DCI: Dual-Conditional Inversion for Boosting Diffusion-Based Image Editing
Authors: Zixiang Li
Haoyu Wang
Wei Wang
Chuangchuang Tan
Yunchao Wei
Yao Zhao
Venue: NeurIPS 2025 poster ()
Keywords: Diffusion model; Image Editing
Primary area: applications
TL;DR: We propose DCI to improve diffusion inversion process for image editing.

Abstract: Diffusion models have achieved remarkable success in image generation and editing tasks. Inversion within these models aims to recover the latent noise representation for a real or generated image, enabling reconstruction, editing, and other downstream tasks. However, to date, most inversion approaches suffer from an intrinsic trade-off between reconstruction accuracy and editing flexibility. This limitation arises from the difficulty of maintaining both semantic alignment and structural consistency during the inversion process. In this work, we introduce **Dual-Conditional Inversion (DCI)**, a novel framework that jointly conditions on the source prompt and reference image to guide the inversion process. Specifically, DCI formulates the inversion process as a dual-condition fixed-point optimization problem, minimizing both the latent noise gap and the reconstruction error under the joint guidance. This design anchors the inversion trajectory in both semantic and visual space, leading to more accurate and editable latent representations. Our novel setup brings new understanding to the inversion process. Extensive experiments demonstrate that DCI achieves state-of-the-art performance across multiple editing tasks, significantly improving both reconstruction quality and editing precision. Furthermore, we also demonstrate that our method achieves strong results in reconstruction tasks, implying a degree of robustness and generalizability approaching the ultimate goal of the inversion process. Our codes are available at: [https://github.com/Lzxhh/Dual-Conditional-Inversion](https://github.com/Lzxhh/Dual-Conditional-Inversion)

DCI: Dual-Conditional Inversion for Boosting Diffusion-Based Image Editing
Zixiang Li Haoyu Wang Wei Wang! Chuangchuang Tan Yunehao Wei!!,, Yao Zhao 1.2+
'Institute of Information Science, Beijing Jiaotong University
Visual Intelligence +X International Cooperation Joint Laboratory of MOE
Abstract
Diffusion models have achieved remarkable success in image generation and editing tasks. Inversion within these models aims to recover the latent noise represen- tation for a real or generated image, enabling reconstruction, editing, and other downstream tasks. However, to date, most inversion approaches suffer from an intrinsic trade-off between reconstruction accuracy and editing flexibility. This limitation arises from the difficulty of maintaining both semantic alignment and structural consistency during the inversion process. In this work, introduce CoallConditional Inversion (DCT), a novel framework that jointly conditions on the source prompt and reference image guide the inversion process. Specifically, DCI formulates the inversion process as a dual-condition fixed-point optimization problem, minimizing both the latent noise gap and the reconstruction error under the joint guidance. This design anchors the inversion trajectory in both semantic and visual space, leading more accurate and editable latent representations. Our novel setup brings new understanding to the inversion process. Extensive experiments demonstrate that DCI achieves state-of-the-ar performance across multiple editing tasks, significantly improving both reconstruction quality and edit- ing precision. Furthermore, we also demonstrate that our method achieves strong results in reconstruction tasks, implying a degree of robustness and generalizability approaching the ultimate goal of the inversion process. Our codes are available at:
1 Introduction
Diffusion models have made significant progress in the field of generative artificial intelligence. Among them, latent Diffusion Models (L.MMs) 41 perform the diffusion process compressed latent space rather than the pixel space, enabling more efficient and high-quality image generation and editing. This architectural design has made LDMs powerful and flexible backbone for a wide range
of downstream tasks, such text-to-image generation [36 40 43 image editing [3 48 3 image restoration 29 [T (44) style transfer [52] 50 etc. In the image editing tasks, the editing achieved by manipulating the diffusion latent representations. However, in most cases, the corresponding latent representation for given image not directly available, which means that we must first perform an inversion process to obtain their latent representations.
The earliest inversion method DDPM 16), and has inspired the development of numerous related methods [47 2 19]. DDPMs add random noise at each timestep, which leads to the loss of information contained in the original image, resulting in poor reconstruction and editing effects. DDIM inversion [0] reformulates the diffusion process to be deterministic solving implicit equation under the assumption that consecutive points along the denoising trajectory remain close.
Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

