Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation
Authors: Mingyu Kang
Yong Suk Choi
Venue: ICML 2025 poster (2025)
Keywords: diffusion models
image editing
diffusion inversion
Primary area: deep_learning->generative_models_and_autoencoders
TL;DR: We introduce ENM Inversion, a technique that improves text-guided image editing by enhancing noise map editability.

Abstract: Text-to-image diffusion models have achieved remarkable success in generating high-quality and diverse images. Building on these advancements, diffusion models have also demonstrated exceptional performance in text-guided image editing. A key strategy for effective image editing involves inverting the source image into editable noise maps associated with the target image. However, previous inversion methods face challenges in adhering closely to the target text prompt. The limitation arises because inverted noise maps, while enabling faithful reconstruction of the source image, restrict the flexibility needed for desired edits. To overcome this issue, we propose Editable Noise Map Inversion (ENM Inversion), a novel inversion technique that searches for optimal noise maps to ensure both content preservation and editability. We analyze the properties of noise maps for enhanced editability. Based on this analysis, our method introduces an editable noise refinement that aligns with the desired edits by minimizing the difference between the reconstructed and edited noise maps. Extensive experiments demonstrate that ENM Inversion outperforms existing approaches across a wide range of image editing tasks in both preservation and edit fidelity with target prompts. Our approach can also be easily applied to video editing, enabling temporal consistency and content manipulation across frames.

Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation
Mingyu Kang'
Yong Suk Choi?
Abstract
1. Introduction
Text-to-Image diffusion models have demonstrated impres- sive performance in high-quality image generation (Rom- bach et al., 2022; Saharia al., 2022; Ramesh et al., 2022; Podell et al., 2023). Building on this success, recent re- search has extended the capabilities to text-guided image (Hertz et al., 2022; Brooks et al., 2023; Tumanyan et al., 2023; Kawar et al., 2023; Cao et al., 2023) and video edit- ing (Wu et al., 2023: Qi et al., 2023; Liu al., 2024) by leveraging these diffusion models. An important step in text-driven image editing involves inverting given image into sequence of noise vectors, termed noise maps. These noise vectors are used for editing. The goal of inversion is to produce editable noise maps that preserve the content of the original image and exhibit high editability.
Text-to-image diffusion models have achieved markable success in generating high-quality and diverse images. Building on these advancements, diffusion models have also demonstrated excep- tional performance in text-guided image editing. A key strategy for effective image editing involves inverting the source image into editable noise maps associated with the target image. However, previous inversion methods face challenges in ad- hering closely to the target text prompt. The limi- tation arises because inverted noise maps, while enabling faithful reconstruction of the source im- age, restrict the flexibility needed for desired ed- its. overcome this issue, we propose Editable Noise Map Inversion (ENM Inversion). novel in- version technique that searches for optimal noise maps to ensure both content preservation and ed- itability. We analyze the properties of noise maps for enhanced editability. Based on this analysis, our method introduces an editable noise refine- ment that aligns with the desired edits by mini- mizing the difference between the reconstructed and edited noise maps. Extensive experiments demonstrate that ENM Inversion outperforms ex- isting approaches across wide range of image editing tasks in both preservation and edit fidelity with target prompts. Our approach can also be casily applied to video editing, enabling tempo- ral consistency and content manipulation across frames.
Source Image
PNPInv
Ours
"a parrot leaf in the garden"
"a single dried rose in front of an orange wall"
Figure 1. ENM Inversion for real image editing. PNP Inversion (PNPInv) preserves the structure and content of the original image but shows limited editing capability. Our method preserves the details source image and enables precise modification.
In practice, many inversion methods rely on Denoising Dif- fusion Implicit Models (DDIM) inversion (Song et al., 2020) to obtain the latent noise representation of an image and then apply their proposed editing process. However, DDIM inversion often introduces approximation errors, resulting in noticeable inconsistencies between the reconstructed and original images. These errors are further amplified by the use of Classifier-Free Guidance (Ho & Salimans, 2022). To address this challenge, Null-Text Inversion (NTT) (Mokady
'Department of Artificial Intelligence, University of Hanyang, Seoul, Korea DDeparmeent of Computer Science. University of Hanyang, Scoul, Korea. Correspondence to: Yong Suk Choi <cys@@ hahhangnacckrr
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelit, Image Manipulation
xT
Attention
#
Attention Control
2r
Proposed inversion
Our inversion path
high distance
zf
Attention
Introduce artifact
DDIM inversion path
editable noise refinement
Ideal noise
Editing path
1
Attention
Reconstruction path
Attention Control
inversion
4i
Our Inversion Jatent
2f
Attention
4--1
Editabilty Prescrvation
Reconstucted latent
Edited latent
(n) Inversion process
(b) Editing process
Figure 2. The pipeline of image editng, (a) DDIM Inversion transforms the input image into noise maps that allow reconstruction of the original image. Additionally, ENM inversion minimizes the gap with ideal noise by applying editable noise refinement, enabling improved reconstruction and editability. (b) Utilizing attention control, the attention maps from the reconstruction path are transferred to the editing path. Our inversion enhances both editability and preservation, resulting in the desired output image.
et al., 2023) optimizes the null-text embedding to improve reconstruction quality. Negative-Promp Inversion (NPI) (Miyake et al., 2023) attempts to approximate the optimized null-text embedding, thereby reducing inference time. PNP Inversion (Ju et al., 2024) more efficiently preserves the essential content in the source image by adding the differ- ences between the inverted and reconstructed noise maps. Furthermore, several works, such as fixed-point iteration methods (Pan et al., 2023; Meiri et al., 2023), reduce the ap- proximation errors each timestep by optimizing implicit function.
editable noise refinement that searches for the ideal noise maps, which are optimized for both preservation and ed- itability, as shown Figure 2(a). Our approach leverages the observation that high-quality edits can be effectively achieved by aligning reconstructed and edited noise maps in each inversion step. By reducing the difference between two versions of noise vectors, we can effectively imprint the desired image onto the noise and improve editability, Additionally, to maintain the content of source image, we decrease reconstruction errors the inverted noise maps. This ensures that ENM Inversion achieves an ideal balance between preserving the details of the original image and edit fidelity.
Despite these advancements in diffusion inversion, editabil- ity remains significant challenge. The primary reason is that the noise maps obtained through DDIM Inversion are designed to reconstruct the source image rather than gener- ate edited output. That is, the target image is not "imprinted" onto the noise maps. Moreover, much of the existing re- search focuses on enhancing image reconstruction quality to improve preservation, resulting in poor editing performance and artifacts in the edited image (e.g., failing transform butterfly into parrot, as shown in Figure 1).
Our proposed approach can be applied to attention based image editing pipelines, such as Prompt-to-Promp (Hertz et al., 2022), MasaCtrl (Cao et al., 2023), and Plug-and- and-Play (Tumanyan al., 2023). Experimental results demonstrate that ENM Inversion outperforms existing methods across diverse image editing tasks both quantitatively and qualita- tively. In addition to image editing, ENM Inversion can be extended to video editing, enabling manipulation of visual content across frames.
In this paper, we propose Editable Noise Map Inversion (ENM Inversion). Unlike previous approaches that concen- trate on reconstructing the source image, ENM Inversion identifies optimal noise maps tailored for both the original and edited images. To achieve this objective, utilize
2

