DNAEdit: Direct Noise Alignment for Text-Guided Rectified Flow Editing
Authors: Chenxi Xie
Minghan Li
Shuai Li
Yuhui Wu
Qiaosi Yi
Lei Zhang
Venue: NeurIPS 2025 spotlight ()
Keywords: editing
diffusion
rectified flow
Primary area: deep_learning

Abstract: Leveraging the powerful generation capability of large-scale pretrained text-to-image models, training-free methods have demonstrated impressive image editing results. Conventional diffusion-based methods, as well as recent rectified flow (RF)-based methods, typically reverse synthesis trajectories by gradually adding noise to clean images, during which the noisy latent at the current timestep is used to approximate that at the next timesteps, introducing accumulated drift and degrading reconstruction accuracy. Considering the fact that in RF the noisy latent is estimated through direct interpolation between Gaussian noises and clean images at each timestep, we propose Direct Noise Alignment (DNA), which directly refines the desired Gaussian noise in the noise domain, significantly reducing the error accumulation in previous methods. Specifically, DNA estimates the velocity field of the interpolated noised latent at each timestep and adjusts the Gaussian noise by computing the difference between the predicted and expected velocity field. We validate the effectiveness of DNA and reveal its relationship with existing RF-based inversion methods. Additionally, we introduce a Mobile Velocity Guidance (MVG) to control the target prompt-guided generation process, balancing image background preservation and target object editability. DNA and MVG collectively constitute our proposed method, namely DNAEdit. Finally, we introduce DNA-Bench, a long-prompt benchmark, to evaluate the performance of advanced image editing models. Experimental results demonstrate that our DNAEdit achieves superior performance to state-of-the-art text-guided editing methods. Our code, model, and benchmark will be made publicly available.

DNAtdit: Direct Noise Alignment for Text-Guided Rectified Flow Editing
Chenxi 1.2,. Minghan Li Shuai Li', Yuhui Wul Qiaosi Yi Lei Zhang 12,
'The Hong Kong Polytechnic University, 2OPPO Research Institute, 3Havvrdd University
chenxi.xie@comee comeeeeooot.....ly........
selzhang@comp.poot
"qqual contribution "Corresponding author
Project Page: https://zieccenxiff99 github. 1o/DNAEdit/
Abstract
Leveraging the powerful generation capability of large-scale pretrained text-to- image models, training-fiee free methods have demonstrated impressive image editing results. Conventional diffusion-basse (RF)-based methods, typically reverse synthesis trajectories by gradually adding noise to clean images, during which the noisy latent the current timestep used to approximate that at the next timesteps, introducing accumulated drift and degrading reconstruction accuracy. Considering the fact that RF the noisy latent is estimated through direct interpolation between Gaussian noises and clean images at each timestep, we propose Direct Noise Alignment (DNA), which directly refines the desired Gaussian noise in the noise domain, significantly reducing the error accumulation in previous methods. Specifically, DÑA estimates the velocity field of the interpolated noised latent at each timestep and adjusts the Gaussian noise by computing the difference between the predicted and expected velocity field. We validate the effectiveness of DNA and reveal its relationship with existing RF- based inversion methods. Additionally, we introduce a Mobile Velocity Guidance (MVG) to control the target prompt-guided generation process, balancing image background preservation and target object editability. DNA and MVG collectively constitute our proposed method, namely DNAEdit. Finally, we introduce DNA- Bench, long-prompt benchmark, to evaluate the performance of advanced image editing models. Experimental results demonstrate that our DNAEdit achieves superior performance to state-of of of-tthe-rt text-gru -utdgguddd editing methods. Our code, model, and benchmark will be made publicly available.
methods, as well as recent rectified flow
Introduction
Recent advances in text-to-image (T2I) generation have been driven by Rectified Flow (RF)-based models [T], which significantly reduce sampling timesteps, enabling faster generation. Leverag- ing large-scale T21 models such as SD3 [ and FLUU g. training-fres text-guided image editing achieve high-quality editing results with fewer sampling steps. Existing RF-based editing methods [7
methods [17 22 30
25 27 30 typically follow earlier Diffusion Model (DM)- 14, first reversing the generative trajectory by gradually adding noise to the clean image, obtaining an inverted noise and then re-denosing it under new conditions to generate the edited image, shown by the black path in Fig. (a). The inverted noise is critical for the preservation of fidelity, as it retains the structural information of the reference image. ensuring consistency between the edited and the reference images. However, this inversion process introduces
7
22
based editing approaches 15
This work is supported by the PolyU-OPPO Joint Innovative Research Center.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Reoieractio Giveation
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Montig Crasdaa Nowa
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Aeranusiatve Drilk
Predicted i
0800
0230
Image
Grussian Noise
Image
Gaussian Noise distribution
0030
distribution
space
(a) Re-noise based RF Invension
(b) Direct Noise Alignment
(c) Reconstruction MSE vs. Timestops
Step 0.3
Step 3-6
Step 6-9
Step 9-12
Step 12-15
Step 15:15
Step 18:21
Step 21:24
Visualization of Moving Noise Difference in DNA Process
Figure I: Illustration of (a) existing re-noise based RF inversion methods and (b) our DNA. The ideal Gaussian noise refers to the noise that exactly reproduce the reference image. (c) The curves of reconstruction MSE vs. timesteps for DNA and re-noise based RF inversion. (d) Visualization of the difference in Gaussian noise between steps.
accumulated drifts that significantly degrade reconstruction accuracy and editing fidelity. These drifts arise because the noise latent at the current timestep is unavailable and it has to be approximated using the noise latents from the previous timesteps. The approximated latent is then used to estimate the velocity field for inversion, resulting in shifted velocity fields. As the process continues, the drifts accumulate, leading to significant distortions in the final noise.
22 25 aim to reduce the drift to improve editing fidelity. For example, RF-inversion 17 employs conditional velocity fields: one for inversion (conditioned on Gaussian noise) and one for editing (conditioned on the source image). However, its global application of guidance ignores region specificity, often degrading edit quality. Subsequent methods [2] 22 [3].
Existing RE-based methods 17 13
such as RF-solver [22]. refine the discretization process using higher-order ordinary differential equation (ODE) solvers, often combined with attention injection during editing enhance fidelity. More recently, FTEdit 2 reduces errors by performing iterative average at each inversion timestep. increasing the number of sampling steps. While these approaches reduce errors through finer discretization of the reverse ODE process, they incur additional Neural Function Evaluations (NFEs), decreasing the efficiency. Despite offering slight improvements over RF inversion, these methods remain inefficient and struggle to mitigate the accumulation and amplification of drift.
Actually, existing editing methods overlook the unique properties of RF. Unlike DM. RF models the generation process as straight trajectory between noise and image, allowing noise latent to be derived via linear interpolation at each timestep. Keeping this in mind, let us revisit the editing process. Note that inversion aims to estimate a noise sample that corresponds to the reference image, enabling an accurate reconstruction of during sampling. In RF, a better noise sample means more direct path between the noise and the reference image. Based on this, an intuitive question is: can we directly refine the desired noise sample the Gaussian noise domain, rather than gradually transforming the image into noise? To answer this question, we propose novel method in this work, called Direct Noise Alignment Editing (DNAEdit). Unlike gradually transforming an image back to Gaussian noise, DNAEdit directly refines the randomly initialized noise in the Gaussian noise domain, gradually aligning it with a better target noise sample, as shown in Fig. (b).
To achieve Direct Noise Alignment (DNA), we start from Gaussian noise sample and iteratively interpolate between it and the clean reference image each timestep. As shown Fig. (b). the predicted velocity field often deviates from the expected one derived from linear interpolation. DNA, thus, corrects by feeding the deviation back into the noise, refining the trajectory. We visualize the changes during the DNA process in Fig. (d). These changes contain the structure of the original image, indicating that the original content gradually infused into the initial random noise, resulting structured noise. This process produces straighter paths and reduces the accumulated error by avoiding dependence on previous random noise and latents. Finally, the noise converges to sample that is well aligned with the reference image, as verified by the reconstruction MSE in

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Fig. (c). We also provide a theoretical analysis showing that DNA shares the same principle with RF-based methods in the Appendix.
