduction
Diffusion models renowned for their ability to gener- ate high-quality images that closely match given prompts. Among the many diffusion models, Stable Diffusion (SD) (Rombach et al., 2022) stands out as one of the most widely utilized models. Another contemporary diffusion
'Equal contribution Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, P.R. China *Skywork AI, Singapore Natioonal University of Singapore. Correspondence to: Rongrong Ji meiiximmu.ducc....
Proceedings of the ndnd International Conference Machine Learning, Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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EasyInv: Toward Fast and Better DDIM Inversion
Considering the reliance on inversion techniques preserve the integrity of the input image, the quality of the inversion process is paramount, as it profoundly influences subsequent tasks. As depicted in Figure I(a), the performance of DDIM Inversion has been found to be less than satisfactory due to the discrepancy between the noise estimated during the inversion process and the noise expected in the sampling pro- cess, Consequently, numerous studies have been conducted to enhance its efficacy. In Null-Text inversion (Mokady et al., 2023), researchers observed that using null prompt as input, the diffusion model could generate optimal results during inversion, suggesting that improvements to inversion might be better achieved in the reconstruction branch. Ju et al.ss work (Ju et al., 2023) exemplifies this approach by cal- culating the distance between latents at the current step and the previous step. PTI (Dong et al., 2023) opts to update the conditional vector in each step guide the reconstruction branch for improving consistency. ReNoise (Garibi et al., 2024) focuses on refining the inversion process itself. This method iteratively adds and then denoises noise at each time step, using the denoised noise as input for the subsequent iteration. However, as shown in Figure 1(b), it can result in a black image output when dealing with certain special inputs, which will be discussed in detail in Section 4. Pan et al. (Pan et al., 2023), while maintaining the iterative updating pro- cess, also amalgamated noise from previous steps with the current step's noise. However, this method's performance is limited in less effective models as displayed in Figure I(c). For instance, it performs well in SD-XL (Podell al., 2023) but fails to yield proper results in SD-VI-4 (Rombach et al., 2022). We attribute this their sole focus on optimizing noise; when the noise is highly inaccurate, such simple opti- mization strategies encounter difficulties. Additionally, the iterative updating of noise is time-consuming, as Pan et al. 's method requires multiple model inferences per time step.
Figure 2. Visualization of latent states at midway denoising steps for various inversion methods. Our Easylnv shows enhanced con- vergence by closely approximating the original image.
Figure ((d). Furthermore, by building upon the traditional DDIM Inversion framework (Couairon et al., 2023), EasyInv does not depend on iterative optimization between adjacent steps, thus enhancing computational efficiency. In Figure 2, we present a visualization of the latent states at the midpoint of the total denoising steps for various inversion methods. The outcomes of our Easylnv are more closely aligned with the original image compared to all other methods, demon- strating that Easylnv achieves faster convergence.
2. Related Works
Diffusion Model. In recent years, there has been sig- nificant progress in the field of generative models, with diffusion models emerging as a particularly popular ap- proach. The seminal denoising diffusion probabilistic mod- els (DDPM) (Ho et al., 2020) introduced a practical frame- work for image generation based on the diffusion process. This method stands out from its predecessors, such as gen- crative adversarial networks (GANs), due to its iterative nature. During the data preparation phase, Gaussian noise incrementally added to a real image until it transitions into state that is indistinguishable from raw Gaussian noise. Subsequently, a model can be trained to predict the noise added at each step, enabling users to input any Gaussian noise and obtain a high-quality image as a result. Ho et al. (Ho et al., 2020) provided a robust theoretical foundation for their model, which has facilitated further advancements. Generative process in DDPM is both time-consuming and inherently stochastic due to the random noise introduced at each step. To address these limitations, the denoising diffusion implicit models (DDIM) were developed (Song et al., 2020). By reformulating DDPM, DDIM has reduced the amount of random noise added at each step. This refor- mulation results in more deterministic denoising process. Furthermore, the absence of random noise allows for the aggregation of several denoising steps, thereby reducing the overall computation time required to generate an image.
In this paper, we conduct an ie-depth analysis and recognize that the foundation of any inversion process is the initial latent state derived from real image. Errors introduced at each step of the inversion process can accumulate, lead- ing to suboptimal reconstruction. Current methodologies, which focus on optimizing the transition between successive steps, may not be adequate to address this issue holistically. To tackle this, we propose a novel approach that considers the inversion process as a whole, underscoring the signifi- cance of the initial latent state throughout the process. Our approach, named Easylnv, incorporates a straightforward mechanism to periodically reinforce the influence of the initial latent state during the inversion. This is realized by blending the current latent state with the previous one at strategically selected intervals, thereby increasing the weight of the initial latent state and diminishing the noise's 's impact. As result, EasyInv ensures reconstructed version that remains closer to the original image, as illustrated in
Image Inversion. Converting a real image into noise is piv- otal in real image editing using diffusion models. The preci- sion of this process has a profound impact in the final edit, with the critical element being the accurate identification
2

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EasyInv: Toward Fast and Better DDIM Inversion
of the noise added each step. Couairon al. (Couairon et al., 2023) swapped the roles of independent and implicit variables within the denoising function of the DDIM model, enabling it to predict the noise that should be introduced to the current latents. However, it is essential to recognize that the denoising step in a diffusion model is inherently an approximation, and when this approximation utilized inversely, discrepancies between the model's output and the actual noise value are likely to be exacerbated. To address this issue, ReNoise (Garibi et al., 2024) iterates through each noising step multiple times. For each inversion step, they employ an iterative approach to add and subsequently reduce noise, with the noise reduced in the final iteration being carried forward to the subsequent iteration. Pan el al. (Pan et al., 2023) offered a theoretical underpinning to the ReNoise. Iterative optimization from ReNoise is clas- sified under the umbrella of fixed-point iteration methods. Building upon Anderson's 's seminal work (Anderson, I 1965), Pan et al. have advanced the field by proposing their novel method for optimizing noise during the inversion process.
the noise zip. Starting with 2r Ziy- we proceed with denoising process in Equation (1) to infer approximate reconstruction Zo that resembles the original latent zú. The primary source of enror in this reconstruction arises from the difference between the noise predicted during the inversion process Eo (z 1.t - 1, (v)) and the noise expected in the sampling process, Eg (Ze: (z 4, (v))), denoted as ét, at each iterative step. This discrepancy originates from an imprecise approximation of the time step from & to t- 1. Therefore, reducing the discrepancy between the predicted noises at each step is crucial for achieving accurate reconstruc- tion, which is essential for the success of subsequent image editing tasks. For simplicity below, we define:
e se (z To()), E Eo (z., to(y).. (4)
3.2. Fixed-Point Iteration
The vanilla DDIM Inversion method, as discussed, involves approximation that is not entirely precise for s². To ad- dress this, researchers have sought to refine a more accurate approximation of ef, thereby ensuring that the desired con- ditions are optimally met. This refinement process aims to enhance the precision of the method, leading to more reliable results in the context of the application:
3. Methodology
3.1. Preliminaries
DDIM Inversion. Let Zy denote noise tensor with Zr
I). The DDIM (Couairon et al., 2023) leverages a pre- trained neural network eo to perform T denoising diffusion steps. Each step aims to estimate the underlying noise and subsequently restore a less noisy version of the tensor, 2(-1, from its noisy counterpart 2, as:
Et
(5)
For clarity, let's first restate Equation (3) as follows:
z gEE)),
(6)
Q:-1
Zt-
2:
which represents the introduction of adding noise to the latent state zi-1- Under the assumption of Equation (5), it should be the case that:
C2
(1)
V
1
a1-1
at
2 = z
where 1 = T + 1, and {or)__ constitutes prescribed variance set that guides the diffusion process. Furthermore, Ta serves as an intermediate representation that encapsulates the textual condition y. We denote:
Subsequently, by employing the noise estimation function from Equation (2), we obtain:
d(z,) co Zz, t, To (v)).