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Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation
2. Related Work
Spiegelglas al., 2024), often exhibits instability in editing, particularly in content preservation and edit fidelity due to the stochastic nature of the process.
As shown in Figure 2, diffusion-based editing pipelines typ- ically consist of two processes: an inversion process, which transforms the input image into Gaussian noise, and an edit- ing process, which commonly employs attention control to modify the image.
Since inverted noise maps are designed to reconstruct the source image, existing methods often face challenges in achieving high fidelity to both the original content and the desired edits. Unlike previous approaches. ENM inversion aims to encode the target image into noise maps. This en- ables our method to achieve content consistency and precise editing. Furthermore, by integrating our inversion with exist- ing attention- based editing pipelines, such as P2P, MasaCtrl, we demonstrate a significant improvement in both content preservation and edit fidelity.
Editing with Diffusion Models. Text-to-image diffusion models such as Stable Diffusion (Rombach et al., 2022), Imagen (Saharia et al., 2022), and DALL-E2 (Ramesh et al., 2022) have significantly advanced the field of image genera- tion. Leveraging these powerful models, current methods (Meng et al., 2021; Hertz et al., 2022; Patashnik et al., 2023; Park et al., 2024; Hertz et al., 2024) apply their capabili- ties to text-guided image editing without additional training. Crompt-to-Pronyy (P2P) (Hertz et al., 2022) was the first to achieve remarkable localized modifications by manipulating cross-attention maps in Stable Diffusion. As illustrated in Figure 2(b), P2P replaces the attention maps of the editing path with the corresponding maps of the reconstruction path. Plug-and-Pl Play (PNP) (Tumanyan et al., 2023) is another ap- proach that modifies spatial features and self-attention maps to control the structure of the generated image. Pix2pix- Zero (Parmar et al., 2023) utilizes cross-attention guidance for zero-shot image-to-image translation. MasaCtrl (Cao et al., 2023) converts the self-attention diffusion into mu- tual self-attention, enabling non-rigid edits. Recent studies (Wu et al., 2023; Wang et al., 2023; Liu et al., 2024) have adopted this technique for video editing, which aims to modify the source video contents according to text prompts. When applied to real-world images, these editing methods require inverting the images into the noise vectors.
3. Method
3.1. Preliminaries
Text-guided Diffusion Model. Diffusion models are gener- ative models that progressively add Gaussian noise to data through forward process and denoise it through a reverse process. The forward process can be formulated as:
21 Va + v1 are, e~~100 I),
(I)
where zu represents the original data, 2, is the noisy data at timestep I, and (ax)== is predefined noise schedule for E [1,
The training objective of diffusion models involves learning noise prediction network, eg. parameterized by a neu- ral network. The network is conditioned on embedding C, which is obtained the given text P and is optimized to minimize the loss:
Inversion with Editing Methods. Inversion techniques are broadly categorized into DDIM-based and BaMM- methods. DDIM inversion is a widely adopted method that estimates the noise map to reconstruct the original image through deterministic sampling. However, when Classifier- Free Guidance is applied, this method incurs significant errors, leading to poor reconstruction. Optimization-bant approaches, such as Null-Text Inversion (NTI) (Mokady et al., 2023), and Negative-prone prompt Inversion (NPI) (Miyake et al., 2023) adjust text embedding to achieve accurate re- construction. Methods like EDICT (Wallace et al., 2023; Zhang et al., 2025) offer an exact inversion via an auxiliary neural network. PNP Inversion (Ju et al., 2024) more effi- ciently preserves the essential content in the source image by disentangling the inversion process into distinct source and target branches. AIDI (Pan et al., 2023), FPI (Meiri al., 2023) and ReNoisc (Garibi et al., 2024) utilize fixed-poin iteration processes at each inversion step to obtain accurate noise maps. While these methods achieve high-quality re- construction, they often exhibit reduced editability. Instead of a deterministic method, DDPM inversion (Huberman-
Ldn = Exg.c,t [[]c c0(2,,, (,2,,,,C)) C)
DDIM Inversion. DDIM inversion is useful for real image editing as it allows encoding an image into the latent space of the diffusion model. DDIM employs deterministic reverse process to generate an image from 2y:
0t-1
21-1
28
(3)
Va,
v
1
Vat-
(1 a0-1
c c0(21,
This process can be written as 2t-1 + f(a,, C). Based on the assumption that the ODE formula can be reversed in the limit of infinitesimally small steps, DDIM inversion can be derived from Equation (3):
3

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Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation
Algorithm Editable Noise Map Inversion
Input: A source image 20. number of inversion steps T. source prompt Pares target prompt Pigt, number of refinement steps K., threshold Output: Inverted noise maps (2T,...., 21). for t + 1, 1,2, T do +
01++1
F(+1
2,
(4)
Vat
vat+1
t, C).
01+1
1,,,,, Cerc)
= - f(2, t, Czgt)
We can summarize the inversion process as 2t (in(22-1,-11,,0 DDIM inversion introduces approxima- tion errors at each timestep, leading to a significant decrease in both reconstruction accuracy and editability. To minimize these errors, fixed-point iteration methods compute the ideal noise for the source image by solving implicit functions. This approach improves reconstruction quality but exhibits limited editability.
for f l....,KK
2 f f Cerc)
Ledit || 24-1112 1//2
Lrecon 1 2--1 1112
24 V(Iracon X
if Lrecon + Abedit < T then
Break
end end for end for
20.0
25.5
wolf
Better performance
25.0
To investigate the properties of noise maps that lead to ef- fective manipulation, analyze cases where editing has succeeded and failed. Using AFHQ images (Choi et al., 2020), we perform various edits where the source image of a dog is transformed into different target objects (e.g., a cat, a penguin). We focus on the differences between the recon- structed noise map and the edited ones at the 30th inversion step, evaluating their impact on editing performance. As shown in Figure 3, we observe that smaller differences be- tween the reconstructed and edited noise maps have strongly correlated with better editing performance. Similar trends are observed across other inversion steps, and a detailed analysis of these is provided in Appendix B.
camel
24.5
CUP
24.0
penguin
elephent
23.5
23.0
22.5
0.32
0.34
0.36
0.38
0.40
0.42
LPIPS
Figure 3. Relationship Between Editing Performance and Noise Map Differences. Fditing performance evaluated using I.PISS, which measures perceptual similarity, and CLIP score, which as- sesses alignment with the target prompt. The size each circle indicates the magnitude of differences between the reconstructed and edited noise maps at the 30th inversion step. Smaller noise map differences correlate with better editing performance.
By leveraging these observations, we implement an editable noise refinement. Specifically, we align the inversion di- rection towards the ideal noise at each inversion step by reducing the difference between the reconstructed and the edited noise maps:
3.2. Editable Noise Map Inversion
Tredst = [1,( 1, Care) - f(z L, Ctgt)lla
(5)
Editable Noise Map Inversion (ENM Inversion) proposes method for inverting noise maps that are suitable for both reconstruction and editing. The primary observation moti- vating our approach is that noise maps, which enable high- quality edits, exhibit minimal differences between those reconstructed with the source prompt and those edited with the target prompt. Our analysis is inspired by Pix2pix-Zero (Parmar al., 2023), which computes editing directions by using changes in attention maps between reconstructed and edited noisy inputs. Unlike attention maps, which mainly reflect high-level semantic relationships, noise maps inher- ently capture low-level structural details and spatial context. Therefore, we analyze the differences between reconstructed and edited noise maps to improve the inversion process.
where C.v and Cigi represent the source and target text embedding, respectively.
In addition, to prevent the noise maps from deviating ex- cessively from the source image, ENM Inversion incorpo- rates an additional term to reduce the reconstruction error E.prev = || - f(2,, 1, Care)ll2. To improve editability and content preservation, our method iteratively updates 2, by minimizing the following loss function:
argmin L = Lpvev + XLedit 2+
(6)
where, À is a hyperparameter weighting factor for the edit-