To better balance editability and fidelity, we introduce a Mobile Velocity Guidance (MVG) to guide denoising by computing the difference between source- and target-conditioned velocity fields in the image domain, which yields smooth transition from source to target images. In addition, existing benchmarks 66. 10 28 [6] focus on short prompts, limiting semantic richness. To address this, benchmark to evaluate RF editing under detailed textual guidance. Experiments on PIE-Bench and MeNechncc show that our DNAEdit method strikes a better balance between fidelity and editability, demonstrating the best performance.
we introduce
a
2 Related Work
Inversion for Image Editing. Inversion seeks to transform an image into a corresponding Gaussian noise. A successful inversion generates an initial noise vector that accurately recreate the reference image, which is essential for further editing. According to DDIM [8], the initial noise can be gradually obtained by iteratively adding predicted noise, but the approximation error can accumulate over timesteps. Various strategies have been developed to address this issue. Null-text inversion 15] optimizes null text embeddings each inversion step, but it is inefficient. Negative- prompt-inversion improves efficiency by replacing null texts with negative prompts. Unlike DMs, RF has notably straighter generation path. However, current RF inversion methods still adhere to DM principles, reversing the ODE to gradually add noise. RF inversion [17 aims to tackle this issue by optimizing the reverse ODE process using dynamic optimal control through a linear quadratic regulator to balance editability and fidelity. RF-solver [22] and FireFlow 12] use higher-order
solvers to better approximate the reverse ODE and reduce errors at each step. FEEdit ? employs a fixed-point iteration strategy, refining the added noise and averaging it to suppress approximation errors. Although these approaches can reduce reconstruction error some extent, they all follow the iterative re-noising paradigm, making error accumulation inevitable. In contrast, our proposed DNA framework directly aligns the noisy latent with the ideal noise derived from model priors within the noise space, significantly reducing the error accumulation.
Rectified Flow-based Editing. Compared to DM-based editing methods [1 21
RF-based editing methods are not fully explored. Unlike most DM models that use the U-Net architecture, RF models 8 3 primarily use the MM-DiT architecture, which makes strategies relying on U-Net's cross-attentinn map unsuitable for RF-based editing methods. Some approaches [2] 22) adapt the
attention injection scheme to maintain fidelity during editing. Other methods (25] focus on the exchange of text-image information in MM-DiT, manipulating features through AdaLN to control the editing process. While these methods have achieved certain success, they often require additional adaptations for different RF models, limiting their applicability. Some model-agnostt methods have uses an inversion- -reee approach by calculating the difference
also been developed. FlowEdit between the source and the target velocity fields, enabling direct image editing in the image space. However, this method relies on editing the velocity difference of the reference image and restricts the range of editable space, making it difficult to perform global editing tasks (e.g., changing style). Our DNA employs two model-agnostic processes: DNA and MVG. DNA minimizes cumulative error and obtain better initial Gaussian noise aligned with the source text. MVG guides the editing process to preserve background fidelity while minimizing compromise on editability.
Direct Noise Alignment Editing (DNAEdit)
3.1 Approximation Errors in RF Inversion
Preliminary. Rectified Flow (RF) 12 models the transition between two observed distributions To and πı using ordinary differential model (ODE):
dZ. = Uy (Zt)dr, Zo ~ To. Z 1~aT π1, t E [0, 1].
(I)
where (-) is the learnable velocity field parameterized by 0. To encourage near-lineat trajectory of the transition, RF employs the following objective to train uo:
min 8
( - Zo) - vo (Z)))))fj 2, = 17 + (1 - Z)
(2)
JU
3

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Gaussian Noise distribution
%
Arp
Recihuu difuer
Basiaa Ofher Anfer 2:
4
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Moriing Glansian Notta
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Ri Ranioon Cransiin Nota
111g O
Cia Noia
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(a) Direet Noise Alignment
(b) Mobile Velocity Guidance
Figure 2: Illustration of (a) DNA and (b) MVG, which collectively build our DNAEdit algorithm.
where Z, denotes the linear interpolation between Zo To and Z1 Tl
Approximation Errors in Inversion. The denoising process maps the standard Gaussian distribution (To = (0, 1)) the image distribution T1, while its reverse maps Th back to To. To solve the ODE in Eq. ( the time interval [o. is discretized into T steps, denoted as (oo....,yy. The Euler solver is used to approximate the solution, with the forward and reverse steps given by:
Fwd: Z--1 Zi + vo (Z))( - ot), Inv Zi Zc+1 vs vo (Z:)( ot),
(3)
where the timestep index increases from 0 to T in the forward process and decreases from T to 0 in reverse process. is evident that in the reverse process, at each step only the noisy latent Z++1 is available, while Z. the desired output, is unknown. Consequently, the velocity field vo (Zi) in Eq. cannot be directly evaluated, making the exact inversion intractable.
Considering that the differences between the noisy latents of adjacent timesteps are relatively small, existing RF inversion methods [2|22 17 (25 approximate the velocity field timestep by evaluating it at timestep t + 1, leading to the following inversion formula:
Inv approx (Z.) R vo vo (Z++)), Z4 ~ Z
- -at)-
However, due to the sequential nature of the inversion process, approximation errors at each step will accumulate over time, resulting in final latent that can deviate significantly from the expected Gaussian noise (see Fig. (a)). Furthermore, the noise sample obtained through this approximated inversion is not guaranteed to follow the standard Gaussian distribution, mismatching with the model assumption. As a result, the reconstructed or edited image (see Fig. (c)) may be distorted or invalid.
3.2 Direct Noise Alignment (DNA)
Our DNAEdit method consists of two key components: DNA and MVG (see Section 3.3 DNA is used to obtain a structured noise sample, enhancing the fidelity of the editing process. The resulting noise is then re-denoised under the guidance of MVG to generate the final image. From the analysis in Section 3.1 we see that the primary source of error comes from the use of the noisy latent Ze+1 to replace Z. to compute the velocity field: ne (Z+) vo (7++)) Since the noise levels of Z, and Ze+1 are different, this approximation introduces inconsistencies with the model prior.
Using RF, fortunately, the latent variables Z, can be constructed via linear interpolation between the clean image and Gaussian noise. One can sample a noise ~ (O 1) and interpolate it with the image. However, since S is randomly sampled, the resulting path may lead to reconstruction errors under text guidance. According to Eq. (2). there exists an optimal noise such that the RF. driven trajectory forms nearly linear path to the reference image. Motivated by this, propose directly shifting the random noise toward the target noise the Gaussian space over timesteps: So.
ST NN0, 1) ST
To achieve this goal, we need to devise an effective algorithm to optimize random Gaussian noise Sr. This can be done by aligning the velocity of predicted by the RF model with the expected

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Algorithm/ 1 Direct Noise Alignment (DNA)
Input: Number of optimization steps T. Source image Zr. Source text "/" Timesteps (o, Hizr, RF model us with parameters 0, Randomly sampled start noise Sr (O, 1).
Output: Noisy latents { (Ze) (Zc)-rr1 and residual offsct [Am, DNA 10
T- Terminal noise sample So
for f = =T -1,,.... 1,0 do
.L)) Zi f Ze+1 2 Si+1 x x m4ts'
Interpolate get noisy latent Zi
o1t1
0/2
Jinear_ = (S - -041)/0041
b Calculate the expected velocity on the lincar path Calculate the predicted velocity by SD3 or FLUX
2/ re 0/ )
Avt
DNA = 1/6
linoar oi
S. + S141 + AnDNA x 0c+1
> Move Gaussian noise from S to St P Move interpolated latent from Z; to Ze b Compute the residual offset
Zi +- Zi + Aot x (01+1 oc)
Aut DNA = Zi= Ze
end for
return Sp. 1Z: 1
and ( 10 JC-T-1
velocity ef allinar along the linear path from Gaussian noise to image latent. Specifically, at timestep t, we construct the linear path from noise St+1 to latent Ze+1 and obtain latent Z; by interpolation:
Z
σe Ze+1 + (1 (1- Oz+1
x St-1-
(5)
0++1
The estimated noisy latent Z is obtained via direct interpolation, eliminating the need to approximate it using Eq. 4 We then compute the velocity field Zi using the RF model as v vi (Zi, vaat))
and define the expected velocity along the linear path yllinaar = (S++1 1 / However, as shown in Fig. aaa), a discrepancy arises between Jinear and ot due to the offset between the actual noise S and the noise S, derived by the predicted velocity. We denote this mismatch as the velocity gap AupNA, and correct it by shifting the noise from Sp+1 to Sp:
Au PNA linear o S. SS41 + AN x 0++1-
(6)
As the noise moves to St, the interpolated noisy latent Zł in Eq. (5) can be refined to a better estimate 7, _ x 74+1 + ) x St- It can be easily derived that the updated linear velocity
d++1 "t -linear = (S;- Ze+ / / matches the predicted velocity uo / sere). The difference between the updated latent Z, and the initial estimate Zi can be expressed as:
7 7
Ot-1 Dt x - -S:-5+41).