(2)
d(z.) = d(g(e))).
(8)
Re-evaluating Equation (1), we derive DDIM Inversion pro- cess (Couairon et al., 2023) as presented in Equation (3). In this reformulation, we relocate an approximate 2 to the left-hand side, resulting in the following expression:
Combining d(z.) and Equation (5), we obtain:
E = d(g(=))).
(9)
: = y (z 1, ro()))
This formulation presents a fixed-point problem, which per- tains to value that remains unchanged under specific transformation (Bauschke et al., 2011). In the context of functions, a fixed point is an element that is invariant under the application of the function. We seek a o that, when transformed by g and followed by d, can map back to itself, signifying an optimal solution as per Equation (5).
a 1-
(3)
a -1) co -
a--1
Review. Given an image I", after encoding it into the latent 2ú, we initiate 7 inversion steps using Equation (3) to obtain
3

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EasyInv: Toward Fast and Better DDIM Inversion
Fixed-point iteration is a computational technique designed identify the fixed points of function. It functions through an iterative process, as delineated below:
yk Hag + Uk-
(13)
In these equations, 2y represents the true value at time-step k. which we aim to estimate; Wk-1 is the control input at time step k 1, such as acceleration; Wk-1 and Uy are random noise or errors introduced during calculation and measurement, thus yk represents the measurement value; A and B are weight matrices; and H is the transformation matrix. When I& and yı are of the same type, the matrix H can be omitted. In practice the value of w;-1 is unknown, otherwise the precise results would be calculated without the needs of Kalman filter. Instead, we have the predicted value Tx, which is F& = - Buk. The key idea of the Kalman filter is to fuse these F& and yee in order to obtain more accurate estimation of the system state. The fusion process is typically represented as:
(et)" Td d(g(et)"~)),
(10)
where n denotes the iteration count. This iterative process can be enhanced through acceleration techniques such as An- derson acceleration (Anderson, 1965). However, calculat- ing complex Ei can be quite onerous. An empirical acceler- ation method proposed (Pan et al., 2023) introduces a refine- ment for ej by setting: (st)" = (z((z))" - 1, ne (v)) and (et)a = E0 ((x))==1 , - 1, To (w)). They finally reach:
(2;)=+== = g(0.5 (=t)* 0.5 (e))m=1),
(11)
where the term 0.5 " + 0.5 (=t) represents the refinement technique for E as suggested by Pan et al. If we were to apply the function to both sides of Equation (11), it would align perfectly with the form of Equation (10). Their experiments have demonstrated that this approach is more effective than both Anderson's 's method (Anderson, 1965) and other techniques in inversion tasks.
i a. + By measure,
(14)
where a + 3 = and y measure = H- We can rewrite the fusion equation as:
it = (1 (1- -)Tx +-3 y)s = 3k + 3(H yk - Fx); (15)
Despite the progress made, this paper acknowledges in- herent limitations in the practical implementation of the inversion technique. (1) Inversion Efficiency: While the method outlined in Equation (11) has shown improvements over traditional fixed-point iteration, it still relies iter- ative optimization. The need for multiple forward passes through the diffusion model is computationally demanding and can result in inefficiencies in downstream applications. (2) Inversion Performance: The theoretical improvements presented assume that e = Et. However, iterative optimiza- tion does not guarantee the exact fulfillment of Equation (7) for every time step f. Therefore, while the method may the- oretically offer superior performance, cumulative errors can sometimes lead to practical outcomes that are less satisfac- tory than those achieved with the standard DDIM Inversion method, as shown in Figure 1.
When we set 3 K HI the objective function would be:
S F + K(yk HBx))
(16)
Here, K is the Kalman gain. Additionally, 3y = =AĀx -1 + Bug is the predicted estimate of the true state Tk, based on the previous estimate 2g-1-
3.4. EasyInv
To facilitate our subsequent analysis, we introduce the notation âr to represent
and 3. to denote Dt-1
1 we can reframe Equation (3) as follow:
With these notations,
3.3. Kalman Filter
z = -ل + B
(17)
The Kalman filter is designed to estimate the state of a dy- namic system from noisy measurements. It assumes two states of the system: the predicted state, &gg which is cal- culated using the process function, and the observed state, yk. which represents the measured state of the system. For instance, the position of an object could be predicted using its velocity and time, while its position might be observed via a radar. Here, & denotes the time-step. Ideally, z and yk should be identical; however, in practice, both are prone to inaccuracies. The Kalman filter aims to provide an op- timized estimate by combining both values. The relevant equations follows:
Similarly, we can express the form of z;- as:
z = 0z-1 + B
(18)
By combining these two formulas, we derive:
z āz C. + Bist
(19)
This can be further generalized to:
2,
= 11 0.)3 +
II a,)c j=++1
(20)
2g ASk-1 1 + But + Wk-1-
(12)
2=1
4

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EasyInv: Toward Fast and Better DDIM Inversion
From Equation (20), it is evident that z is weighted sum of 2g and a series of noise terms ej. The denoising process of Equation (I) aims to iteratively reduce the impact of these
Algorithm Add EasyInv to an existing inversion method
Require: A inversion algorithm Inz(), total inversion steps T, latent chosen steps i, empirical parameter 1)
noise terms. In prior research, the crux of inversion is to introduce the appropriate noise of at each step to identify a suitable zi. This allows the model to obtain 2g as the final output after the denoising process, However, iteratively updating E can be time-consuming, and when the model lacks high precision, achieving satisfactory results within reasonable number of iterations may be challenging.