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Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation
Table Quantitative comparisons of inversion methods across text-guided image editing techniques: Prompt-to-Promme (P2P) (Hertz al.. 2022), MasaCtrl (Cao et al., 2023), and Blug-and-Pla Play (PnP) (Tumanyan et al.. 2023), on the PIE- PIE-Bench dataset. Our method consistently outperforms other baseline methods editing performance. Best results are highlighted bold, and second- best results underline.
Method
Structure
Background Preservation
CLIP Similariy
Inverse
Editing
Distance x18
PSNR
LPIPS 100
MSE x
SSIM x102 f
Whole t
Edited
DDIM
P2P
69.43
17.87
208.80
219.88
71.14
25.01
22.44
NTT
P2P
13.44
27.03
60.67
35.86
84.11
24.75
21.86
StyleD
P2P
11.65
26.05
66.10
38.63
83.42
24.78
21.72
NMG
P2P
22.83
26.01
79.42
109.53
82.40
24.53
21.60
P2P
18.05
24.55
91.88
94.58
81.57
23,97
21.03
PNPIny
P2P
11.65
27.22
54.55
32.86
84.76
25.02
22.10
Ours
P2P
10.13
28.19
45.26
27.02
86.29
25.30
22.12
DDIM
MasaCtrl
28.38
22.17
106.62
86.97
79.67
23.96
21.16
NMG
MasaCtrl
38.72
20.33
127.21
135.03
77.48
24.54
21.32
PNPInv
MasaCtrl
24.70
22.64
87.94
81.09
81.33
24.38
21.35
Ours
MasaCtrl
22.89
23.01
83.99
77.55
82.34
24.62
21.44
DDIM
PnP'
28.22
22.28
113.46
83.64
79.05
25.41
22.55
PNPInv
PnP
24.29
22.46
106.06
80.45
79.68
25.41
22.62
Ours
PnP
18.44
25.32
78.53
46.34
83.57
25.57
22.63
use Stable Diffusion v1.5 base model (others all Stable Diffusion v1.4)
ing alignment term. Furthermore, for efficient inversion, we introduce a pre-defined threshold at each timestep t. By iteratively refining 2, to minimize this loss, ENM Inver- sion imprints the target image more strongly onto the noise maps while preserving content from the source image. We summarize our proposed process in Algorithm
temporal coherence between frames.
4. Experiments
4.1. Experiments Setup
Datasets. To evaluate the effectiveness and efficiency of our proposed ENM Inversion, we use two datasets. For image editing, et al., 2024), which contains high-resolution images, each accompanied by distinct editing tasks, covering a diverse range of semantic and structural modifications. For video editing, we follow previous works (Bar- Tal et al., 2022; Liu et al., 2024) and use videos from the DAVIS dataset (Pont- Tuset al., 2017) and the Internet to evaluate our approach. The two datasets consist of paired source and target prompts, along with target region masks for localized evaluation.
3.3. Extension to Video Editing
the PlE-Bench introduced by (Ju
Video editing focuses on modifying source video content according to a given text prompt. Recent methods such Lune-A-Video (TAV) (Wu et al., 2023) and Video-P2P (Liu et al., 2024) have extended text-to-image diffusion models to the video domain, enabling text-guiee got-gg video editing. However, these approaches treat video frames as a whole for editing, which often results in poor editability and temporal inconsistency between frames.
To overcome these limitations of video editing, inte- grate inversion into Video-P2P. First, fine-tune image diffusion model for text-to-video modeling. Subse- quently, we utilize ENM Inversion to invert each frame of the source video into noise maps that preserve the struc- ture of the original video and provide enhanced editability. Finally, we apply attention control across all frames to en- sure consistent modifications throughout the video. This approach addresses limitations in previous methods by abling high-quality edits while maintaining structural and
Comparison Methods. We quantitatively and qualitatively compare ENM Inversion with existing inversion techniques. These include DDIM Inversion (Song et al., 2020). Null- Text Inversion (NTT) (Mokady et al., 2023), StyleDiffusion (StyleD) (Li al., 2023). Noise Map Guidance (NMG) (Cho et al., 2024), Edit-Friendly DDPM Inversion (EF) al., 2024), and PnP Inversion (PNPInv) (Ju et al., 2024). For image editing, these inver- sion methods are integrated with three text-guided image editing techniques: Compt-to-Promppe (P2P) (Hertz et al.,
Sabberman-Spicgelgeeee
5

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Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation
A colorful car is parked on the street - A colorful motorcycle is parked on the street
Gold buddha statue in the temple - Stone buddha statue in the temple
A woman with glasses sitting on stone wall A woman sitting on stone wall
The Christmas illustration of santa's laughing face The Christmas illustration of santa's angry face
A woman with dark hair + A woman with dark hair wearing earrings
red rose in the dark A blue rose in the dark
Source Image
DDIM
NTI
StyleD
NMG
EF
PNPInv
Ours
Figure Qualitative comparisons various inversion methods using Prompt-to-Prome (P2P) (Hertz al., 2022). Other inversion techniques result in an inconsistent background or structure with the source image exhibit limited editing capabilities. Our approach not only retains high fidelity to the source image but also demonstrates superior editing capabilities.
2022), MasaCtrl (Cao et al., 2023), and Plug-and-Pla (PnP) (Tumanyan et al., 2023). For video editing, DDIM Inversion and NTI are applied to Tune-A- Video (TAV) (Wu et al., 2023) and Video-P2P (Liu al., 2024), respectively.
(Zhang et al., 2018), MSE, and SSIM (Wang et al., 2004). Background preservation specifically computed in the unedited regions, defined by areas outside the editing mask. Additionally, we evaluate target prompt-image consistency by computing CLIP Similarity (Wu et al., 2021) for the entire image and the regions within the editing mask. For video editing, we further evaluate temporal consistency by incorporating the Temp metric (Esser et al., 2023).
Evaluation Metries. To quantitatively assess the perfor- mance of method across various aspects, utilize the metrics established by PNPInv. We measure structure distance using the DINO score (Tumanyan et al., 2022), and assess background preservation through PSNR, LPIPS
Implementation Details, We perform all experiments on
6