O++1
By substituting Eq. 6 into Eq. 7
derive that Z, Z3 + ANA x (6141 ot). Eq.
implies that although the random Gaussian noise Sr may initially deviate from the ideal Gaussian noise, the difference will become much smaller for interpolated latent Z, due to the small scaling coefficient (a++1 - ( where o++1 : + O. Therefore, it can be deduced that 0o (Z, (bare) Rd oo(2; )ere). Starting from 74, the predicted velocity field can lead to Z4+1 with small error. Eq. 7 suggests aligning from large to small timesteps to avoid early errors, so we iteratively adjust noise from or to og- We also show that the residual offset Ar.DNA- = Z; Ze allows exact reconstruction. Adding it back to Zz, we recover Zi and compute vo(2i (Zi, ( for precise updates from Z, to Ze+ I-
The algorithm is presented in Algorithm I ba-eaded methods is provided in the Appendix.
and theoretical analysis between DNA and existing
3.3 Mobile Velocity Guidance (MVG)
Although DNA can estimate noise sample with small reconstruction error, directly using target texts to guide image generation may destroy the original structure of the reference image. Inspired by [17], we can control the fidelity of the edited image by integrating the velocity field pointed to the reference image. However, the velocity field is introduced improperly, it can interfere with the denoising process, resulting in an undesired overlay of the reference and target images and degrading editability. To address this, we introduce Mobile Velocity Guidance (MVG), as illustrated in Fig.2bb which adaptively guides the editing process balance fidelity and editability.
The denoising process starts from noise sample and gradually moves to the target image, forming trajectory from Gaussian noise to the target, called Zedit, Specifically, at timestep t, we first
5

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Algorithm 2 Mobile Velocity Guidance (MVG)
Residual gaps Input: Source image Zr. Timesteps for (oz) RF vo()), Noisy latents {Ze (AmpXA) BAmPSADE=-11 Start Step fs. Target text abt0t
Output: Edited Image Zzdit
Inít: Z = Te,
for f = t,, to +1,
, do
rie ra (Zi Zg 1/151),
t 77 + A.
Involve residual gap to get target velocity Reuse source velocity from DNA latents Move the mobile latent toward the target image Calculate the mobile velocity guidance Calculate the synthesized denosing velocity b Perform denoising step
ri r u0 Li 1/ 15
Z0+1-21
Avi 10
vi tre
o2+1
7, Z
Av
(0++1 ae)
"t mvg (Z
Z ) oτ)
vi edit 11 st (1 1) vi m
Zedit + Zedit + 1i
adit
x (0++1 o.)
end for return Z
incorporate AxDNA to shift Z to Zredit, enabling the exact computation of target velocity v!"",
Z Z + Art
ANDNA, DNA
it vo (Zeeedit A0)
(8)
This step is erucial. According Eq. 7 the residual Aa.NNA Zi 7. is equivalent to the weighted difference between two Gaussian noise samples and therefore free from image content. Essentially, this operation ensures that the regions intended to be preserved in Z,edit are perfectly aligned with the noisy latent in the reverse process, i.e., Z in Eq. (5). As a result, the computed target and source velocities (u² and vf"9) become near-identical in those preserved regions, which not only improves reconstruction accuracy but also significantly enhances editing fidelity. Our ablation study in Appendix further supports this finding.
In addition, there exists another trajectory that transitions from the source image to the target image purely within the image space, denoted Z,mvg Before using o/4t to update the noisy latent Z conditioned on the target text, we first apply the velocity difference between the source and target to shift Z from the reference image toward the target image. This velocity difference and the corresponding mobile latent modification are defined as:
Au
lgt Ur
srC Ut
Z
ZuIv + Aur x (0t+1 - -t).
(9)
Here, the saved latents in DNA to calculate the velocity ufre, which not only reduces the number of function evaluations (NFEs) but also accurately captures the velocity field associated with the reconstruction of the reference image. The velocity difference Av, is then applied the mobile reference image Zineg modifying specific regions to reflect the differences induced at timestep t under the source and target text conditions. Using the updated latent Zmng obtain the final
denoising velocity by blending the velocity fully conditioned on the target text ut with the MVG ve nuvg using weighting coefficient !) as
mvg 0r
(Zeilit
/ oz), of 17 x ut tgt + (1 17)
odit
Ue
mvg
(10)
where 7) [0, 1] controls the trade-off between fidelity and editability.
Finally, we apply the synthesized velocity to perform the denoising by Zodit = Zudit + + ofdit (a:+1 oz). This process applied across all timesteps, as shown in Fig. (b. To avoid excessive changes during editing, we skip the initial step and select Zt, as the starting point for editing. The complete algorithm of MVG summarized in Algorithm [ Using a fixed reference image for guidance, as in [77, we maintain the denoising direction close to the reconstruction of the source image during the early editing stages, thereby enhancing the fidelity. However, in later stages, as the image content undergoes substantial changes, relying on the original reference image can severely hinder editability. In contrast, our proposed MVG mvg mitigates this issue by employing moving reference for guidance, effectively balancing fidelity and editability throughout the editing process.

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Table 2: Quantitative comparison on -IE-Bench. Red
blue
yellow
represents top performers.
Structure
Background
Preservation
CLIP Similarity
Rank
Method
Model
Distance
PSNR
LPPPS
MSR
SSIM
Whole
Edited
Arg. +
PnP[21]
SD1.5
27.35
22.31
112.76
82.95
79.25
25.41
22.52
9.17
MasaCtrl
SD1.4
27.12
22.19
105.44
86.37
79.91
24.03
21.15
10.42
DI+PnP[6]
SD1.5
23.35
22.46
105.51
79.94
79.88
25.49
22.64
6.83
DI+MasaClulf6
SDL.4
23.58
22.68
87.41
80.63
81.51
24.39
21.41
8.75
InfEdit 26
LCM
19.31
27.31
56.32
47.80
85,30
24.90
22.14
5.67
InsP2P
InsP2P
58.13
20.80
159.23
221.3
76.47
23.61
21.68
13.42
RF-Inv 17
FLUX
42.29
20.20
179.54
139.2
69.91
24.57
22.20
12.42
RFEdit 22
FLUX
21.79
24.83
113.15
52.46
83.38
25.57
22.26
6.42
FirePlow
FLUX
29.03
23.33
133.40
70.83
81.22
26.19
22.99
7.42
FlowEdit
FLUX
27.82
21.96
112.19
94.99
83.08
25.25
22.58
9.50
FlowEdit 7
SD3
27.12
22.22
104.12
85.96
93.22
26.53
23.57
5.58
FTFdit 25
SD3.5
18.17
26.62
80.55
40.24
91.50
25.74
22.27
3.50
DNAEdit (Ours)
FLUX
18.87
24.99
95.06
50.45
85.71
25.79
22.87
3,42
DNAEdit (Ours)
SD3.5
14.19
26.66
74.57
32.76
88.63
25.63
22.71
2.50
4
Experiments
4.1 Experimental Settings
DNA-Bench. Text-guided editing has been extensively studied, and evaluation benchmarks 6 10 16 have been proposed to evaluate and compare the different editing methods. However, most of these benchmarks developed in conjunction with diffusion-basee models. Due the limitations of text encoders and pre-trained models, diffusion models struggle to accurately understand long text inputs. As result, existing benchmarks typically feature short descriptions. For example, PIE-Bench 6 has an average prompt length of 9.46 words. In contrast, RF-based models have shown significant improvements understanding long text imput, but the short descriptions in existing benchmarks cannot fully evaluate the editing capabilities of RF-based models.
To bridge this gap, propose extended version of PlE--Bnchh, called DNA- -NA-bench, which is tailored for long-text prompts. To construct DNA-Bench, we design target-aware prompts and leverage the powerful multimodal large language model GPT-4o [20] to generate detailed descriptions of the source images as source prompts. In addition, we modify and extend the original target prompts align with the editing tasks. The average prompt length DNA-bench 33.17 words. More details of the construction process and example prompts of DNA-Bench can be found in the Appendix.
Implementation and Compared Methods. Two versions of DNAEdit provided, which based on FLUX dev [9] and SD3.5-medium [19]. respectively. In both versions, the MVG coefficient 7 is fixed at 0.8. Detailed byper-parameter settings be found in the Appendix. We compare DNAEdit with representative DM-hased methods 21 26 and latest RF-based editing methods 2 7 25) using their official implementations and default settings in shared environment, except FTEdit, where we use provided results.