DDIM
(c) DirecINV
(e) DCI
D
Fixedipoirr
D
D
BND
Nous
Image refenenes:
Dan
NTL. NP
(d) SPDINV
U-Net
Der
Disi
D
Editing path
D
High Edit Fidelity
Reconstruction path
Editing path
Invension path
Reference path
Interaction
optimizztionn
Figure 1: Pipelines of different inversion methods in diffusion-based image editing. Each sub-figure illustrates the specific process: (a) DDIM inversion; (b) NTI and NPI; (c) DirectInv; (d) SPDInv; (e) our Dual-Conditional Inversion(DCID. Obviously, DCI significantly reduces both latent noise gap( (noo)) and reconstruction error(Drec).
However, in practice, especially when using a limited number of denoising steps, this assumption often breaks down, leading to significant inaccuracies in the inversion results. In order improve the reconstruction effect of DDIM inversion, multiple works have proposed effective optimization methods, such as null-text embedding((TT) process. As illustrated in figure ! both NTI and NPI attempt to reduce the reconstruction reeeDree) by optimizing the text embeddiigs. In the meanwhile, the researchers have proposed some alternative solutions from a non-optimization perspective. For instance, DirectInv (20] introduces a target-aware branch correct the source branch trajectory. improving reconstruction quality. It performs well inversion methods. Renoise 13 based on the linear assumption that the direction from 2g to 2141 can be approximated by the reverse direction from to -1- By calculating the direction from 2, to 2++1 multiple times and taking the average, a more accurate direction from 2, to Σt+1 could be obtained. SPDInv (25) uses optimization method to bridge the latent gap on each timestep, but the improvement of reconstruction gap (Drec) is limited. Although these methods have achieved certain success, they still face intrinsic trade-off between reconstruction accuracy and editing flexibility. As illustrated in figure M such approaches struggle to reconcile semantic precision with structural consistency, particularly when textual supervision is sparse ambiguous.
33 and negative prompt(NPI) 332 in the inversion
especially in terms of content preservation, and it is faster than optimization-
In this work, we present Dual-Conditional Inversion (DCT), a new perspective on diffusion-based image editing that unifies text and image conditioned inversion within a fixed-point optimization framework. DCI addresses this limitation by introducing conditooni mechanism: jointly leverages the source prompt ps and the reference image æn to guide the inversion process. At the core of our formulation is a two-stage iterative procedure. The first stage, eeference-guiiee noise correction, refines the predicted noise at each timestep by anchoring it to visually grounded reference derived from the source image. The second fixed fixed-point latent refinement, imposes self- consistency by optimizing each latent variable as fixed point of the generative trajectory defined by DDIM dynamics. Formally, we cast inversion as a conditiond fixed-point optimization problem that minimizes two objectives: (1) the discrepancy between the predicted and reference noise vectors across timesteps, and (2) the reconstruction error between the generated image and the original reference. This formulation not only improves inversion stability but also yields latent representations that are inherently editable and semantically aligned.
To sum up, our framework enables plug-and-play integration with variety of existing diffusion models, requiring neither retraining nor any modification to the original model. Through extensive experiments across multiple editing tasks, DCI achieves superior reconstruction quality and editing