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Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation
[[aput Video camel walking a fenced area.
[Input Video] A brown bear walking on the beach.
[DDIM TAV| elephant walking fenced
[DDIM TAV] A bear walking the grass.
D
[NTI Video-P2PJ A elephant walking in fenced in area.
[NTI Video-P2P/ A brown bear is walking the
D
[Ours Video-P2PP P elephant walking in fenced in area.
[Ours Video-P2PI P| A brown bear walking on the grass.
Figure 5. Qualitative comparison of our inversion using Video-P2P (Liu et al., 2024) for video editing. Our method demonstrates superior
performance in terms of temporal consistency, content maintenance, and editing quality, when modifying backgrounds or objects.
a single RTX3090. Following previous studies (Ju et al., 2024; Liu et al., 2024), we use Stable Diffusion V1-4 for P2P and MasaCtrl, while Stable Diffusion V1-5 is used for PnP, TAV, and Video-P2P. We employ a DDIM schedule with 50 steps and apply Classifier-Free Guidance of 7.5 for the editing process. For editing experiments, adapt the default parameters for cross-attention injection and self- attention injection.
Qualitative Results. We present
Table Inference time
a visual comparison of inversion methods using P2P Figure Our approach consistently sur- passes competing methods in ious editing tasks, such chang- ing content, removing an object, and modifying attributes like ma- terial or color. Existing meth- ods struggle with various editing tasks such as changing car into
of inversion techniques.
Method
Time
DDIM
18,22
NTI
148 48
StyleD
382.98
NMG
36.48
EF
19.10
PNPInv
28.17
4.2. Comparison with Image Editing
Ours
38.87
Quantitative Comparisons. Table 1 provides the quantita- tive evaluations of various inversion methods on PIE-Bench. Compared to existing methods, ENM Inversion achieves no- table improvements in most metrics. Our approach demon- strates superior performance in text-image alignment and structure preservation, achieving higher CLIP Similarity, DINO Score, PSNR, and SSIM, while maintaining lower LPIPS and MSE values.
motorcycle (the 1st row), removing glasses from face (the 3rd row), and modifying the color of rose (the 6th row). NTI faces challenges in maintaining the details of the original image (the 2nd and 3rd rows). Furthermore, both NTT and PnPInv attempt to modify smiling face to an an- gry expression but leave residual traces the smile (the 4th row). EF often introduces noise artifacts into the generated image (the 4th and 6th rows). In contrast, our method excels in preserving structure of the original image while achieving higher alignment with the text prompt compared to existing approaches. For additional qualitative results using other editing methods, please see Appendix F.
We also compare the inference time of different inversion methods integrated with P2P, as shown Table 2. ENM Inversion achieves better editing performance and more
efficient than NTI and StyleD.

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Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation
Table Quantitative comparisons of performance between fixed-point iteration methods and our approach the PlF--Bnnhh dataset. Following their settings, AIDI and FPI are combined P2P, while ReNoise utilizes the img pipeline of Stable Diffusion V1-4.
Method
Structure
Background Preservation
CLIP Similariy
Inverse
Editing
Distance, 108
PSNR
LPIPS x103
MSE x04
SSIM x102
Whole t
Edited
DDIM
P2P
69.43
17.87
208.80
219.88
71.14
25.01
22.44
AIDI
P2P
12.19
26.96
57.92
39.82
84.17
24.96
22.01
FPI
P2P
14.71
26.61
61.97
37.64
83.52
23.93
21.35
ReNoise
22.60
25.19
85,29
49.51
82.30
23.78
21.15
Ours
P2P
10.13
28.19
45.26
27.02
86.29
25.30
22.12
result occurs because these methods focus on solving an implicit function to ensure accurate reconstruction of the souree image, leading to lower editability. In contrast, ENM Inversion optimizes a loss function that refines the noise map for editing, aligning it with the target image while maintain- ing details from the source. This allows us to enhance both content preservation and edit performance simultaneously.
Table Quantitative evaluation against baselines in video editing. We measure target text alignment (CLIP Score), background preser-
vation (LPIPS and SSIM), and temporal consistency (TEMP).
Method
CLIP f
LPIPS 1
SSIM f
TEMP f
DAI
26.13
169.80
68.02
0.9464
NTI+Video-P2P
26.14
104.64
74.74
0.9451
Ours+ PPde-PPd
26.57
98.24
75.43
0.9454
D0PM
Durs
4.3. Comparison with Video Editing
Aibentcco a Algemnn (?)
Quantitative Comparisons. We present the quantitative evaluation results in Table 4, which compare the perfor- mance of our method with state-of-the art models. Com- pared DDI and NTI+Video-P2P, we achieve higher CLIP Score, SSIM, and lower LPIPS, demonstrat- ing better alignment with text, superior structural integrity, and perceptual accuracy. Moreover, our approach produces these results in significantly less time compared to NTI. Table shows that ENM inversion excels text fidelity and content preservation, delivering high-quality video with improved efficiency.
Denosing Stop
Figure 6. Cross-attention alignment for image editing across de- noising steps with different inversion techniques.
presents the results of video editing. DDIM+TAV struggles to preserve the content of the input video accurately. NTI+Video-P22 PPP fail to consistently align with the user-specified prompts, leading to the genera- tion of artifacts and unrealistic outputs. Both baselines also exhibit poor editing performance due to limited editability of the latent noise (right side of Figure 5). In contrast, our inversion with Video-P2P performs temporally consistent video editing, while preserving fine details of the original video and achieving high edit fidelity.
Qualitative Results. Figure 5
4.5. Exploring the Editability of Noise Maps
Since the editing performance is measured solely based CLIP Similarity, we add experiments for the editability of noise maps. To assess edit fidelity, we evaluate cross- attention alignment for image editing across denoising steps (Tumanyan et al., 2023; Butt et al., 2025). Specifically, we compute the cross-attention map corresponding to the target text and measure its overlap with the target region mask. The alignment is denoted as score [AA, M/) Σ As- A represents the cross-attes -tttenttntn map at timestep and M is the binary mask indicating the edited region. We compare our method against DDIM Inversion and DDPM Inversion, applying P2P in Figure 6. DDIM Inversion ini- tially improves alignment at early timesteps, and gradually declines after certain point, leading to reduced editability.
4.4. Comparison With Fixed-Point Methods
In Table 3, we compare ENM Inversion with fixed-poin exen-point iteration methods for image editing. Fixed-point iteration methods (Pan et al., 2023; Meiri et al., 2023; Garibi et al., 2024) improve the structure and background preservation DDIM Inversion, but clip similarity has decreased. This
8