17
22
Evaluation and Metrics. We first conduct reconstruction and text-guided editing experiments on PIE-bench (6). The PIE-bench consists of 700 natural and artificial images evaluate editing methods across 9 distinct dimensions. It provides the source and target prompts for each image, along with the editing area masks to assess background preservation and local editing ability. We then conduct experiments
proposed DNA-bench. To evaluate the reconstruction and preservation performance non-edited areas, we adopt the commonly used image quality metrics, including
Table Quantitative results on reconstruction.
Method
NFE,MSE, LISCCLPIPSSSSSIIS
Vallina Inversion
60
0.028
0.342
0.601
Valtas
0/AA
RF- Inverseerin 17
56
0.023
0.279
0.526
RFFdit
2
60
0.022
0.244
0.677
FireFlow
2
57
0.015
0.200
0.726
RPESI
APtearster
RveFlow
DNA
0.010
0.110
0.830
Figure 3: Qualitative comparison on reconstruction.
7

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Image
charge object
Add Flower
Change Material
Origani
Change Pose
Change Style
change Color
Delete
Figure Qualitative comparison of different text-guided image editing methods.
LPIPS 29 SSIM 23
MSE, PSNR and structure distance 6 Meanwhile, CLIP similarity 24 is
employed to assess the consistency between target prompts and edited results.
4.2 Main Results
Results on Reconstruction. In Fig. we present qualitative comparison among different RF-based reconstruction methods. We that our DNA method surpasses other approaches in reconstruction accuracy, with only minor differences from the original image the area highlighted by red-box. FireFlow and RFEdit both use higher-order ODEs reduce inversion drift, achieving good overall accuracy. However, noticeable differences appear in areas such as the background and arms. RF- Inversion uses the original image as a reference during reconstruction. leading over-generation. such as the house in the background, which is not faithful to the original image. Moreover, using the original image as a reference can compromise the editability during editing. The quantitative results are reported in Section Compared to existing inversion- based methods achieves the lowest reconstruction error under similar NFEs.
17 22
our DNA
Quantitative Comparison on Text-guided Editing. In Table we evaluate editing methods in three dimensions: structure preservation, background preservation, and clip similarity. We present the overall ranking for each method by averaging the rankings in these dimensions in the last column of Table 2 Our approach demonstrates superior overall performance, particularly terms of structure and background preservation. Specifically, while InfEdit shows better PSNR and LPIPS metrics, its CLIP similarity significantly lower than other methods. This suggests that its strong background preservation hinders its editing capabilities. FlowEdit-ShD
achieves higher CLIP score but shows weaker background and structure preservation compared our method (PSNR 22.22dB vs. 26.66dB), indicating potential over-editing. This is attributed to its use of a large CFG in the generation, which also leads to poorer visual quality, as illustrated in Fig. 4 Lastly, the recently developed FTEdit shows relatively balanced performance. However, our method excels in background and structure preservation, with similar whole CLIP scores (25.63 vs. 25.74). In edited regions, our DNAFdit achieves notably higher CLIP score, 22.71 vs. 22.27, indicating that our approach better preserves

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Table 3: Quantitative Results on DNA DNA-Bench. Red
blue
yellow represents top 3 performers.
Structure
Background Preservation
CLIP Similarity
Rank
Method
Model
Distance 4
PSNR +
LPIPS 4
MSE 4
SSM
Whole
Edited f Avg.
RFEdit
FLUX
20.18
25.13
104.92
49.46
84.01
28.34
22.99
2.92
Inversosn
FLUX
41.92
20.18
176.40
139.69
69.96
27.98
22.89
6.83
Edit
SD3
30.93
21.35
118.48
103.52
81.45
28.89
23.58
4.58
FlowEdit
FLUX
29.06
21.57
116.97
102.50
82.54
28.15
22.86
5.33
FireFLow
FLUX
26.97
23.57
124.78
68.51
81.82
28.69
23.32
3.83
DNAEdit
FLUX
18.61
24.89
93.76
50.80
85,80
28.36
22.99
2.41
DNAEdit
SD3.5
25.67
23.24
112.60
67.32
83.69
28.90
23.66
2.08
non-edited areas while accurately editing targeted regions. Furthermore, our method is model-agnostic and does not require alterations to the MM-DiT architecture, enhancing its applicability.
Qualitative Comparison on Text-guided Editing. We present visual comparisons state-of-the-an editing methods in Fig. including object, attribute, color, material, style, and pose editing. It can be found that InfEdit often results in edits without visible changes (see the 2nd, 4th and 7th rows). In contrast, FireFlow frequently exhibits over-editing, shown in the 2nd, 4th and 6th rows. Although the editing instructions are followed, the original image's structure is significantly changed. As for FlowEdit, while the SD3 version of it successfully accomplishes most editing tasks without altering much the original image's s structure, it suffers from low visual quality and noticeable over-saturation, evident in row 1 and row 4. The FLUX version of FlowEdit offers better visual quality, but it has some shortcomings in fidelity and instruction following for some editing tasks. For instance, in row 2, fails add a flower, and there is noticeable change in the dog's ears. Compared these methods, our proposed DNAEdit method is versatile across various editing tasks and shows superior visual quality. For example, in row 5, our approach successfully applies global pixel style while preserving the original image's ' character pose and overall structure. Similarly, in row 6, our method effectively changes the makeup color while retaining facial ID and other details, achieving desired results for both edited and non-edited areas, In summary, DNAEdit effectively balances between editability and fidelity, which is consistent with our quantitative results.
Results on DNA-Beneh Table 3 shows the results of RF-based editing methods on DNA- Bench. Our DNAEdit remains the best, achiev- ing high clip scores while preserving the back- ground. This validates that DNAEdit can be used for image editing with short- and long- text inputs without any change. Comparing the results of the same method under long and short prompts, we can see that inversion- methods achieve better background preservation with long prompts (e.g., FireFLow achieves PSNR of 23.33dB on PIE-Bench and PSNR of 23.57dB on DNA-Bench). This is because long texts provide detailed descriptions that align with image contents, allowing more accurate reconstruction. As shown in Fig. 5] short prompts change a cat into a dog but alter the pose, while long prompts keep the original pose, producing consistent results. In addition, long prompts also result in improved clip similarity scored.
Source Image
Shert Prompt
Long Prompt
and flelly striped cut wrting in the greandi mes period and akhikms digtoyel, giving
groand.
expresiion.
Figure 5: Visual comparison between editing re- sults using short and long prompt.
4.3 Ablation on Proposed Modules
Baseline. In Table 4 we present the experimental results of our proposed method with different modules. We begin with Exp. ①. which utilizes velocity fields computed on interpolated latents to reverse ODE process. In Exp. D. we initiate the inversion process by randomly sampling Gaussian noise, which then used throughout the inversion process. At each step, the Gaussian noise is interpolated with the latent at timestep t to compute the velocity field for timestep t 1, facilitating the inversion. During the re-denoise phase, we use the velocity field conditioned on target prompt to generate target image. By employing interpolation to construct the latent of timestep / - 1, we can mitigate errors caused by approximation. As observed, this setting already achieves relatively good results. However, despite avoiding approximation errors at each step, the use of fixed Gaussian

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Table 4: Ablation study on proposed modules.
Structure
Background Preservation
CLIP Similarity
Exp.
Component
Distance 4
LPIPS
MSE 4
SSIM
Whole 1
Edited f
Interpolation
31.90
22.61
147.77
84.60
78.93
25.73
22.47
+DNA
32.67
22.18
146.27
89.92
79.73
26.24
23.08
+ResOffiet
24,93
23.75
120.56
64.81
82.24
26.02
22.79
+DNA +ResOffset
33.98
21.97
149.84
95.19
79.32
26.40
23.23
+DNA+MVG
18.81
24.91
95.44
50.29
85.55
25.78
22.48
+DNA +RSGOGfS
18.87
24.99
95.06
50.45
85.71
25.79
22.87
noise throughout the process can introduce significant errors when approaching pure noise, especially when the initially sampled Gaussian noise deviates much from the desired structure noise.
Effectiveness of DNA. In Exp. 2 we introduce DNA by adjusting the initially sampled Gaussian noise during the inversion process. By comparing ! and @ we can observe that as the structural and background preservation metries remain nearly unchanged, the Clip Whole and Clip Edited similarity significantly increase, improving from 25.73 and 22.47 to 26.24 and 23.08, respectively. This improvement is attributed the DNA process, where the noise is continuously moved by the difference between the linear velocity and the velocity conditioned on the source prompt. This movement injects the original image structure and gradually aligns the random noise with the structured noise corresponding to source prompt, enabling the generated results to better match the target prompt during editing.