1: for t in T do
2: Et+11 = =I
3: if t tten
4:
24+1 1-11- * 2141 + (1 - 17) * 21
5: end if
6: end for Output: inverted latent 2T
To address this, we propose an alternative perspective. Dur- ing inversion, rather than searching for better noise, we aggregate the latent state from the last time step 2i-1 with the current latent state 2 at specific time steps I, as illus- trated in the following formula:
4. Experimentation
In Table I and Table 2, we compare our Easylnv over other inversion methods, using SD V1.4 on one NVIDIA GTX 3090 GPU. For Fixed-Point Iteration (Pan et al., 2023), we re-implemented it using settings from the paper. We set the data type of all methods to float16 by default to improve efficiency. The inversion and denoising steps are T 50, except for Fixed-Point Iteration, which recommends T 20. For our Easylnv, we set 0.05 T < f < 0.25 T and = 0.5. In Table 3 we compare the performance of different down-string tasks when using different inversion methods, the dataset and codes we used in this experiment are from PNPinversion (Ju et al., 2024).
e = 72 + (1- (1-1)zm-1-
(21)
where y is trade-off parameter. The selection of l and r)
will be discussed in Section 4.1. Considering Equation (16),
when applying it to solve the inversion problem, we obtain the following equation:
ziz = Kz +
K) K)yk.
(22)
where H is ignored since yk and āR show be same kind of data. zi represents the estimated value ī사. Next, we replace the measured value y/k with 1 because while 2 cannot be directly measured during the inversion process at time- step ( 1, the difference 8y- = 2 ž assumed to be Gaussian noise, in accordance with the basic principles of diffusion models. Thus, 2t-1 can be treated as the measured value Z₁ in Equation (13) for the inversion problem. The equation then becomes:
We use three major qualitative metrics: LPIPS index (Zhang et al., 2018 8), SSIM (Wang et al., 2004), and PSNR with the inference time, We sample 2,298 images from the COCO 2017 test and validation sets (Lin et al., 2014).
4.1. Quantitative Results
a K23 + (1 K)2
(23)
Table 1 presents the quantitative results of different methods. Easylnv achieves a competitive LPIPS score of 0.321, better than ReNoise (0.316) and Fixed-Point Iteration (0.373), indi- cating closer perceptual similarity to the original image. For SSIM, EasyInv achieves the highest score of 0.646, show- ing superior structural similarity crucial for maintaining
Typically, determining the Kalman Gain K is the main goal of the Kalman filter algorithm, which can be compu- tationally expensive. However, the approach described in Equation (21) can be viewed as a simplified version of the Kalman filter, where K is treated as constant value, deter- mined empirically. With this method, the added noise for inverting _ to 2 would be:
2- zi n(2; 27 1)
(24)
Table 1. A comparative result of quantitative outcomes.
LPIPS (L)
SSIM (t)
PSNR (t)
Time (L)
Here, z represents the original 2 on the right-hand side of Equation (21), generated via DDIM inversion in our case. As indicated in Equation (24), the distribution tran- sition from _ to z in our method mirrors the distri- bution pattern of DDIM inversion, maintaining the same variance. Due to < 1, the average value is reduced. In other words, our method preserves the noise pattern of the original method but applies a rescaling factor.
DDIM Inversion
0.328
0.621
29.717
5s
ReNoise
0.316
0.641
31.025
Fixed-Point Iteration
0.373
0.563
29.107
14s
Easylnv (Ours)
0.321
0.646
30.189
5s
Table 2. A comparative result of half- and full-precision FasyInv.
LPIPS (L)
SSIM (t)
PSNR (1)
Time (L)
Full Precision
0.321
0,646
30.184
9s
Algorithm 1 illustrates the approximate operation of our method in conjunction with an inversion framework.
Half Precision
0.321
0,646
30.189
5s
5

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EasyInv: Toward Fast and Better DDIM Inversion
Table Performance of downstream methods. Results except "Ours+Diirettnn" from PNPinversion (Ju al., 2024). DDIM inversion (Couairon et al., 2023) for DDIM, Null-text inversion (Mokady et al., 2023) for NT. Negative-Prompe Inversion (Miyake al.. 2023) NP, StyleDiffusion (Wang al., 2023) StyleD, PNPinversion (Ju al., 2024) for Directlnv, Prompt-to to-ppttt (Hertz al.. 2022) for P2P, MasaCtrl (Cao al., 2023) for MasaCtri, Pix2Pix-Zero (Parmar et al., 2023) for P2P-Zero and Plug-and-Play (Tumanyan al., 2023) for PnP.
it averaged results A800 and KTX3090 since different environment leads to slightly different performance. uses Stable Diffusion v1.5 as base model (others all use Stable Diffusion v1.4).
Method
Structure
Background Preservation
CLIP Similarity
Inverse
Editing
Distance 10a
PSNR
LPIPS
MSE 10+ +
SSIM x
f
Whole t
Edited
DDIM
P2P
69.43
17.87
208.80
219.88
71.14
25.01
22.44
NT
P2P
13.44
27.03
60.67
35.86
84.11
24.75
21.86
NP
P2P
16.17
26.21
69.01
39.73
83.40
24.61
21.87
StyleD
P2P
11.65
26.05
66.10
38.63
83.42
24.78
21.72
Directlav
P2P
11.65
27.22
54.55
32,86
84.76
25.02
22.10
Ours+DirectIny
P2P
11.58
27.30
53.52
32.37
84.80
24.97
22.00
DDIM
MasaCtrl
28.38
22.17
106.62
86.97
79.67
23.96
21.16
Directinv
MasaCtrl
24.70
22.64
87.94
81.09
81.33
24.38
21.35
Ours+DirectIny
MasaCtrl
23.82
22.72
87.65
79.73
81.82
24.36
21.32
DDIM
P2P-Zero
61.68
20.44
172.22
144.12
74.67
22.80
20.54
DirectInv
Per-Zero
49.22
21.53
138.98
127.32
77.05
23.31
21.05
Ours+DirectIny
P2P-7ero
48.02
21.54
136.78
124.00
77.71
23.40
20.93
DDIM
PnP"
28.22
22.28
113.46
83.64
79.05
25.41
22.55
DirectInv
PnP
24.29
22.46
106.06
80.45
79.68
25.41
22.62
Ours+DirectInw
PnP
22.88
22.56
102.34
78.57
80.27
25.38
22.53
image coherence. For PSNR, Easylnv scores 30,189, close to ReNoise's 's highest score of 31.025, indicating high im- age fidelity. Easylnv completes the inversion process in the fastest time of 5 seconds, matching DDIM Inversion (Coua- iron et al., 2023), and quicker than ReNoise (16 seconds) and Fixed-Point Iteration (14 seconds), highlighting its effi ciency without compromising on quality. EasyInv performs strongly across all metries, with the highest SSIM score indicating effective preservation of image structure. Its effi- cient inversion makes it suitable for real-world applications where both quality and speed are crucial.
tional optimization for half precision, offering faster speeds and reduced resources without compromising output quality.
In Table 3, we compare the performance of several down- stream tasks using our method and other inversion tech- niques. Most of the results in this table are from PNPInver- sion (Ju et al., 2024), where dataset for 9 different image editing tasks is introduced, along with the corresponding code for both editing and evaluation. We used these codes and dataset for this experiment. The only modification we made was to incorporate our method into DirectInv, the in- version method proposed in their work (Ju et al., 2024), and its results are indicate as 'ours+DirectInv' Table 3. As we pointed out, our method able to combined with most existing inversion algorithm. The results in Table 3 show the our advantage. By adding of DirectIny improves in 5 out of 7 metrics across all editing tasks, with minimal changes in the remaining 2 metrics.