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Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation
DDPM Inversion achieves better performance than DDIM but exhibits instability and low initial alignment due to its stochastic nature. Our method achieves higher scores than both inversion techniques. Furthermore, unlike DDPM In- version, which suffers from instability, our approach ensures stable improvement in alignment across timesteps. With our editable noise maps, we can effectively generate edited im- ages.
707-723. Springer,
Tprinerr 2022.
Brooks, T., Holynski, A., and Efros, A. A. Instructpix2pix: Learning follow image editing instructions. Proceed- ings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pp. 18392-18402, 2023.
Butt, M. A., Wang, K., Vazquez-Corral, J., and van de Wei- jer, J. Colorpeel: Color prompt learning with diffusion models via color and shape disentanglement, In European Conference on Computer Vision, pp. 456-472. Springer, 2025.
5. Conclusion
In this paper, we propose ENM Inversion, an effective in- version technique for high-quality real image editing. By refining noise maps to align with both the source and tar- get images, ENM Inversion encodes the target image more strongly into the noise maps, thus enabling high-quality edits while preserving the details of the source image. Ex- perimental results demonstrate the superiority of our method over existing methods in various image editing tasks. More- over, with the high edit flexibility of our noise maps, our approach can be easily extended to video editing.
Cao, M., Wang, X., Qi, Z., Shan, Y., Qie, X., and Zheng, Y. Masactrl: Tuning-free mutual self-attention control for consistent image synthesis and editing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 22560-22570, 2023.
Cho, H., Lee, J., Kim, B., Oh, T.-H., and Jeong, Y. Noise map guidance: Inversion with spatial context for real image editing, arXiv preprint ar arXhv:240.006625, 2024.
Acknowledgements
Choi, Y., Uh Y., Yoo, J., and Ha J.-W. Stargan v2: Diverse image synthesis for multiple domains. In Proceedings of the IEEF/CVF conference on computer vision and patlern recognition, pp. 8188-8197, 2020.
This work was supported by the National Research Founda- tion of Korea (NRF) grant (No. the Institute of Information and communications Technol- ogy Planning and evaluation (IITP) grant (No. RS-2020- 11201373), funded by the Korean Government (MSIT: Min- istry of Science and Information and Communication Tech- nology).
8) and
Esser, P., Chiu, J., Atighehchian, P., Granskog, J., and Ger- manidis, A. Structure and contnttgguide video synthesis with diffusion models. In Proceedings of the IEEF/CVF International Conference on Computer Vision, pp. 7346- 7356, 2023.
Impact Statement
Esser, P., Kulal, S., Blattmann, A., Entezari, R., Müller, J., Saini, H., Levi, Y., Lorenz, D., Sauer, A., Boesel, F,, et al. Scaling rectified flow transformers for high-resolution image synthesis. In Forty-first international conference machine learning, 2024.
This work makes a contribution to the advancement of text- guided image and video editing using diffusion models. From a research perspective, we introduce new approach improving editability diffusion inversion and demon- strate strong performance through extensive experiments. From industrial perspective. our proposed method holds potential benefits for various practical applications, includ- ing creative content generation, personalized media editing, and educational tools that rely on expressive visual modifi- cations.
Garibi, D., Patashnik, O., Voynov, A., Averbuch-Elo H., and Cohen-Or, D. Renoise: Real image inversion through iterative noising. arXiv preprint arXiv:2403. /4602, 2024.
Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., and Cohen-Or, D. compt-tto-rrompe im- age editing with cross attention control. arXiv preprint arXiv:208..01666 2022.
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Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation
Table Analysis Hyper-parammeers of ENM Inversion added with Prompt-to-Prompll (P2P) (Hertz al., 2022).
Structure
Background Preservation
CLIP Similariy
Parameters
Distance x 1005
PSNR f
LPIPS x10
MSE x10
SSIM x102
Whole
Edited
=
10.10
28.22
45.19
26,91
86.23
25.17
22.03
À 10
10.13
28.19
45.26
27.02
86.29
25.30
22.12
= 15
10.38
28.13
46.33
27.25
86.15
25.32
22.07
A 20
12.41
28.01
46.80
27.30
86.12
25.34
22.08
T 20
8.74
28.50
44.05
25.19
86.35
24.95
21.71
T' 50
10.13
28.19
45.26
27.02
86.29
25,30
22.12
T' 75
10.35
28.10
46.47
27.68
86.11
25.35
22.13
T= 100
10.89
28.01
48.03
27.91
86.05
25.44
22.20
Default
10.13
28.19
45.26
27.02
86.29
25.30
22.12
A. Limitations
Our proposed method has several limitations. First, it relies on the generative capabilities of Stable Diffusion. As result, target image lies outside the domain that Stable Diffusion can generate, our method may fail edit the image effectively. Another limitation lies in computational efficiency. Unlike existing methods that perform single inversion per source image and reuse the result across multiple target prompts, our approach requires separate inversion process for each target text-image combination. This increases the computational cost, especially in scenarios involving multiple edits of the same image. Additionally, the optimizing of noise maps introduces further inference time. While this added cost is relatively small, as shown in Table 2, it may still pose challenges in real-time applications.
B. Analysis of Noise Map Differences Across Inversion Steps
We analyze the differences between the reconstructed and edited noise maps across various inversion steps better understand their impact editing performance. Figure (left) provides analysis of noise map differences across inversion steps for various editing cases. For editing cases with lower performance (e.g.. transforming dog into elephant or giraffe), we observe larger gaps across inversion steps. This indicates that greater deviations between the reconstructed and edited noise maps can lead to poorer editing outcomes. Figure (right) illustrates the relationship between editing performance and noise map changes at the 30th inversion step. Compared to DDIM inversion, our approach achieves a smaller gap between the reconstructed and edited noise maps, which leads to higher editing performance.
16-3
Ous
12
#xcherl
20ngin
25.0
Better
Scors
0.6
34.0
0.4
25.
20
Figure 7. Noise map differences across inversion steps for various editing cases (left). Relationship between editing performance and noise map differences the 30th inversion step (right). The size of and circles represents the magnitude of the difference.
12