Effectiveness of ResOffset. Comparing Exp. 1 and 3 we see that the introduction of ResOffset during the re-denoising process significantly improves the metrics for image structure and background preservation. For instance, the Structure Distance improves from 31.90 to 24.93, and the PSNR increases from 22.61 dB 23.75 dB. By incorporating the latent calculated with ResOffset, we shift Zzdhit to Z,eddt enhancing the consistency between the intended preserved regions in the noisy latent Zr during the DNA process and those Z during re-denoising. This ensures that the velocity field during denoising accordingly tends to preserve these regions.
Effectiveness of MVG. Comparing Exp. + and O. we observe that the introduction of MVG results in decrease in structure similarity from 33.98 to 18.87, while PSNR increases from 21.97 dB to 24.99 dB, indicating a significant improvement in editing fidelity. As trade-off for faithfulness to the original image, there is a decrease in CLIP similarity. Since MVG ensures that the overall structure of the edited image undergoes minimal changes, it imposes greater limitations on whole image editing, leading to decrease in the whole image metric from 26.40 to 25.79 (-0.61). However, because MVG distinguishes between edited and non-edited regions and uses the evolved image to guide the editing of specific areas, the restriction edited regions is greatly reduced, resulting in only a slight decrease of 0.36 in Edited CLIP similarity. This demonstrates that under the guidance of MVG, fidelity can be significantly enhanced while avoiding the constraints edited regions that could lead to unchanged results. Please refer to Appendix for detailed ablation studies on the setting of the MVG coefficient.
5 Conclusion
We presented a novel RF-based method, namely DNAEdit, for text-guided image editing. By utilizing RF's property of linear trajectory, we proposed method to estimate accurate latents by calculating the velocity fields at specific timesteps through random sampling and linear interpolation. By analyzing the expected and predicted velocity fields, we presented DNA to align the image the ideal noise directly in the Gaussian noise domain. We then introduced MVG to maintain background areas while guiding effective changes in editing regions. Theoretical analyses were provided explain the effectiveness of DNA and MVG and their connections with existing RF-based editing methods. Experiments were conducted on the commonly used PIE-Bence and our newly improved long-text DNA-Bench. Both qualitative and quantitative results showed that our DNAEdit approach performed well on various editing tasks, producing high-quality edits faithful to the original image.
Limitations. As a training-free editing method, DNAEdit utilizes the strong priors of pre-trained T2I models by converting images into structured noise aligned with the given text. Therefore, it may fall short in achieving desired editing results for cases that lie outside the foundation T2I model's prior.
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Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.
Question:
Answer: [NA]
Justification: Our core method focuses on image editing and does not involve LLMs as part.
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The answer NA means that the method development in this research does not involve LLMs as any important, original, or non-standard components.
Please refer to our LLM policy battps://neurre http://cccccceeeeeeee/////////// for what should or should be described.
19


Reviews:
[{"review_id": "VnEHUMwfFk", "reviewer": "Reviewer_Z4Cp", "summary": "The paper introduces a novel approach to flow-based image editing, termed Direct Noise Alignment Editing (DNAEdit). Unlike traditional inversion-based editing techniques, DNAEdit operates by directly refining randomly initialized noise within the Gaussian noise domain, progressively aligning it with a more suitable target noise sample. Additionally, the authors present a technique called Mobile Velocity Guidance (MVG), which enhances the balance between editability and fidelity by calculating the difference between source- and target-conditioned velocity fields in the image domain. Finally, the authors propose a new image editing benchmark that uses long prompts, named DNA-Bench.\n\nExperiments on both PIE-Bench and DNA-Bench, along with qualitative evaluations, demonstrate the competitive performance of the proposed method.", "questions": "Overall, the paper is of good quality, presenting an interesting and novel idea supported by strong qualitative and quantitative results. Below, I offer several comments and suggestions to further enhance the quality of the submission:\n\n\n1. Please add user study results (Weakness 1)\n\n2. Please add KV-edit to comparison  (Weakness 2)\n\n3. Please elaborate how parameter $\\eta$ quantitatively influences editing quality  (Weakness 3)\n\n4. Please elaborate on style editing scenarios  (Weakness 4)", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strengths:\n\n1. The ideas of DNA and MVG are novel and original\n\n2. The paper is well-organized and presented in a clear, comprehensible manner\n\n3. Editing examples and experiments results demonstrate the method's strong performance\n\n\n## Weaknesses\n\n1. The qualitative results lack user study\n\n2. Table 1 lacks important baseline - KV-edit [1]\n\n3. Equation 10 introduces $\\eta$ parameter. Could you please provide quantitative effect of this parameter on method performance?\n\n4. It seems that DNAEdit may have problems with editing tasks where background preservation is not required, such as stylization. Could you please elaborate on that?\n\n[1] Zhu, T., Zhang, S., Shao, J., & Tang, Y. (2025). KV-edit: Training-free image editing for precise background preservation. arXiv preprint arXiv:2502.17363.", "was_revised": true, "final_justification": "Authors have addressed all my concerns. I have checked other reviews and rebuttals and believe that the submission is of the very good quality.", "rebuttal": "We sincerely thank this reviewer for the constructive comments and suggestion. We hope our following point-to-point responses can address this reviewer's concerns.\n\n**Weakness 1: The qualitative results lack user study.**\n\nThanks for the nice suggestion. As suggested by this reviewer, we conducted a user study on the 7 top-performing methods in terms of quantitative metrics. The results are shown in the table below. In this study, we randomly selected 15 images, each edited using the 7 competing methods, and invited 20 participants to evaluate the editing results. We asked the participants to evaluate the edited images by considering the following factors: **the perceptual quality of the edited image, the fidelity to the original image, and whether the editing purpose is achieved**. With these factors in mind, each of the participants selected the best and second-best methods for each image. The statistical results are presented in the table. It can be seen that our DNAEdit-FLUX accounts for 68% of the \"best\" selections, far exceeding other methods. In the \"second-best\" selections, our SD-based version also slightly outperforms RF-Solver and FlowEdit-FLUX. These results demonstrate the effectiveness of our method.\n\n| Method           | RF-Solver | FireFlow | InfEdit | FlowEdit-FLUX | FlowEdit-SD | Ours-SD | Ours-FLUX |\n|------------------|-----------|----------|---------|---------------|-------------|---------|-----------|\n| Best (%)         | 9.3       | 3.1      | 4.0     | 6.7           | 0.4         | 8.4     | **68.0**  |\n| Sec-Best (%)     | 22.2      | 13.8     | 5.3     | 20.0          | 1.8         | **27.6**| 9.3       |\n\n**Weakness 2: Table 1 lacks important baseline - KV-edit.**\n\nWe thank this reviewer for the suggestion. There is a discrepancy in the settings between KV-Edit and the baseline methods we compared. The baseline methods we need to compare are pure text-guided editing methods, whereas KV-Edit, in addition to text input, requires pre-defined masks as inputs to distinguish editable and non-editable regions. This gives KV-Edit a unique advantage in terms of background preservation metrics such as PSNR for editing tasks. We will include citations and discussions related to KV-Edit in the revision to clarify the differences between methods with varying settings.\n\n**Weakness 3: Equation (10) introduces parameter $\\eta$. Could you please provide quantitative effect of this parameter on method performance?**\n\nThanks for the suggestion. As suggested, we conducted additional ablation studies on the parameter $\\eta$. The results are shown in the table below.\n\n| Exp. | $\\eta$ | Structure Distance $\\downarrow$ | Background Preservation       |                  |                  |                  | CLIP Similarity       |                  |\n|:----:|:------:|:-------------------------------:|:------------------------------:|:----------------:|:----------------:|:----------------:|:---------------------:|:----------------:|\n|      |        |                                 | PSNR $\\uparrow$                | LPIPS $\\downarrow$ | MSE $\\downarrow$ | SSIM $\\uparrow$  | Whole $\\uparrow$      | Edited $\\uparrow$ |\n|  ①   |  1.0   |             33.98               |             21.97              |      149.84      |      95.19       |      79.32       |       **26.40**       |      **23.23**   |\n|  ②   |  0.9   |             24.52               |             23.64              |      118.20      |      67.05       |      83.06       |        26.16          |       22.98      |\n|  ③   |  0.8   |             18.87               |             24.99              |       95.06      |      50.45       |      85.71       |        25.79          |       22.87      |\n|  ④   |  0.7   |           **15.32**             |           **26.06**            |      **79.43**   |     **39.20**    |     **87.58**    |        25.39          |       22.06      |\n\nAs can be seen from the above table, when $\\eta = 1$, the denoising relies entirely on $v^{\\text{tgt}}$. Although it achieves a high CLIP score, the structural distance and background preservation are extremely poor (*e.g.,* the LPIPS metric reaches 149.84).  \n\nAs $\\eta$ decreases to 0.9–0.8, the influence of $v^{\\text{mvg}}$ increases, leading to significant improvements in structural consistency and background preservation. The LPIPS metric improves to 95.06, indicating that over-editing is mitigated. Although the overall CLIP score decreases slightly, the CLIP score for the edited region remains relatively stable, only falling from 23.23 to 22.87. This suggests that setting $\\eta$ around 0.9–0.8 can guide editing toward the target region while preserving the background.  \n\nWhen $\\eta = 0.7$, the improvements in structural and background metrics become limited, and the CLIP score is severely compromised: the CLIP score for the edited region drops to 22.06. An excessively small $\\eta$ causes the editing to over-prioritize the original image content, resulting in editing failures.  \n\nThis ablation study demonstrates the impact of $\\eta$ on benchmark performance: an $\\eta$ value between 0.9 and 0.8 generally yields better editing results. We will add this study in the revision.\n\n**Weakness 4: It seems that DNAEdit may have problems with editing tasks where background preservation is not required, such as stylization. Could you please elaborate on that?**\n\n\nDNAEdit can also perform well in global editing tasks such as stylization. Although stylization does not require background preservation, information like the layout and colors of the original image still needs to be retained. Therefore, accurately identifying the initial noise is crucial for stylization, and our Direct Noise Alignment can achieve this goal. In addition, the MVG we designed can adaptively calculate the regions that need modification by computing the velocity difference between conditioning on the source prompt and the target prompt. For the target prompt in stylization, it will modify the entire image. \nDue to rebuttal rules, we cannot provide new visual demonstrations of stylization. However, the reviewer can refer to the comparisons in the last two rows on the right side of Figure 10 in the Appendix, where our DNAEdit can achieve the target stylization while preserving the contents of the original image."}, {"review_id": "3xP6fQ3eiJ", "reviewer": "Reviewer_rquf", "summary": "Existing image editing methods (diffusion-based and Rectified flow methods) suffer from noise accumulation during the inversion process. The work proposes DNAEdit, consists of Direct Noise Alignment (DNA) and Mobile Velocity Guidance (MVG). DNA refines the Gaussian noise in the latent space directly by aligning predicted and expected velocity fields at each timestep, thereby reducing reconstruction drift. MVG can balance background preservation and target object editability. Additionally, the authors propose DNA-Bench, a new benchmark for evaluating long-prompt editing tasks.", "questions": "Aside from the questions proposed in  \"weakness\" section, I d like the authors clarify the following questions:\n1. The concern about the efficiency of the proposed methods, including the inference speed with baseline.\n2. The reason why the DNAedit methods can boost the long prompt editing.\n3. The authors show DNAedit can be applied into video editing domain as well, which is promising. Thus, i hope the authors can provide more details and techniques about this part.", "limitations": "The limitations of the work are mainly as follows:\n1. some typos and bad formulations should be improved.\n2. more experimental results should be revealed.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "### Weaknesses\n\n- The authors need to carefully recheck the correctness of the formulas to avoid typos. For example, in Algorithm 1, the calculation of the residual offset should be $Z_t^*−Z_t$​. These formulas are crucial to the method's implementation.\n- I would like to know where the superiority and uniqueness of the design of Equation (5) lie. Have the authors considered and compared other interpolation methods? For example, an alternative formulation could be $Z_t^* = \\sigma_t \\times Z_T + (1-\\sigma_t) \\times S_{t+1}$. Compared to such a formulation, only $S_{t+1}$ is inaccurate in this case, whereas in the original Equation (5), both $S_{t+1}$ and $Z_{t+1}$ are approximated. Could the authors elaborate more on the motivation behind their design and its advantages?\n- Could the authors please provide more specific details regarding the experimental settings, including the number of optimization steps $T$, the Classifier-Free Guidance scale, and any other relevant hyperparameters? This information would be helpful for better understanding and reproducing the results.\n\n### strength\n\n- This paper proposes a novel DNA inversion method, which gradually aligns randomly sampled Gaussian noise to the ideal inversion noise through an iterative process, achieving excellent reconstruction performance. The method introduces MVG for image editing.\n- A long-text prompt benchmark, named DNA-bench, is proposed to evaluate text-driven image editing methods.\n- The proposed method achieves comparable performance on PIE-bench and SOTA result on DNA-bench.", "was_revised": true, "final_justification": "Most of my concerns have been addressed in the rebuttal stage.", "rebuttal": "We sincerely thank this reviewer for the constructive comments and suggestion. We hope our following point-to-point responses can address this reviewer's concerns.\n\n**Weakness 1: The authors need to carefully recheck the correctness of the formulas to avoid typos. For example, in Algorithm 1, the calculation of the residual offset should be $Z_t^\\*-Z_t$. These formulas are crucial to the method's implementation.**\n\nThank you very much for carefully reading our work and pointing out the issue. We are sincerely sorry for this typo. We will carefully check all formulas throughout the paper to ensure their correctness and enhance readability.\n\n**Weakness 2: I would like to know where the superiority and uniqueness of the design of Equation (5) lie. Have the authors considered and compared other interpolation methods? For example, an alternative formulation could be $ Z_t^\\* = \\sigma_t \\* Z_T + (1 - \\sigma_t) \\* S_{t+1} $. Compared to such a formulation, only $ S_{t+1} $ is inaccurate in this case, whereas in the original Equation (5), both $ S_{t+1} $ and $ Z_{t+1} $ are approximated. Could the authors elaborate more on the motivation behind their design and its advantages?**\n\nWe actually considered the interpolation method mentioned by this reviewer, but we finally chose the interpolation scheme presented in the paper. This is because our goal is to align the expected velocity from $ Z_t $ to $ Z_{t+1} $ with the predicted velocity at $ Z_t $. In contrast, the interpolation method mentioned by this reviewer aligns the velocity from $ Z_t $ to $ Z_T $ with the predicted velocity at $ Z_t $. According to the denoising ODE process of the flow matching model, *i.e.*, $ Z_{t+1} = Z_t + v_\\theta(Z_t)(\\sigma_{t+1} - \\sigma_t) $, our presented interpolation scheme reduces the error at each step, minimizing the overall error more effectively during reconstruction.  \n\nIn addition, our interpolation scheme offers an extra advantage. The residual offset generated during the DNA process enables error-free reconstruction. The details can be found in Sections A.2 and A.3 of the Appendix.  \n\nRegarding the question that \"in  Equation (5), both $ S_{t+1} $ and $ Z_{t+1} $ are approximated, while in your proposed interpolation scheme, only $ S_{t+1} $ is approximated\": by comparing Equation (5) with the interpolation scheme mentioned by this reviewer, we can see that the coefficients for the common term $ S_{t+1} $ are $ 1 - \\frac{\\sigma_t}{\\sigma_{t+1}} $ and $ 1 - \\sigma_t $, respectively, which correspond to sequences such as \\{1/10, 1/9, 1/8..., 1/2, 1\\} and \\{1/10, 2/10, 3/10, ..., 9/10, 1\\}. Since the initial noise $ S $ is sampled randomly, its error is relatively large. However, the coefficient sequence of our interpolation scheme will rely less on $ S_{t+1} $ when $ S_{t+1} $ is unreliable, therefore it will not increase the error during the noise alignment process.\n\n**Weakness 3: Could the authors please provide more specific details regarding the experimental settings, including the number of optimization steps , the Classifier-Free Guidance scale, and any other relevant hyperparameters? This information would be helpful for better understanding and reproducing the results.**\n\nIn our experiments, for the FLUX model, we use 28 optimization steps, set the Classifier-Free Guidance (CFG) scale to 2.5, and set $t_s$ to 4. And for the SD version, we use the SD3.5-medium model, with DNA steps set to 40, $t_s$ set to 13, and CFG set to 3.5. Both versions have the MVG coefficient $\\eta$ set to 0.8. Please refer to Section C.1 in the Appendix for more details.\n\n**Question 1: The concern about the efficiency of the proposed methods, including the inference speed with baseline.