Table 2 compares EasyInv's performance in half- salf-precision (float16) and full-precision (float32) formats. Both achieve the same LPIPS score of 0.321, indicating consistent percep- tual similarity to the original image. Similarly, both achieve an SSIM score of 0.646, showing preserved structural in- tegrity with high fidelity. For PSNR, half precision slightly outperforms full precision with scores 30.189 and 30.184. This slight advantage in PSNR for half precision is note- worthy given its well reduced computation time. The most significant difference observed in the time metric, where half precision completes the inversion process in 5 seconds, approximately 44% faster than full precision, which takes 9 seconds. This efficiency gain highlights EasyInv's excep-
method, the performance
4.2. Qualitative Results
We visually evaluate all methods using SD-V1-4, Figure 3 displays the results using images sourced from the inter- net. These images also feature large areas of white color. ReNoise struggles with images containing significant white areas, resulting in black images. Fixed- Fixed-Point Iteration and
6

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EasyInv: Toward Fast and Better DDIM Inversion
COR
Exsenx
Table Ablation Experiments 77-
Scttte
Stectare
Background Prperation
Similnty
Disusss..o
PrP
25,90
13.29
113.86
66.11
20.13
24.66
21.60
PxP
34.44
1339
50.00
31.76
33,89
90.27
0.6
PaP
23.36
2321
101.37
67.6
8000
25.02
22,00
PLP"
22.59
22,92
98.44
72.30
30.04
25,30
22,29
Figure visual assessment of various inversion techniques.
Urignal DUM Nahana
maa--Forr.
Figure 5. Results of MasaCtrl (Cao et al., 2023) with prompt "A football", using inverted latent by different methods input.
4.4. Downstream Image Editing
To showcase the practical utility of our EasyInv, we have employed various inversion techniques within the realm of consistent image synthesis and editing. We have seam- lessly integrated these inversion methods into MasaCtrl (Cao et al., 2023), widely-adopted image editing approach that extracts correlated local content and textures from source images to ensure consistency. For demonstrative purposes, we present an image of "peach" alongside the prompt "A football." The impact of inversion quality is depicted in Figure 5. It is clear that our inversion method demon- strates superior performance in both texture and shape. We also apply our Easylnv to two additional downstream tasks: DiffusionTrean (Zhan et al., 2024) for virtual try-on and UniVST (Song et al., 2024) for video style transferring. In Figure 6, using our method results better consistency of the cloth compared to DDIM and results in Figure 7 show that DDIM inversion leads chaotic outcomes, whereas our method yields much clearer results.
Original image
Easylnv (Ours)
Original image
Easylnv (Ours)
Figure More visual results of our EasyInv.
DDIM Inversion also fail to generate satisfactory results in such cases, suggesting these images pose challenges for inversion methods. Our method, shown in the figure, effec- tively addresses these challenges, demonstrating robustness and enhancing performance in handling special scenarios. These findings underscore the efficacy of our approach, par- ticularly in addressing challenging cases that less com- mon in the COCO dataset. For better illustrations, show more visual examples in Figure 4.
4.3. Ablation Studies
To clarify our experimental choices, in Table we present ablation studies on the parameter 7/, conducted on the same dataset as in Table 3. Note that 1 would correspond to would bypass the inver-
standard DDIM inversion and sion operation. Therefore, we exclude them. Our findings indicate that 7 = 0.5 yields the best overall performance.
Figure 6. Comparison from DiffusionTrasn (Zhan et al., 2024).

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EasyInv: Toward Fast and Better DDIM Inversion
Origin
DDIM
EasIin
Origin
DDIM
EagyIm
Figure Comparison from UniVST (Song et al., 2024).
4.5. Limitations
5. Conclusion
One potential risk is the phenomenon known as "over- denoising," which occurs when there a disproportionate focus achieving pristine final-step latent state. This may result in overly smooth image outputs, exemplified by the "peach" figure in Figure 3. In most real-world image editing tasks, this is not typical issue, as these tasks often involve style migration, which inherently alters the details of the original image. However, in specific applications, such as using diffusion models for creating udvertisements, this could pose a challenge. Nonetheless, our experimental re- sults highlight that the method's two key benefits outweigh this minor shortcoming. Firstly, it is capable of delivering satisfactory outcomes even with models that may under- perform relative to other methods. Secondly, it enhances inversion efficiency by reverting to the original DDIM In- version baseline (Couairon et al., 2023), thereby eliminating the necessity for iterative optimizations. This strategy not only simplifies the process but also ensures the maintenance of high-qualit outputs, marking it as noteworthy advance- ment over current methodologies.
Our EasyInv addresses the inefficiencies and performance limitations traditional iterative optimization methods. By emphasizing the importance of the initial latent state and introducing refined strategy for approximating inversion noise, EasyInv enhances both the accuracy efficiency of the inversion process. Our method reinforces the initial latent state's 's influence, mitigating the impact of noise and ensuring closer reconstruction to the original image. This approach not only matches but often surpasses the performance of existing DDIM Inversion methods, especially in scenarios with limited model precision computational resources. FasyInv also shows remarkable improvement in infer- ence efficiency, achieving approximately three times faster processing than standard iterative techniques. Through ex- tensive evaluations, we have shown that EasyInv delivers high-quality results, making it a robust and efficient solution for image inversion tasks. The simplicity and effectiveness of Easylnv underscore its potential for broader applications.
A Acknowledgments
In conclusion, our research has made significant strides with the introduction of Easylnv. As we look ahead, our com- mitment advancing this technology remains unwavering. Our future research agenda will be focused on the persistent enhancement and optimization of the techniques. This will be done with the ultimate goal of ensuring that our method is not only robust and efficient but also highly adaptable to the diverse and ever-evolving needs of industrial applications.
This work was supported by the National Science Fund for Distinguished Young Scholars (No.62025603) the Na- tional Natural Science Foundation China (No. U21B2037, No. U22B2051, No. U23320383 No. 62176222, No. 62176223, No. 62176226, No. 62072386. No. 62072387, No. 62072389, No. 62002305 and No. 62272401), and the Natural Science Foundation of Fujian Province of China (No. 2021J06003, No.2022106001),
8

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EasyInv: Toward Fast and Better DDIM Inversion
Impact Statement
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ra- manan, D., Dollár, P., and Zitnick, C. L.. Microsoft coco: Common objects in context. In European Conference on Computer Vision, 2014.
This paper presents work whose goal to advance the field of Machine Learning. There are many potential societal consequences of our work, none which we feel must be specifically highlighted here.
Liu, J., Huang, H.,, Jin, C., and He, R. Portrait diffu- sion: Training-free face stylization with chain-of- arXiv preprint arXiv22312.02112 2023.