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Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation
Table Quantitative comparisons of performance hetween flow-based models and our approach on the PIE--ennch dataset. Our method outperforms other now-based methods in editing performance.
Structure
Background Preservation
CLIP Similariy
Method
Distance
+
PSNR
LPIPS x103
MSE +
SSIM x102 t
Whole t
Edited
SDEdit-Flux RFInv
118.97
14.41
329.92
450.06
60.82
25.06
22.50
60.08
18.24
232.88
210.02
64.78
24.94
22.65
Ours RFIny
46.38
19.77
185.86
153.90
69.57
25.05
22.65
Table Comparison of reconstruction quality when using source prompt Care and null text during inversion.
Structure
Background Preservation
Method
Distance x100 +
PSNR t
LPIPS x103 4
MSE x104
SSIM x102
f
Care Null
10.13
28.19
45.26
27.02
86.29
9.89
28.18
46.28
27.13
86.11
C. Analysis of Hyper-parameters
In Table 5, we provide further experiments to analyze the impact of different choices in our method. As the editing alignment weight À increases, the performance in preserving the structure and background of the image decreases, while the CLIP similarity improves. This trend indicates that a larger À amplifies the editability, increasing the likelihood edits but also the probability of affecting unintended regions of the image. Additionally, increasing À requires more inference time. To maintain balanced performance without excessive computational overhead, we select À 10 practical choice.
Furthermore, we conducted experiments with DDIM sampling steps T set to 20, 50, 75, and 100 in ENM Inversion. Fewer steps resulted in better preservation of background and structure, while higher step counts improved CLIP similarity. To achieve balanced performance across all metrics, we set T = 50 as the default setting.
D. Comparison with Flow-Based Models
While diffusion-based methods have demonstrated strong capabilities in reconstructing and editing images, recent advances in flow-based models offer an alternative and promising direction. In particular, Rectified Flow (RF) models (Liu et al., 2022; Albergo & Vanden-Eijnden, 2022; Esser et al., 2024) have emerged as an efficient generative framework that leverages reverse Ordinary Differential Equations (ODEs) instead of the Stochastic Differential Equations (SDEs) commonly used in diffusion models. Recent works have explored inversion techniques tailored for Rectified Flows, introducing algorithms such as RF Inversion (RFInv) (Rout al., 2024). Our method can integrated into these methods. We conducted additional experiments using the following baselines: SDEdit (Meng al., method significantly improves reconstruction quality while preserving the edit fidelity inherent in flow-based models.
RFInv, and Ours RFInv. As shown in Table 6,
E. Robustness to Different Source Prompts
In our method, accurate image reconstruction during inversion is achieved by minimizing Lprev as defined in Equation (6). This loss computed using the denoising function f'( 4, Cero), where Care is the source prompt provided during inversion. To validate the robustness with respect a different choice of C're. we conduct an additional experiment by setting Care to null text and comparing the quality against the where the actual source prompt is used. Table show that there is no significant difference in reconstruction quality between the two settings.
F. Additional Qualitative Results
We present additional qualitative results using the PIE-Bench dataset. Figure 8, Figure 9, and Figure 10 show comparisons of inversion methods combined with Prompt-to-Pronet MasaCtrl, and Plug-and-Plaa techniques. Furthermore, Figure 11
13

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Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelity Image Manipulation
provides additional visualizations for video editing.
view of the mountains covered in A view of the mountains covered in leaves
in jacket standing rain
woman blouse standing the rain
A black raven sits on tree stump in the rain - A white raven sits on tree stump in the rain
long haired cat looking up at something A short haired cat looking something
digital with curly hair standing in front of buildings digital with straight hair standing in front of buildings
A watercolor illustration chicken background illustration chicken on white background
Source Image
StyleD
NMG
PNPInv
Ours
Figure 8. Qualitative results of different inversion methods with Prompt-to- Trompt-to-Prompt (P2P).
14

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Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelit, Image Manipulation
An illustration cat sitting on top of rock - An illustration a bear sitting on top rock
A bee flies over flowering tree branch A flowering tree branch
woman a and walking down woman a running
A serious man angry man
A lone tree is reflected the water at night with bright moon lone tree reflected the water at night
Source Image
DDIM
NMG
PNPInv
Ours
Figure 9. Qualitative results of different inversion methods with MasaCtrl.
15

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Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelit Image Manipulation
A cat sitting in the rocks A cat sitting in the
A puppy sitting field of dandelions A puppy is sitting in field
wearing tie wearing black and yellow stripes tic
young girl
Black and white sketch a young girl
White flowers on tree An painting white flowers on a tree
Source Image
DDIM
PNPInv
Figure 10. Qualitative results different inversion methods with Plug-and and-Play (PnP).
16

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Editable Noise Map Inversion: Encoding Target-image into Noise For High-Fidelit, Image Manipulation
|Input Video| breakdancing the street.
[Input Videol bird flying the forest.
[DDIM + TAVI A man breakdancing on the beach.
|DIM TAVI An origami bird flying in the forest.
INTI Video- | A man breakdancing on the beach.
INTI Video- An origami bird flying in the forest.
Video-P2PI breakdancing the beach.
Video-P2PI origami bird flying in the
Figure 11. Qualitative results different inversion methods Plug-and- and-Play (PnP).
17