**\n\nThanks for the suggestion. We compare the efficiency of our model with the baseline methods in the table below. Compared with DDIM-based methods, RF-based methods have an overall smaller number of sampling steps because of their straighter trajectories. Compared with RF-based methods, our DNAEdit has more NFEs than FireFlow but outperforms other RF-based methods. In terms of inference time, DNAEdit introduces little additional computational cost, as all operations involved are simple calculations. Overall, DNAEdit is comparable to other RF-based methods in terms of efficiency.\n\n| **Methods**       | **Model**       | **NFEs** | **Time(s)** |\n|-------------------|:---------------:|:--------:|:-----------:|\n| DNAEdit-FLUX      | FLUX            | 48       | 8.35        |\n| DNAEdit-SD        | SD3.5-medium    | 54       | 4.05        |\n| RF-Inversion      | FLUX            | 56       | 11.2        |\n| FireFLow          | FLUX            | 32       | 5.9         |\n| RF-Solver         | FLUX            | 60       | 10.84       | \n| FlowEdit-FLUX     | FLUX            | 48       | 8.32        |\n| FlowEdit-SD       | SD3-medium      | 66       | 3.14        |\n| InfEdit           | LCM             | 24       | 1.92        |\n| PnP               | SD 1.5          | 100      | 10.31       |\n| MasaCtrl          | SD 1.4          | 100      | 20.07       |\n\n**Question 2: The reason why the DNAEdit methods can boost the long prompt editing.**\n\nOn one hand,  as illustrated in Figure 1 (d) of the main text, the DNA process injects information from the original image into the noise by computing the velocity differences $v_t^{src}-v_t^{linear}$, where $v_t^{src} $ is the velocity conditioned on the source prompt. For long prompts that have more detailed information, the DNA process will inject the original image structure more precisely into the noise, thereby better preserving the information of the original image. On the other hand, in our MVG process, it is important to note that $ v^{mvg} $ is derived from the difference between $ v^{src} $ and $ v^{tgt} $, which are conditioned on the source and the target prompts, respectively. More detailed prompts will make this difference focus more precisely on the editing region. In summary, DNAEdit can better leverage the advantages offered by long prompts, thereby achieving superior editing performance. \n\n**Question 3: The authors show DNAEdit can be applied into video editing domain as well, which is promising. Thus, I hope the authors can provide more details and techniques about this part.**\n\nOur method is model-agnostic. Thus, applying it to video editing can be done by implementing the DNA and MVG processes as we do in image editing.   The key differences lie in that we need to employ a pre-trained text-to-video (T2V) model instead of a text-to-image (T2I) model and adjust the hyperparameters.   In our experiment, we used the Wan 2.1 1.3B T2V model. The number of sampling steps is set to 50 by default. For the DNA process, the CFG scale is 1. For the MVG process, the CFG scale is 5.0 (as recommended by the Wan T2V model), $t_s$ is set to 12, and the MVG coefficient ranges from 0.9 to 1.  Such a simple modification in implementation further demonstrates the generality of DNAEdit for RF-based generative models."}, {"review_id": "3qwxPaTu62", "reviewer": "Reviewer_iQQ3", "summary": "This paper proposes a text-guided image editing method, DNAEdit, for the rectified flow model. The method is motivated by the limitation of the previous method, which suffers from estimation error during the reverse process. To tackle this problem, this paper proposes the Direct Noise Alignment (DNA) to optimize the random noise in the Gaussian space, and the Mobile Velocity Guidance (MVG) to apply editing while preserving the unchanged regions.  The effectiveness is evaluated on the PIE-Bench and a new proposed DNA-Bench considering longer text inputs.", "questions": "Would initializing the Gaussian noise by obtaining from previous methods, following **Eq.4** rather than random noise, lead to better performance when combined with the proposed DNA approach?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n- The proposed method is well motivated. The estimation error during reverse is a key challenge for image editing.\n- The contribution is solid, including both a novel editing method from the perspective of latent noise optimization and an evaluation benchmark. \n- The paper is well written and easy to follow.\n\n### Weaknesses\n- As the optimization starts from a randomly initialized Gaussian noise $S_{t+1}$, it raises concerns about the initial estimation error of \n$Z_{t}^{*}$.  Is there a possibility that suboptimal initializations could hinder convergence to the ideal Gaussian noise?\n- The proposed method performs consistently worse than prior methods on the CLIP Similarity metric, as shown in Table 2. Reasons and further analyses are expected for this observation.", "was_revised": true, "final_justification": "I have read the rebuttals, and my concerns are addressed. I keep my positive score and recommend accepting this paper.", "rebuttal": "We sincerely thank this reviewer for the constructive comments and suggestion. We hope our following point-to-point responses can address this reviewer's concerns.\n\n**Weakness 1: As the optimization starts from a randomly initialized Gaussian noise $S_{t+1}$, it raises concerns about the initial estimation error of $Z_t^*$. Is there a possibility that suboptimal initializations could hinder convergence to the ideal Gaussian noise?**\n\nAs shown in Equation (7) in the main paper: $Z_t-Z^\\star_t=\\frac{\\sigma_{t+1}-\\sigma_{t}}{\\sigma_{t+1}}\\times (S_{t}-S_{t+1})$, although the random noise $ S_T $ may initially deviate from the ideal Gaussian noise, such deviations caused by random sampling can be significantly reduced in the interpolated latent variable $ Z_t $ due to the small scaling coefficient $ (\\sigma_{t+1} - \\sigma_t)/\\sigma_{t+1} $, where $ \\sigma_{t+1} $ ranges from 1 to 0. In our implementation, we did not encounter difficulties in convergence caused by the initial Gaussian noise. \n\n**Weakness 2: The proposed method performs consistently worse than prior methods on the CLIP Similarity metric, as shown in Table 2. Reasons and further analyses are expected for this observation.**\n\n\nIn Table 2 of the main paper, when compared with FLUX-based baselines, the  CLIP metrics of our method (CLIP-Whole: 25.79; CLIP-Edited: 22.87) are only slightly lower than those of Fire-Flow (CLIP-Whole: 26.19; CLIP-Edited: 22.99). However, Fire-Flow performs worse than most other methods, including ours, in terms of structural and background preservation metrics. This indicates that Fire-Flow tends to over-edit, *i.e.*,  generating images that align with the target prompt but at the price of preserving the original structure, which artificially inflates its CLIP scores. \nFor SD3-based methods, a similar phenomenon can be observed. Although our CLIP metrics are lower than those of FlowEdit-SD, it can be seen that our method's structural distance (14.19) is much lower than that of FlowEdit (27.12), and it has significant gaps in background preservation metrics such as PSNR (26.66 vs 22.22 lower is better). This indicates that FlowEdit also suffers from over-editing. In addition, from the visual comparisons in Fig. 4 of the main paper and Figs. 9 and 10 of the Appendix, it can be seen that the results of FlowEdit-SD have the issue of over-editing. Moreover, FlowEdit requires a relatively large CFG coefficient, which leads to obvious over-saturation and severely reduces visual quality.\n\n\n**Question 1: Would initializing the Gaussian noise by obtaining from previous methods, following Eq.4 rather than random noise, lead to better performance when combined with the proposed DNA approach?**\n\n\nPrevious inversion-based methods do not sample noise; instead, they start from a clean image and estimate noise step by step to obtain a Gaussian noise, *i.e.*, the so-called noise inversion. In contrast, our method directly starts from a random Gaussian noise, so it does not need to utilize previous methods for noise initialization. Following this reviewer's suggestion, we conducted an experiment using the inverted noise by FireFlow as the initial noise of our DNA process. \nThe results are shown in the table below. We can see that the two initialization methods show almost no difference, but this inversion-based initialization introduces an additional 50\\% computational load. This experiment can also demonstrate that our DNA method is insensitive to the randomness introduced by initialization—any sampled Gaussian noise can be optimized to the ideal noise.\n\n| Exp.                     | Structure Distance $\\downarrow$ | Background Preservation       |                  |                  |                  | CLIP Similarity       |                  |\n|--------------------------|:-------------------------------:|:------------------------------:|:----------------:|:----------------:|:----------------:|:---------------------:|:----------------:|\n|                          |                                 | PSNR $\\uparrow$                | LPIPS $\\downarrow$ | MSE $\\downarrow$ | SSIM $\\uparrow$  | Whole $\\uparrow$      | Edited $\\uparrow$ |\n| Inversion Noise Initilization |             19.73               |             24.95              |      **94.47**   |      50.47       |     **85.78**    |        25.77          |       22.39      |\n| Random Gaussian Sampling  |           **18.87**             |           **24.99**            |       95.06      |     **50.45**    |      85.71       |       **25.79**       |      **22.87**   |\n| $\\Delta$                 |             -0.14               |             +0.04              |       +0.59      |      -0.02       |      -0.07       |        +0.02          |       +0.48      |"}, {"review_id": "g4O0o9xEYw", "reviewer": "Reviewer_H5bA", "summary": "The paper focuses on image editing and 3 contrbutions:\n1. Instead of running inversion, it directly optimize the latent noise to find a good structed noise that correspondes to the original image. The method is called Direct Noise Alignment (DNA).