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EasyInv: Toward Fast and Better DDIM Inversion
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10


Reviews:
[{"review_id": "X1XlO3PA00", "reviewer": "Reviewer_arbb", "summary": "This paper introduces EasyInv, a novel DDIM inversion method that significantly enhances inversion efficiency and reconstruction quality by optimizing the utilization of the initial latent state. The work demonstrates thorough experimentation and good practical value, with solid theoretical foundation, making it a highly impactful contribution to the field.", "questions": "", "limitations": "", "rating": 5, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the reviewer’s constructive feedback and suggestions for additional experimental validation. Due to space constraints, some of these analyses were deferred to future work; however, several of the suggested experiments have either been completed or are actively underway. We address the key points as follows:\n\n\n**1. Quantitative Analysis of Over-Smoothing**\n\nWhile our paper qualitatively discusses the over-denoising issue (e.g., the “peach” example in Fig. 3), we acknowledge that a more comprehensive quantitative analysis (e.g., using FID or user studies) would better elucidate its practical impact. We have initiated such studies and plan to incorporate these quantitative metrics in an extended version of our work.\n\n**2. Generalization Across Models**\n\nOur experiments currently focus on Stable Diffusion V1.5. We agree that evaluating our method on larger models (such as SD-XL) and alternative architectures (e.g., DALL-E 3) would strengthen the claims regarding generalizability. The qualitative experiments on SD-XL have been done, but not able to display here due to the restriction of OpenReview's rebuttal rule. We are actively extending rest experiments to include these models and will report the results in the final version.\n\n**3. Parameter Sensitivity Analysis**\n\nWe conducted an ablation study on the parameter $\\eta$, summarized in the table below, which guided our selection of $\\eta = 0.5$ as it achieved the overall best performance. Note that $\\eta = 1$ corresponds to a standard DDIM inversion method and $\\eta = 0$ would essentially bypass the operation, so those cases were not included. \n\n| $\\eta$ | Editing Method | Distance $\\(\\times 10^2\\)\\(\\downarrow\\)$ | PSNR $\\(\\uparrow\\)$ | LPIPS $\\(\\times 10^3\\)\\(\\downarrow\\)$ | MSE $\\(\\times 10^3\\)\\(\\downarrow\\)$ | SSIM $\\(\\times 10^2\\)\\(\\uparrow\\)$ | Whole CLIP Similarity$\\(\\uparrow\\)$ | Edited CLIP Similarity$\\(\\uparrow\\)$ |\n|:-------:|:------:|:---------------------------------------:|:-----------------:|:------------------------------------:|:---------------------------------:|:--------------------------------:|:-------------------:|:--------------------:|\n| η=0.2   | PnP*      | 25.90          | **23.29**  | 113.86       | **66.11**     | 80.13      | 24.66   | 21.60    |\n| η=0.4   | PnP*      | 24.44          | **23.29**  | 107.18       | 66.32     | 80.69      | 24.82   | 21.76    |\n| η=0.5   | PnP*      | **22.88**          | 22.56  | 102.34       | 78.57     | 80.27      | **25.38**   | **22.53**    |\n| η=0.6   | PnP*      | 23.36          | 23.21  | 101.37       | 67.96     | **81.07**      | 25.02   | 22.00    |\n| η=0.8   | PnP*      | 22.89          | 22.92  | **98.44**        | 72.30     | 80.94      | 25.30   | 22.29    |\n\nRegarding the empirical parameter $\\tilde{t}$, our experiments with diffusion models having reduced parameter counts suggest that optimal performance is achieved when EasyInv is applied during the early denoising steps. We agree that further sensitivity analyses across different tasks and datasets would provide additional insights and are planned for our future revisions.\n\n**4. Validation Across Diverse Noise Levels**\n\nWe recognize that our current experiments were conducted using a fixed noise configuration (T=50 steps). To further demonstrate the robustness of EasyInv, we plan to evaluate its performance under varying noise conditions (e.g., T=30 and T=100) and across different generation scenarios (e.g., low-step fast generation vs. high-step high-precision generation). These experiments will help clarify whether EasyInv maintains its efficiency and reconstruction quality when the inversion steps vary significantly.\n\n**5. Comparison with Established and Emerging Methods**\n\nWe appreciate the suggestion to include direct comparisons with both classical inversion techniques (e.g., EDICT) and emerging approaches (e.g., Inversion-Free Image Editing). We excluded EDICT from our current comparisons because its original work lacked comprehensive evaluations against existing methods, which we felt diminished the persuasiveness of its results. Moreover, emerging techniques like inversion-free image editing rely on framework-specific implementations (e.g., rectified flow in SDv3/FLUX) that are currently incompatible with our framework. We plan to develop cross-framework adaptation layers and implement these reference methods in future work to provide a more comprehensive benchmarking of reconstruction fidelity, computational efficiency, and robustness.\n\n\n**Summary**\n\nWe value the reviewer’s insights and are committed to enhancing the methodological rigor of our work through these additional experiments and comparative analyses. Many of these extensions are already underway, and we will incorporate the resulting findings in our revised and future publications.", "claims_and_evidence": "The authors claim two major merits from two perspectives: fast and better. Tables 1 and 2 do provide the evidence of a fast inference. Performance from Tables 1 and 3 also show its overall best performance over existing methods. Also, downstream tasks in Figure 6 and 7 do provide better visual results.", "methods_and_evaluation_criteria": "This paper provides both quantitative and qualitative evaluations. The quantitative evaluation mostly follows standard criteria with prior methods while the qualitative evaluations include visualization of different methods in inversions as well as the applications of downstream tasks.", "theoretical_claims": "N/A", "experimental_designs_or_analyses": "The experimental designs and analyses are comprehensive. In particular, it shows it practical applications on the downstream tasks, which, I believe, is a good case study to demonstrate its promising performance.", "relation_to_broader_scientific_literature": "The method in this paper contributes a lot to the DDIM inversion that reverses the denoising process. By aggregating current latent states with the last step’s, it results in a fast and better inversion results.", "essential_references_not_discussed": "NA.", "other_strengths_and_weaknesses": "*Strengths*\n\n1. Novelty and Elegance of the Method:\nEasyInv’s core innovation lies in its departure from traditional iterative noise\noptimization. Instead, it emphasizes preserving and dynamically aggregating\ninformation from the previous latent state. The weighted fusion of latent states (Eq.\n19) effectively mitigates noise accumulation errors while amplifying the dominance of\nthe initial information during inversion. This strategy is both elegant and efficient, and\nits theoretical connection to the Kalman filter framework (Eq. 20-22) provides a solid\nfoundation. Compared to existing methods (e.g., ReNoise, Fixed-Point Iteration),\nEasyInv exhibits unique problem formulation and solutions, representing a significant\nadvancement in DDIM inversion.