Reviews:
[{"review_id": "gbYux0PJsm", "reviewer": "Reviewer_RLWR", "summary": "The paper introduces Editable Noise Map Inversion (ENM Inversion), a technique that improves both reconstruction quality and editing capabilities in diffusion-based image editing. ENM optimizes noise maps during inversion by minimizing the differences between reconstructed and edited versions, effectively encoding the target image's intended edits directly into the noise representation.", "questions": "", "limitations": "", "rating": 3, "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": "First of all, we sincerely appreciate your time and effort in reviewing our research. Below, we provide responses to all the points raised.\n\n**Other Strengths And Weaknesses:**\n> The discussion of method efficiency is insufficient. While competing methods require only one inversion calculation per source image that can be applied across multiple target texts, the proposed method demands a separate inversion process for each target text and image combination—making it computationally expensive when performing multiple edits on a single image.\n\n**Answer:**\nThank you for your insightful comments. It is indeed correct that our ENM Inversion method requires a separate inversion process for each text-image combination. This design choice was made to generate an optimal noise map tailored to each specific editing requirement. To compensate for efficiency while maintaining performance, we have carefully optimized our approach. As shown in Table 2, our method significantly reduces computational costs compared to NTI and StyleD in terms of inference time. This demonstrates that ENM Inversion can be a relatively efficient methodology for various image editing tasks. Once again, we sincerely appreciate your valuable feedback.\n\n**Essential References Not Discussed & Questions for Authors:**\n> Have you considered extending or comparing your approach to other generative modeling frameworks, such as flow-based models? If so, how does your method perform relative to such alternatives?\n\n**Answer:**\nSince our research focused on the inversion of diffusion models, we did not include a comparison with flow-based models. However, based on the reviewer’s suggestion, we conducted additional experiments incorporating ProxEdit and flow-based models [1][2], and we have included the results in the Appendix. Additionally, we integrated RF-Inversion into our method and conducted further experiments.\n\n| **Method** | **Structure Distance** ↓ | **PSNR** ↑ | **LPIPS** ↓ | **MSE** ↓ | **SSIM** ↑ | **CLIP Similarity (Whole)** ↑ | **CLIP Similarity (Edited)** ↑ |\n|--------------|----------------------------|------------|------------------|------------------|------------------|-------------------------|-------------------------|\n| ProxEdit   | 11.87                        | 27.12      | 45.70            | 31.70            | 85.73            | 24.13                   | 21.36                   |\n| SDEdit-Flux [1]  | 118.97                        | 14.41      | 329.92            | 450.06            | 60.82            | 25.06                   | 22.50                   |\n| RFInv [2]  | 60.08                        | 18.24      | 232.88            | 210.02            | 64.78            | 24.94                   | 22.65                   |\n| Ours + RFInv  | 46.38                        | 19.77      | 185.86            | 153.90            | 69.57            | 25.05                   | 22.65                   |\n| Ours  |  10.13                        | 28.19      | 45.26            | 27.02            | 86.29            | 25.30                   | 22.12                   |\n\nTable 1. Results of comparing and combining our method with flow-based models.\n\nThe table above demonstrates that our approach outperforms editing methods using flow-based models in terms of structural distance, background preservation, and editability. Furthermore, integrating RF-Inversion into our method leads to even greater performance improvements.\n\nWe thank the reviewer for engaging with us in the discussion.\n\n**Reference**\n\n[1] SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations\n\n[2] Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations", "claims_and_evidence": "Yes.", "methods_and_evaluation_criteria": "The experimental design is sound, employing well-established benchmarks (PIE-Bench and DAVIS) alongside widely accepted evaluation metrics in diffusion-based image editing research (LPIPS, PSNR, SSIM, and CLIP similarity).", "theoretical_claims": "This paper does not contain explicit theoretical proofs. It primarily contributes algorithmic and empirical insights rather than theoretical claims.", "experimental_designs_or_analyses": "The authors carefully selected appropriate baseline comparisons and established metrics to objectively measure editing performance and fidelity.", "relation_to_broader_scientific_literature": "The key contributions build on existing diffusion inversion techniques, including DDIM, Null-Text Inversion, Negative Prompt Inversion, and Plug-and-Play methods.", "essential_references_not_discussed": "Han, Ligong, Song Wen, Qi Chen, Zhixing Zhang, Kunpeng Song, Mengwei Ren, Ruijiang Gao et al. \"Proxedit: Improving tuning-free real image editing with proximal guidance.\" In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 4291-4301. 2024.", "other_strengths_and_weaknesses": "The discussion of method efficiency is insufficient. While competing methods require only one inversion calculation per source image that can be applied across multiple target texts, the proposed method demands a separate inversion process for each target text and image combination—making it computationally expensive when performing multiple edits on a single image.", "other_comments_or_suggestions": "Additional visual examples showing challenging scenarios or failures of the proposed method could better illustrate practical limitations.", "questions_for_authors": "Have you considered extending or comparing your approach to other generative modeling frameworks, such as flow-based models? If so, how does your method perform relative to such alternatives?", "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": "Eu6z7fMwxj", "reviewer": "Reviewer_Qit8", "summary": "This paper propose ENM Inversion, a technique for high-quality real image editing. By refining noise maps to align with both the source and target images, ENM Inversion encodes the target image more effectively into the noise maps, allowing for high-quality edits while preserving the source image's details.\n\n## update after rebuttal\nThe authors' rebuttal has addressed my previous concerns. After considering the feedback from the other reviewers and the authors' response, I have decided to maintain my original evaluation to a Weak Accept.", "questions": "", "limitations": "", "rating": 3, "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 sincerely thank the reviewer for taking the time to evaluate our research. Below are our responses to all the points raised:\n\n**Paper Weaknesses:**\n> What does $Z_t^s$ represent in Figure 2? There is no clear definition of this symbol.\n\n**Answer:**\nWe sincerely thank the reviewer for the detailed observations. In Figure 2, $Z_t^s$ represents the reconstructed latent. Methods such as Prompt-to-Prompt, Plug-and-Play, and MasaCtrl reconstruct the original image using this latent representation. We have updated Figure 2 to explicitly define each latent variable used in the illustration. Thank you.\n\n> The authors state that \"smaller differences between the reconstructed and edited noise maps are strongly correlated with better editing performance.\" However, this is not always the case. As shown in Figure 3, the camel, elephant, and giraffe exhibit similar noise map differences, yet their editing performance varies. Have the authors attempted using the same editing prompt, such as \"camel,\" to perform different types of edits?\n\n**Answer:**\nWe acknowledge your concern regarding the relationship between noise map differences and editing performance. CLIPScore and LPIPS are based on deep learning models, which may introduce variations in perceived similarity even when noise map differences appear similar. We aimed to investigate the trend between noise map differences and editing performance. To do this, we conducted experiments on 20 different editing prompts, and as a result, the correlation coefficient between 'LPIPS / CLIPScore' and the 'difference between the reconstructed and edited noise maps' was found to be 0.8. Thank you for your careful review.\n\n> There are no definitions provided for $f$ in Equations 5 and 6. Please clarify the meaning of these symbols.\n\n**Answer:**\nThe function $f$ is defined in Section 3.1 (Preliminaries, DDIM Inversion) of the paper as $z_{t-1} ← f(z_t, t, C)$. It is a function that calculates $z_{t-1}$ from $z_t$.\n\nWe sincerely appreciate the reviewer for taking the time to review our research. We kindly ask you to consider our responses once more in your review. Thank you.", "claims_and_evidence": "Yes.", "methods_and_evaluation_criteria": "Yes.", "theoretical_claims": "No, as there are no theoretical claims made.", "experimental_designs_or_analyses": "I reviewed the experimental results presented in Tables 1, 2, 3 and 4, and they appear to be correct.", "relation_to_broader_scientific_literature": "This paper contributes to the broader scientific literature by addressing the challenge of high-fidelity image manipulation through the technique of encoding target images into noise for editable manipulation.", "essential_references_not_discussed": "None", "other_strengths_and_weaknesses": "**Paper Strengths:**\n\nThe paper is well written. The main motivation is clear and easy to understand.