---

fidelity when compared to prior inversion baselines. Moreover, we demonstrate that the proposed dual-conditional fixed-put point formulation facilitates stable convergence and generalizes well across a wide range of editing scenarios, highlighting the robustness and scalability of the proposed approach.
2 Related Work
2.1 Image Editing with Diffusion Models
In recent years, large number of works based on diffusion models in the field of image editing demonstrate significant potential and adaptability across diverse tasks. These methods utilize diverse forms of guidance, such as text prompts, image references and segmentation maps to achieve editing objectives. [24 22 8
17] [2]] These advances better enable the ability to maintain editing precision and semantic consistency. The rapid development of diffusion models has significantly improved image generation capabilities. Among them, the widespread use of models such GLIDE (36), Imagen [3). DALL-E2 [40], and Stable Diffusion(SD) [4] has gradually expanded downstream tasks based on image generation. models to enable text-driven image editing while preserving spatial structure through localized prompt adjustments. Pix2pix-zero (38 achieves zero-shot image-to-image translation by aligning latent features with text guidance. Plug-and-Plas [48] integrates task-specific modules into pretrained diffusion backbones without retraining. MasaCtrl 4 enhances real-time spatial control in diffusion models by injecting mask-guided attention constraints for precise region-specific manipulation. IP- Adapter (56) injects visual features into the attention mechanism, enabling personalized generation without fine-tuning, ControlNet (57) introduces an auxiliary network to condition diffusion models on structural inputs like edges or poses. Some recent efforts have proposed different approaches
porrt-tooPPoopppPPPP) [5] modifies cross-attention maps in diffusion
to improve the precise of image editing from various perspectives [53] 34 21]. Despite these methods have shown promising results, they often suffer from editing failures due to inversion methods. Our DCI improves upstream inversion to enhance downstream editing fidelity.
2.2 Inversion methods of diffusion models
The earliest inversion methods include DDPM 19] and DDIM [45]. DDPM generates high-quality images by progressively adding noise in forward process and learning the reverse denoising process. 99 46 Building on this foundation, DDIM introduces a deterministic sampling mechanism. Its near-invertible properties provide crucial foundation for subsequent image inversion and editing techniques. Researchers have conducted in-depth and extensive studies on the inversion process of diffusion models achieve both efficiency and precision. Some methods focus on optimizing text embedding 333 [2) 14]. Null-Text Inversion (NTI) (33 adjusts latent encodings and text embeddings reconstruct the original image. To improve efficiency, PigaativeProope Inversion (NPI) (32) and its enhancements, including Proximal Guidance [14], have emerged to reduce the reliance on time- consuming optimization processes. EDICT [49], for example, achieves exact invertibility through coupling transformations, while methods like Direct Inversion (20) and Fixed-Point Inversion [30] focus on simplifying the inversion process. The former decouples the diffusion branches, while the latter utilizes fixed-poin point iteration theory to ensure high reconstruction quality while reducing computational overhead. Many inversion techniques also particularly focus on improving downstream editing tasks [1) For example, Source Prompt Disentangled Inversion (SPDInv) (5 aims to decouple image content from the original text prompt, enhancing editing flexibility and accuracy. Specialized inversion and editing frameworks have been developed for specific editing needs 26 44
Additionally, the concept of inversion has been extended to broader domains [12 I8
I
6 59
Textual Inversion proposes learning new text embeddings to represent user-specific concepts for
personalized image generation [2]. ReVersion [8] further explores learning and inverting relational concepts from images. Meanwhile, works like Aligning Diffusion Inversion Chain (59] focus on generating high-quality image variants by aligning inversion chains.
Although the above methods have solved the reconstruction problem to certain extent, they may bring artifacts and inconsistent details when applied to editing tasks. Most the time, they only focus on the text prompt the original image, but do not integrate them. In our work, we propose a simple but effective method fuse the text prompt and source image in the form of fixed-point iteration. Our method improves the editing fidelity a lot and shows inspiring results.
3