\n\n2. Exceptional Efficiency and Practicality:\nExperiments show that EasyInv matches the inference speed of baseline DDIM\ninversion (5 seconds) while outperforming iterative optimization methods (e.g.,\nReNoise: 16 seconds). Its compatibility with half-precision computation (float16)\nfurther reduces computational costs, highlighting its practicality. The \"four-line code\nintegration\" scheme (Algorithm 1) significantly lowers deployment barriers,\nenhancing its usability in real-world applications.\n\n3. Comprehensive and Convincing Experiments:\nThe paper thoroughly evaluates EasyInv through quantitative metrics (LPIPS, SSIM,\nPSNR), qualitative comparisons (Figs. 1-4), and downstream task validations (Table 3).\nNotably, EasyInv achieves superior or comparable performance on both the COCO\ndataset (2,298 images) and challenging scenarios (e.g., images with large white\nregions). The analysis of half- vs. full-precision computation (Table 2) further validates\nthe method’s robustness.\n\n4. Clear Theoretical Explanation:\nThe authors draw a compelling analogy between latent state aggregation and the\nKalman filter’s prediction-update mechanism (Eq. 18-22), offering a theoretical\ninterpretation of the method. While the Kalman gain is simplified, this connection\nenriches the paper’s theoretical depth.\n\n**Weaknesses**\n1. Quantitative Analysis of Over-Smoothing:\nWhile the \"over-denoising\" issue is qualitatively discussed (e.g., the \"peach\" example\nin Fig. 3), quantitative metrics (e.g., FID) or user studies could better assess its practical\nimpact. Additional analysis here would provide a more holistic evaluation of limitations.\n\n2. Generalization Across Models:\nCurrent experiments are primarily based on Stable Diffusion V1.4/V1.5. Expanding\nevaluations to larger models (e.g., SD-XL) or other architectures (e.g., DALL-E 3)would strengthen claims about the method’s generalizability.\n\n3. Parameter Sensitivity Analysis:\nThe paper sets empirical parameters (e.g., 0.05*T < t̃ < 0.25*T) without a detailed\nexploration of their impact. A sensitivity analysis of η and t̃ across different tasks or\ndatasets would clarify their robustness and guide optimal parameter selection.\n\n4. Validation Across Diverse Noise Levels:\nThe current experiments primarily focus on a fixed noise configuration (e.g., T=50\nsteps). To ensure broader applicability, it would be valuable to evaluate EasyInv under\nvarying noise levels (e.g., T=30, T=100) and generation scenarios (e.g., low-step fast\ngeneration vs. high-step high-precision generation). For instance, does EasyInv\nmaintain its efficiency and reconstruction quality when the inversion steps are\nsignificantly reduced or increased? Such experiments would demonstrate robustness\nin real-world settings where noise configurations may vary.\n\n5. Comparison with Established and Emerging Methods:\nWhile the paper presents extensive experimental results, it lacks direct comparisons\nwith widely recognized classical inversion techniques (e.g., EDICT [1]) and novel\napproaches (e.g., Inversion-Free Image Editing [2]). Including such comparisons—\nparticularly in terms of reconstruction fidelity, computational efficiency, and\nrobustness to challenging inputs would strengthen the methodological positioning\nof EasyInv.", "other_comments_or_suggestions": "See weakness.", "questions_for_authors": "NA", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "zpQqMvpYkf", "reviewer": "Reviewer_UW4y", "summary": "The paper introduces EasyInv, a novel approach to DDIM inversion that improves efficiency and reconstruction quality by refining inversion noise approximation. The novelty compared to other inversion methods lies in the addition of a relaxation step, which makes it compatible with other inversion methods. The method follows from a Kalman-filter argument to compensate for errors in the noise approximation step. The authors showcase their methods on a number of examples, and show that it allows to perform image editing with superior results to other methods.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Dear Reviewer UW4y,\n\nWe would like to express our sincere gratitude to you. It is truly an honor and a stroke of luck to have a reviewer as dedicated and responsible as you. Your constructive suggestions have been invaluable to us.\nWe are particularly touched by your **understanding regarding our reimplementation** of the Fixed-Point Iteration, given the lack of alternatives. Your insight that addressing Table 1 and Figure 3 could potentially **raise our score** is greatly appreciated. We are also impressed by your careful review, which effectively summarized our claims and supporting evidence.\n\nWe give our point-by-point response below.\n\n**1. Discrepancy Between Table 1 and Figure 3**\n\nThe apparent discrepancy between Table 1 and Figure 3 arises from the different demonstration objectives of these experiments. While Table 1 quantifies ReNoise’s overall performance—showing that it performs reasonably well (albeit slightly inferior to our method), Figure 3 is designed to illustrate a critical limitation: ReNoise’s instability when processing **real images** with extensive white regions. In such scenarios, ReNoise tends to produce undesirable black artifacts. This visual evidence underscores the robustness and superior stability of our approach under challenging conditions.\n\nWe would like to emphasize that the above explanation will be explicitly incorporated into our final paper. We sincerely hope that our detailed explanation can effectively address your concerns and look forward to an increase in our score.\n\n\n**2. Evaluating the Latent Quality Versus the Image Domain**\n\nOur main contribution lies in performing fixed-point iteration in the latent space to **improve computational efficiency** during the denoising process. Since the ultimate objective is to achieve high-quality image generation via the final decoded images, we opted to emphasize the image-domain evaluation. While efforts have been made, we found it hard (or even irrational) to compare with ``ground-truth latent'' in the latent space for: **1. Lack of Suitable Metrics**: We have been unable to identify a robust quantitative metric that can effectively measure the quality of latent representations.; **2. Irrelevance to Final Objective**: Comparisons in the latent space do not directly reflect the ultimate goal of generating high-quality images. The latent space is merely an intermediate representation, and its fidelity does not necessarily correlate with the quality of the final decoded images.; **3. Efficiency Focus**: Our primary motivation for operating in the latent space is to improve computational efficiency. The latent space serves as a means to an end, rather than an end in itself.\n\nWe hope you understand the limitations we face in providing direct comparisons in the latent space and appreciate our focus on achieving the best possible outcomes in the image domain.