\n\n**Paper Weaknesses:**\n\n1. What does $Z_t^{s}$ represent in Figure 2? There is no clear definition of this symbol.\n2. The authors state that \"smaller differences between the reconstructed and edited noise maps are strongly correlated with better editing performance.\" However, this is not always the case. As shown in Figure 3, the camel, elephant, and giraffe exhibit similar noise map differences, yet their editing performance varies. Have the authors attempted using the same editing prompt, such as \"camel,\" to perform different types of edits?\n3. There are no definitions provided for $f$ in Equations 5 and 6. Please clarify the meaning of these symbols.", "other_comments_or_suggestions": "None", "questions_for_authors": "See weakness.", "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": "9oLIyvBGzj", "reviewer": "Reviewer_nzEq", "summary": "This paper proposed a new inversion-based imag/video editing method called ENM inversion. The motivation is to improve text alignment with the target text prompt. The authors proposed editable noise refinement, which conduct inference time optimzation on the intermediate latents. The proposed ressults achieved sota performance on both image and video editing datasets.\n\n\n## update after rebuttal\nAs mentioned the in rebuttal reponse, I increase the score to accept only if the authors add related details to the final version. The authors haven't confirm yet, which I take as acknowledgement.", "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": "We sincerely appreciate you taking the time to review our research. Below, we have provided responses to points raised.\n\n**Claims And Evidence:**\n> I'd like to see if it works for more editing tasks: (1) adding object, e.g., adding a hat. (2) multi object editing: e.g., you have a blue toy holding a yellow flower, you want to change to a yellow toy holding a blue flower.\n\n**Answer:**\nThank you for your valuable feedback. We have incorporated qualitative results demonstrating the ability of our method to handle object addition. However, our work does not specifically aim to address multi-object editing, and the dataset we used, PIE-Bench, does not contain multi-object editing tasks. We acknowledge the importance of this direction and consider it a promising avenue for future research.\n\n**Methods And Evaluation Criteria Question:**\n> In the construction of the loss (5)(6), the assumption is that the src prompt can give you a latents that reconstruct the image well. Can you prove if this assumption is correct? Will different src prompts lead to different results?\n\n**Answer:**\nWhile different source prompts can lead to slight variations in the results, the differences are not significant. As mentioned in NTI, inversion using a null source prompt already provides a latent that reconstructs the image well. When a source prompt is provided, even with the Classifier-Free Guidance scale set to 1, the reconstruction quality remains high. Additionally, NTI demonstrates that optimizing only the text embedding (rather than the latent) is sufficient for accurate image reconstruction. In our approach, we follow the same setup as other comparison methods by setting the scale to 1 during inversion.\n\n**Experimental Designs Or Analyses:**\n> I think the editability mainly comes from the editing module like PnP, while the proposed method makes the output more consistent to the source image as eq. (6). So I would expecting that you outperforms the other method mainly on the editing part but not editing part. But it seems from the results that other methods with the same editing module still performs worse (e.g., fig.4 last row only the proposed method is able to change the color). Can you explain?\n\n**Answer:**\nThank you for your insightful review. You raise a valid point regarding the role of the editing module in determining editability. However, editability is influenced not only by the editing module but also by the initial noise. As explained in [1], the choice of latent space plays a important role in generating specific concepts. Our method is designed not only to better preserve the source image but also to find a latent that facilitates the generation of the edited image. Furthermore, Figure 6 demonstrates that our inversion approach allows the attention map to be applied to the edited region more quickly and stably. This indicates that our latent generates images more quickly and consistently with the source image while also enabling stronger enforcement of edits compared to other inversion methods.\n\n> Does the method works on latest flow models like SD3.5/Flux?\n\n> The recent RF-inversion has similar ideas behind the scene, which construct a new vector field to update intermediate latent at each timestep, would be good to compare with this model or it's variants.\n\n**Answer:**\nYes, our method is applicable to Flux and other flow-based models. We have conducted additional experiments specifically on flow-based models [2][3] and integrated our approach with RF-inversion [3] to evaluate its effectiveness. \n\n| **Method** | **Structure Distance** ↓ | **PSNR** ↑ | **LPIPS** ↓ | **MSE** ↓ | **SSIM** ↑ | **CLIP Similarity (Whole)** ↑ | **CLIP Similarity (Edited)** ↑ |\n|--------------|----------------------------|------------|------------------|------------------|------------------|-------------------------|-------------------------|\n| SDEdit-Flux [2]  | 118.97                        | 14.41      | 329.92            | 450.06            | 60.82            | 25.06                   | 22.50                   |\n| RFInv [3]  | 60.08                        | 18.24      | 232.88            | 210.02            | 64.78            | 24.94                   | 22.65                   |\n| Ours + RFInv  | 46.38                        | 19.77      | 185.86            | 153.90            | 69.57            | 25.05                   | 22.65                   |\n\nTable 1. Results of comparing and combining our method with flow-based models.\n\nAs shown in the table, our method enhances the performance of RF-Inversion, demonstrating its capability to work effectively on models like Flux.\n\nWe appreciate your insightful comments and suggestions.\n\n**Reference**\n\n[1] Generating images of rare concepts using pre-trained diffusion models\n\n[2] SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations\n\n[3] Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations", "claims_and_evidence": "Most claims for valid and supported by evidence. But since the paper claims to be a general image manipulation methods, the authors should test on more types of manipulations. All the results presented in the paper seems to be minor editings like texture, color, style, expression. I'd like to see if it works for more editing tasks: (1) adding object, e.g., adding a hat. (2) multi object editing: e.g., you have a blue toy holding a yellow flower, you want to change to a yellow toy holding a blue flower.", "methods_and_evaluation_criteria": "The methods are somewhat novel, though there are other works explring updating the latent space, but most of them are in the reverse process, not inverse process. The eval matrics are good. Would be better to add human eval since the metrics are sometimes not very reflective in human preference as noted in many papers like dreambooth and RB-modulation.\n\nQuestion:\n1. In the construction of the loss (5)(6), the assumption is that the src prompt can give you a latents that reconstruct the image well. Can you prove if this assumption is correct? Will different src prompts lead to different results? e.g. \"a walking tiger\", \"a tiger is waling on the ground\", \"a tiger\", \"a tiger in the jungle\" might all refers to the same src image.", "theoretical_claims": "The formulations are correct.", "experimental_designs_or_analyses": "Question:\n1. I think the editability mainly comes from the editing module like PnP, while the proposed method makes the output more consistent to the source image as  eq. (6). So I would expecting that you outperforms the other method mainly on the editing part but not editing part. But it seems from the results that other methods with the same editing module still performs worse (e.g., fig.4 last row only the proposed method is able to change the color). Can you explain?\n2. Does the method works on latest flow models like SD3.5/Flux?\n3. The recent RF-inversion has similar ideas behind the scene, which construct a new vector field to update intermediate latent at each timestep, would be good to compare with this model or it's variants.", "relation_to_broader_scientific_literature": "Mostly comprehensive, added one more suggestions in the experiment section above.", "essential_references_not_discussed": "There are many papers on inversion-based editing on the rectified-flow models, would be good to have them for completeness.\nBut not very essential since this paper only compares diffusion models.", "other_strengths_and_weaknesses": "Discussed above.", "other_comments_or_suggestions": "no.", "questions_for_authors": "included above.", "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 work has achieved unanimous positive reviews among reviewers in making sufficient contributions in diffusion model based image editing. The initial concerns are mostly around the evaluation and clarity (relatively minor), and the authors have addressed them during rebuttal.

Therefore I recommend acceptance but strongly encourage the authors to integrate the rebuttal into their revision.
Decision: Accept (poster)