\n2. It proposes a new vector field for editing in the reverse process, called Mobile Velocity Guidance (MVG), which does not rely on a fixed terminal state.\n3. A new benchmark DNABench is proposed for image editing with long prompts.\n\nThe authors proveded both qualittive and quantitative results to demonstarte the proposed method.", "questions": "1. I'm a bit confused with E.3. In my understanding, \\eta controls the v_t^{tgt} in eq. 10, while v_t^{MVG} controls the background preservation. This seems to be misalined with the definitions of MVG in E.3?\n\n2. what is the choice of start time t_s?\n\n3. typo in Algo. 1, 3rd to the last row, should be Z_t?\n\n4. Regarding MVG, as moving Z_t^{MVG} to the direction of v_t^{tar} - v_t^{src}, will it give less and less control, eventually converging with v^tgt?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength:\n\n1. The motivation is well illustrated and the methods are clearly written.\n2. The authors provide comprehensive expriments, including ablation studies on different component.\n3. Both qualitative and quantiative results demonstarte the effectiveness of the proposed method.\n\nWeakness:\n1. The constructed vector vield is also a bit over-complicated, I think more detailed ablations on MVG and be futher studied.\n2. More explain on why is direct noise alignment a better design choice than inversion?\n3. Current image inversion methods have some weaknesses including hyperparaemter robustness and background robuestness. The proposed method mainly demonstrated the second part but the details in hypermarameter choice should be further illustrated.", "was_revised": true, "final_justification": "The authors answers addressed most of my concerns. The proposed method maps X_0 directly to noises instead of running inversion. I think this is a novel idea, however the design is a bit over-complicated, the writing can be improved to better illustrate the idea, which is the main reason I keep the score.", "rebuttal": "We sincerely thank this reviewer for the constructive comments and suggestion. We hope our following point-to-point responses can address this reviewer's concerns.\n\n**Weakness 1: The constructed vector field is also a bit over-complicated, I think more detailed ablations on MVG can be further studied.**\n\nThanks for the suggestion. Although the formula looks complex, the underlying idea of our constructed vector field is straightforward. We introduce $ v^{mvg} $, a velocity oriented toward region-specific changes in the original image. This can suppress unintended modifications in background areas.\n\nAs suggested, we have conducted additional ablation studies on MVG coefficient $\\eta$ below.\n| Exp. | $\\eta$ | Structure Distance $\\downarrow$ | Background Preservation       |                  |                  |                  | CLIP Similarity       |                  |\n|:----:|:------:|:-------------------------------:|:------------------------------:|:----------------:|:----------------:|:----------------:|:---------------------:|:----------------:|\n|      |        |                                 | PSNR $\\uparrow$                | LPIPS $\\downarrow$ | MSE $\\downarrow$ | SSIM $\\uparrow$  | Whole $\\uparrow$      | Edited $\\uparrow$ |\n|  ①   |  1.0   |             33.98               |             21.97              |      149.84      |      95.19       |      79.32       |       **26.40**       |      **23.23**   |\n|  ②   |  0.9   |             24.52               |             23.64              |      118.20      |      67.05       |      83.06       |        26.16          |       22.98      |\n|  ③   |  0.8   |             18.87               |             24.99              |       95.06      |      50.45       |      85.71       |        25.79          |       22.87      |\n|  ④   |  0.7   |           **15.32**             |           **26.06**            |      **79.43**   |     **39.20**    |     **87.58**    |        25.39          |       22.06      |\n\n\nWe can see that\nan excessively large $\\eta$ value (e.g., 1), which means denoising fully according to the velocity $v^{tgt}$, will generate new images based on the target prompt. Although the CLIP metrics are high, the fidelity to the original image is significantly reduced. On the other hand, an excessively small $\\eta$ value (e.g., 0.7 or lower) will lead to editing failure. Based on our experience, when $\\eta$ is set to around 0.8-0.9, it can both maintain the fidelity of the image and ensure the success rate of editing. We will add this study in the revision.\n\n\n**Weakness 2: More explain on why is direct noise alignment a better design choice than inversion?**\n\n\nAs shown in Equation (4) in the preliminary section of the main paper, during the conventional noise inversion process, we cannot obtain $ Z_t $ to compute $ v_\\theta(Z_t) $ and thus can only use $ Z_{t+1} $ with an incorrect noise level, introducing errors. These errors accumulate gradually during the entire noise inversion process.  \n\nIn contrast, our DNA method leverages the unique property of Rectified Flow; that is, the latents can be constructed via linear interpolation. We construct $ Z_t $ with the correct noise level through linear interpolation, avoiding errors caused by mismatched noise levels. Moreover, by using velocity differences to directly adjust Gaussian noise within the noise domain, DNA reduces accumulated errors, resulting in a better starting point for editing.\nWe will add more discussions in the revision.\n\n**Weakness 3: Current image inversion methods have some weaknesses including hyperparameter robustness and background robustness. The proposed method mainly demonstrated the second part but the details in hypermarameter choice should be further illustrated.**\n\n\nSorry for missing the details. In our experiments, for the FLUX model, we use 28 optimization steps, set the Classifier-Free Guidance (CFG) scale to 2.5, and set $t_s$ to 4. The coefficient $\\eta$ for MVG is set to 0.8. Please refer to Section C.1 in the Appendix for more details of hyperparameter choice. \nIn addition, our method allows for fine control over the editing effect of a single image by adjusting only $\\eta$ while keeping the CFG and $t_s$ fixed, making the selection of hyperparameters simple. \n\n**Question 1: I'm a bit confused with E.3. In my understanding, $\\eta$ controls the $v_t^{tgt}$ in eq. 10, while $v_t^{MVG}$ controls the background preservation. This seems to be misalined with the definitions of MVG in E.3?**\n\nEquation (10) defines $ v_t^{edit} = \\eta v_t^{tgt} + (1-\\eta) v_t^{mvg} $. In Section E.3, we show that MVG can effectively ensure faithfulness to the reference image. A smaller $\\eta$ means that the velocity $ v_t^{edit} $ is closer to $ v_t^{mvg} $, which can better preserve the background of the original image. This is consistent with the formula. Our notation ``MVG=1.0\" (instead of $\\eta=1.0$) in Figure 12 of the Appendix may cause confusion. This was an oversight in labeling, and we apologize for the misunderstanding caused.  Please feel free to raise further questions during the subsequent discussion session if this explanation does not fully address your concerns.\n\n**Question 2: what is the choice of start time $t_s$?**\n\n\nFor the FLUX version, $ t_s $ is set to 4; for the SD version, $ t_s $ is set to 13. For more detailed hyperparameter settings, please refer to Section C.1 in the Appendix.\n\n**Quesstion 3: typo in Algo. 1, 3rd to the last row, should be $Z_t$?**\n\n We sincerely apologize for this typo. We will carefully double-check all formulas throughout the paper.\n\n**Quesstion 4: Regarding MVG, as moving $Z_t^{MVG}$ to the direction of $v_t^{tar} - v_t^{src}$, will it give less and less control, eventually converging with $v^{tgt}$?**\n\n\nYes, as $Z_t^{MVG} $ moves, $ Z_t $ also evolves toward the target image, meaning that the velocity $ v^{mvg} $ directing it gradually converges with $ v^{tgt} $.  \nWe consider this convergence as a reasonably adaptive process: in the initial stage, $ v^{mvg} $ prevents excessive disruption of the image content; in the subsequent stages, it enables content generation aligned with the target prompt to produce the final high-quality edited result."}]

Author rebuttal:

Meta-review: This paper presents a new methods for image editing based on T2I rectified flow models.  The approach works by foregoing inversion and instead gradually adjusts the noise sample.  The resulting method demonstrated state-of-the-art results on image editing tasks when compared with multiple recent baselines.

After rebuttal and discussion the reviewers and AC all agreed that the work warranted acceptance.  Authors should be sure to incorporate the additional results and discussions into the final version of the paper.
Decision: Accept (spotlight)