\n\n\n**3. Additional Ablation Studies on Parameter $\\eta$**\n\nTo further clarify our experimental choices, we present additional ablation studies on the parameter $\\eta$, conducted on the same dataset as in Table 3 of our paper. Note that $\\eta = 1$ would correspond to a standard DDIM inversion method and $\\eta = 0$ would effectively bypass the inversion operation. Therefore, these boundary cases were excluded from the ablation experiments. Our findings indicate that $\\eta = 0.5$ yields the best overall performance:\n\n| $\\eta$ | Editing Method | Distance $\\(\\times 10^2\\)\\(\\downarrow\\)$ | PSNR $\\(\\uparrow\\)$ | LPIPS $\\(\\times 10^3\\)\\(\\downarrow\\)$ | MSE $\\(\\times 10^3\\)\\(\\downarrow\\)$ | SSIM $\\(\\times 10^2\\)\\(\\uparrow\\)$ | Whole CLIP Similarity$\\(\\uparrow\\)$ | Edited CLIP Similarity$\\(\\uparrow\\)$ |\n|:-------:|:------:|:---------------------------------------:|:-----------------:|:------------------------------------:|:---------------------------------:|:--------------------------------:|:-------------------:|:--------------------:|\n| η=0.2   | PnP*      | 25.90          | **23.29**  | 113.86       | **66.11**     | 80.13      | 24.66   | 21.60    |\n| η=0.4   | PnP*      | 24.44          | **23.29**  | 107.18       | 66.32     | 80.69      | 24.82   | 21.76    |\n| η=0.5   | PnP*      | **22.88**          | 22.56  | 102.34       | 78.57     | 80.27      | **25.38**   | **22.53**    |\n| η=0.6   | PnP*      | 23.36          | 23.21  | 101.37       | 67.96     | **81.07**      | 25.02   | 22.00    |\n| η=0.8   | PnP*      | 22.89          | 22.92  | **98.44**        | 72.30     | 80.94      | 25.30   | 22.29    |\n\n\n**4. Clarification on Notation in Lines 201–207**\n\nWe appreciate the reviewer’s suggestion regarding the notation used in lines 201–207. We agree that the current format is potentially confusing since these are not formal equations. In the revised version, we will switch to a clear itemized list (e.g., using (i) and (ii)) to improve clarity.", "claims_and_evidence": "The main claim/evidences of the paper can be summarized as follows:\n\n- **Claim1:** Existing methods do not satisfy (7) rigorously and a correction step is necessary.\n- **Evidence1:** Rigorous theoretical derivations are proposed to show how to incorporate Kalman-type corrections. \n\n- **Claim2:** Existing methods require multiple forward passes, yielding slow inversion procedure.\n- **Evidence2:** The benefits of the proposed algorithm is not really clear on paper, since the method is added to an existing inversion algorithn. However, the experimental results clearly show a benefit.\n\n- **Claim3:** The proposed relaxation step leads to better inversion quality.\n- **Evidence3:** While the proposed theory does not prove that the correction is necessary nor that it does improve the results, the theoretical derivation is rigorous. Experiments support the claim.", "methods_and_evaluation_criteria": "The proposed method is properly evaluated: it is compared with several existing methods, on large datasets.", "theoretical_claims": "This is not a theoretical paper. The light theoretical derivations are here for providing intuition, but they remain rigorous.", "experimental_designs_or_analyses": "Experimental design looks good. My only concern lies is a potential unfair comparison to:\n1. Fixed-Point Iteration (Pan et al., 2023), which was reimplemented by the authors. However, the source code is unavailable, so the authors do not have alternatives.\n2. ReNoise: the method seems to always fail in the visuals of the paper, despite Table 1. This is more critical and should be adressed by the authors.", "relation_to_broader_scientific_literature": "The relation to scientific literature is clear and well done.", "essential_references_not_discussed": "Essential references are present.", "other_strengths_and_weaknesses": "**Strenghts:**\n1. The paper is well written and easy to follow.\n2. Claims are supported by evidences.\n3. The method is original and interesting, with good experimental results.\n4. Literature review is quite broad.\n\n**Weaknesses:**\n1. The comparison with ReNoise should be clarified. Table 1 and Figure 3 seem incompatible. In my opinion, this needs to be adressed for accepting the paper.\n2. Ablation studies on the parameter $\\eta$ are not present and this should be discussed.", "other_comments_or_suggestions": "- the (1) and (2) lines 201 - 207 are confusing as these are not equations. Maybe the authors could switch to (i)-(ii) or something else.", "questions_for_authors": "1. My main issue with the paper is the ReNoise results in Fig. 3, in contradiction with Table 1. Could the authors comment on that, and provide decent visual results for ReNoise? This is a reason for my rating to 3, which I would be happy to increase if the authors replied positively. EDIT this has been addressed by the authors.\n2. Here, all results are shown in the image domain. However, the main claim of the paper is a fixed point interation in the latent. Is there a way to quantify the quality of the latent with respect to the ground-truth latent instead of performing visual comparisons? This would strengthen the paper, in particular in view of the fixed-point algorithms community.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "iNF3f6gqSZ", "reviewer": "Reviewer_md9u", "summary": "This paper introduces ​**EasyInv**, an additional mixture operation integrated into the diffusion model-based image editing pipeline, aiming to ​**enhance inversion accuracy** and ​**improve computational efficiency**.\n\n## update after rebuttal\n1. I acknowledge the approximation of the Kalman Filter and additional experiments, thus raising the score to 2.5.\n2. However, the inversion visual effect in Figure 2 is not that satisfactory, so I cannot give a higher score.\n3. And I still doubt about analogizing learned score as Kalman noise; however, if it works in practice, it's fine, and an intuitive starting point may deviate from the underlying true mechanism.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for reviewing our paper. The novelty of our work is well recognized by the other two reviewers. However, we received the lowest score from Reviewer md9u. We appreciate your feedback and hope our explanations below address your concerns, leading to a score reconsideration.\n\n\n**1. Over-Simplification of the Kalman Gain and Questionable Noise Equivalence Assumption**\n\nSection 3.3 provides essential background on the Kalman filter, with Equations (12)--(14) summarizing standard formulations consistent with Section 7 of [a]. Notably, the cited reference explicitly states that for specific applications, the Kalman gain $K$ in Equation (14) can be simplified to a blending factor within $[0,1]$, a strategy directly adopted in our methodology. Furthermore, Equation (6) of KLDD [b] demonstrates that the Kalman gain can evolve into a time-varying constant at each timestep, aligning with our approach.\n\n[a] Kalman and bayesian filters in python.\n\n[b] Kldd: Kalman filter based linear deformable diffusion model in retinal image segmentation.\n\n**2. Misleading Analogy with PF-ODE Inversion**\n\nRegarding $\\varepsilon^\\ast$, while theoretically derived through score-matching optimization, it is important to clarify that this term is not simply a scaled score. Instead, as shown in Equation (15), it is scaled by $\\bar{\\beta}_t$. In practice, during SDv1.5 training, the model learns to predict the Gaussian noise added to original images [c]. Given its strong noise prediction capabilities, assuming $\\varepsilon^\\ast$ follows Gaussian characteristics is reasonable.\n\n[c] High-resolution image synthesis with latent diffusion models.\n\n\n**3.Distribution between $z_t$ and $p_t$**\n\nOur formulation preserves the latent variable $z$'s distributional consistency. Equations (18)--(19) show that at the final inversion step ($\\bar{t}=T$), $z_T$ emerges as a convex combination of $\\eta z_T$ and $(1-\\eta)z_{T-1}$. Since ${z_T}$ and ${z}_{T-1}$ are close to the sum of a series of $\\varepsilon_i^\\ast$ (Gaussian-distributed), their linear combination retains Gaussian properties. To validate this, we computed the KL divergence between inversed latent representations and random input noise in SDv1.5, yielding an average KL divergence of 210.69049 on 298 samples. For comparison, the average KL divergence across 298 randomly generated noise pairs was 247.50026. These results suggest our method effectively preserves latent space distribution.\n\n**4. Studies on Parameter $\\eta$**\n\nTo clarify our experimental choices, we conduct additional ablation studies on parameter $\\eta$ using the same dataset as Table 3. Since $\\eta = 1$ corresponds to standard DDIM inversion and $\\eta = 0$ bypasses inversion, these cases were excluded. Our results show that $\\eta = 0.5$ achieves the best performance.\n\n| $\\eta$ | Editing Method | Distance $\\(\\times 10^2\\)\\(\\downarrow\\)$ | PSNR $\\(\\uparrow\\)$ | LPIPS $\\(\\times 10^3\\)\\(\\downarrow\\)$ | MSE $\\(\\times 10^3\\)\\(\\downarrow\\)$ | SSIM $\\(\\times 10^2\\)\\(\\uparrow\\)$ | Whole CLIP Similarity$\\(\\uparrow\\)$ | Edited CLIP Similarity$\\(\\uparrow\\)$ |\n|:-------:|:------:|:---------------------------------------:|:-----------------:|:------------------------------------:|:---------------------------------:|:--------------------------------:|:-------------------:|:--------------------:|\n| η=0.2   | PnP*      | 25.90          | **23.29**  | 113.86       | **66.11**     | 80.13      | 24.66   | 21.60    |\n| η=0.4   | PnP*      | 24.44          | **23.29**  | 107.18       | 66.32     | 80.69      | 24.82   | 21.76    |\n| η=0.5   | PnP*      | **22.88**          | 22.56  | 102.34       | 78.57     | 80.27      | **25.38**   | **22.53**    |\n| η=0.6   | PnP*      | 23.36          | 23.21  | 101.37       | 67.96     | **81.07**      | 25.02   | 22.00    |\n| η=0.8   | PnP*      | 22.89          | 22.92  | **98.44**        | 72.30     | 80.94      | 25.30   | 22.29    |\n\n**5. Extra  literature reviews**\n\nWe appreciate the suggestion to broaden our literature review. The three cited methods—EDICT, BDIA, and O-BELM—are classified as exact inversion techniques.\n\n* **EDICT** extends affine coupling mechanisms with alternating dual-state refinement through reciprocal updates between primary and auxiliary diffusion variables.\n\n* **BDIA** enhances this approach with symmetric bidirectional state integration, ensuring provable invertibility but introducing trade-off parameters that may affect robustness across frameworks.\n\n* **O-BELM** synthesizes concepts from both methods, employing a bidirectional explicit architecture while replacing heuristic hyperparameters with analytically derived coefficients optimized via local truncation error minimization, improving numerical stability.\n\nWhile these methods highlight system reversibility, their empirical updates introduce trade-offs. We focus on fixed-point iteration but acknowledge that incorporating these methods could enhance our analysis. We will include them in the final version.", "claims_and_evidence": "**My concerns about the intuition behind EasyInv's Kalman filter analogy:**\n\n1. ​**Over-Simplification of the Kalman Gain**  \n   - The original Kalman gain matrix dynamically adapts to uncertainty estimates, but in EasyInv, it is reduced to a ​**constant value**.  \n   - This simplification strips away the core adaptive mechanism of Kalman filters, raising questions about whether the analogy holds theoretical rigor.\n\n2. ​**Questionable Noise Equivalence Assumption**  \n   - The derivation from Equations (12)-(13) to (14) implicitly assumes ​$ v_k = w_{k-1} $, where:  \n     - $ v_k $: Dynamic noise (process uncertainty),  \n     - $ w_{k-1} $: Measurement noise (observation uncertainty).  \n   - These two terms are fundamentally ​**independent** in Kalman filtering theory. Asserting their equivalence lacks justification and introduces a contradiction: combining two independent noise sources should not yield cancellation ($ v_k - w_{k-1} = 0 $).\n\n3. ​**Misleading Analogy with PF-ODE Inversion**  \n   - The term $ \\epsilon^* $ in PF-ODE inversion represents ​**scaled diffused data scores**, not random noise.  \n   - Unlike Kalman filter noise (stochastic by nature), $ \\epsilon^* $ encodes meaningful gradient directions derived from data. Equating them conflates ​**stochastic control theory** with ​**deterministic score-based diffusion processes**, weakening the theoretical foundation.", "methods_and_evaluation_criteria": "The evaluation of  EasyInv on image editing and PIEbench is reasonable.", "theoretical_claims": "**The paper lacks theoretical proofs.**  \n- It is unclear whether the ​**marginal distribution** of the inverted distribution converges to the ​**diffused distribution**.  \n- EasyInv is presented as a solver for the reverse PF-ODE with ​**order of accuracy 0**. However, no proof is provided to demonstrate that the resulting random variable $ z_t $ of the EasyInv distribution adheres ​**distributionally** to the diffused distribution $ p_t $.  \n- A formal proof of this adherence would significantly strengthen the theoretical foundation of the proposed method.", "experimental_designs_or_analyses": "**The impact of the two hyperparameters on performance remains unclear.**  \n- The choice of $\\eta = 0.5$ appears to be ​**heuristic**, lacking a theoretical or empirical justification.  \n- When applying this method to ​**different tasks**, determining the optimal hyperparameter values becomes a critical challenge.  \n- Conducting ​**ablation studies** on these hyperparameters could provide valuable insights into their influence and help establish guidelines for task-specific tuning.", "relation_to_broader_scientific_literature": "This work mainly leverages some findings in the control theory to devise enhanced methods in diffusion model-based inversion.", "essential_references_not_discussed": "**The literature review appears to be somewhat limited.**  \n- A range of ​**exact inversion sampler techniques**, such as EDICT [1], BDIA [2], and O-BELM [3], have not been discussed or included in the experiments.  \n- Incorporating these methods into the analysis could provide a more comprehensive understanding of the landscape and strengthen the paper's contributions.  \n\n**References:**  \n[1] Wallace, Bram, Akash Gokul, and Nikhil Naik. \"Edict: Exact diffusion inversion via coupled transformations.\" *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*. 2023.  \n\n[2] Zhang, Guoqiang, Jonathan P. Lewis, and W. Bastiaan Kleijn. \"Exact diffusion inversion via bidirectional integration approximation.\" *European Conference on Computer Vision*. 2024.  \n\n[3] Wang, Fangyikang, et al. \"BELM: Bidirectional Explicit Linear Multi-step Sampler for Exact Inversion in Diffusion Models.\" *NeurIPS*. 2024.", "other_strengths_and_weaknesses": "The idea is expressed clearly, which is easy to follow.\n\nA lot of visual comparisons are provided.", "other_comments_or_suggestions": "no", "questions_for_authors": "no", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper presents EasyInv, a new DDIM inversion method that enhances efficiency and reconstruction quality through a refined noise approximation strategy and latent state aggregation. The reviewers recognized the method’s practical value, computational efficiency, and strong empirical performance in downstream tasks. While Reviewer 1 (strong accept) and Reviewer 2 (accept) praised the clarity, theoretical grounding, and comprehensive experiments, Reviewer 3 initially raised concerns about the Kalman filter analogy and parameter sensitivity. The authors addressed these via additional ablation studies, expanded literature comparisons, and clarifications on theoretical assumptions, leading Reviewer 3 to raise the score. The consensus is that the method’s empirical strengths, broad applicability, and thorough rebuttal justify acceptance. The work offers a simple yet impactful contribution to diffusion-based inversion, with clear practical benefits and rigorous validation.
Decision: Accept (poster)