---

DCI: Dual-Conditional Inversion
Inversion Process
X
U-Nett
DDIM
ENC
Editing with
Image reference
DC1
Latent pap
New latens
Latent noise representation in timestep
Ideal latens
Input image
Figure 2: Inversion process of DCL. The green box on the left illustrates DCI, which use dual- conditional guidance reduce the latent gap. The right describes how DCI modifies the inversion process and generate the latent noise code. It also shows our method can improve the editing method.
Dual-Conditional Inversion
3.1 Motivation and Problem Formulation
In most diffusion-based image editing frameworks, the inversion process plays foundational role: converts image to the latent noise representation from which the image be reconstructed and edited. However, diffusion models inherently lack an explicit and exact inverse process to convert an image back its corresponding latent noise representation. Ideally, successful inversion would yield a latent code 2r that faithfully preserves both the semantic content and structural details of the input image, thereby enabling accurate reconstruction and precise downstream editing. However, the information loss caused by repeated noise injection in inversion process makes perfect inversion unattainable, even when auxiliary constraints such as text prompts or reference images are employed.
To analyze the limitations current inversion strategies, we begin with DDIM (Denoising Diffusion Implicit Models) [45], deterministic variant of DDPM (16). DDIM defines closed-form sampling process that generates a latent image 2n from Gaussian noise 2T ~ NO I) as follows:
Var-1 vau-i
1 04-1
Ea ca(2t,
Where a denotes the cumulative noise schedule, and Eş represents the noise predicted by U-Net, conditioned on the current timestep and a control input (e.g. text prompt). However, using only text prompt as e insufficient for accurately reconstructing the original image. Recent methods such as ControlNet [57] and IP-Adapter [56] enrich the conditioning input with visual features from the original image, thereby improving generation quality. Nevertheless, these methods are often computationally expensive and difficult to integrate into the inversion process. Ideally, inversion requires recovering 2, from known 2t-1, which leads to the following "ideal inversion" formula:
2t = = -t-1 + C2 Eg(22; t, Cidcal),
(2)
where the coefficients are defined as: C₁,1
Vor Vac_1'
C2. var (V
However, in practice, this expected inversion not feasible because the ideal latent 2, is not available when performing the inversion step from 2,-1- Thus, the DDIM inversion process approximates this update by feeding (2--1 - 1, c. into the inversion process instead of (ze, 1, c), leading to the practical inversion formula:
21 C,.1- C2 c0(21-1, 1,c).
(3)
This approximation breaks the strict reversibility of the ODE-based formulation and introduces temporal mismatch error between the predicted noise and the actual generative trajectory. Since the diffusion model assumes infinitesimal step size for reversibility (akin continuous ODE), using coarse discrete steps and mismatched inputs (i.e., c0 1, 1,c) instead of the ideal (, t, c)) induces systematic enror each timestep.
If a real image and its corresponding text prompt are given, the image generated directly using the text prompt will be very different from the real image. The reason arises from the inaccuracy of text

---

prompt and randomness in the generation process. From this perspective, there are also errors in the use of (0 t, c)) for the inversion process. This error is also accumulated over time, resulting in the final not being well applied to reconstruction and editing. In previous work, SPDInv [25] transforms the inversion process into a search problem that satisfies fixed-poin constraints. The pre-trained diffusion model is used to make the inversion process as independent of the source prompt as possible, thereby reducing the gap between Eg(zt, and eœ 62 - 1, c). Although SPDInv narrows the gap between cœ(z,, i, c)) and Eœ (=(-11, 1, c). However, in the previous analysis, (o(z,,,c)) is not an ideal noise. The ideal noise should not only be separated from the source prompt, but also retain more information of the original image. What needs to be reduced is the difference between eg (2t, t, Cideat) and c0(2)11t 1, c), and this difference will appear in each inversion process and accumulate in the final output.
To achieve high-fidelity inversion, it is essential to minimize the discrepancy between the predicted noise and the ideal generative direction at each timestep. This requires not only disentangling the inversion process from the souree prompi(mentioned in (25)), but also preserving as much information from the original image possible. Addressing both aspects simultaneously is key to reducing cumulative errors and improving the reconstruction and editability of the inverted latent noise representations in diffusion-based image editing.
3.2
Dual-Conditional Inversion (DCI)
To address the limitations of existing inversion methods, we propose Dual-Conditional Inversion (DCI), a novel framework that enhances the latent noise representations in diffusion models. DCI leverages both the original image and text prompt guide the inversion process, ensuring high- fidelity reconstruction and improved editability. Unlike prior approaches, DCI integrates these into dual-conditional fixed-point optimization pipeline. The method consists of two key stages: reference-guice noise correction that anchors the inversion to the source image, and fixed-point latent refinement that ensures self-consistency with the generative process.
3.2.1 Reference-Guided Noise Correction
The first stage of DCI introduces a reference-based constraint to align the predicted noise with the source image. At each DDIM timestep t, we compute initial noise estimate conditioned on the source prompt ps:
&raw = co(2e, f, Px)).
(4)
where eg is the noise prediction model (e.g., a U-Net) and 21 is the current latent. However, &naw often deviates from the ideal noise due to the coarse constraint of p.. While this prediction reflects prompt-level semantics, it often deviates from the actual noise corresponding to the input image due to limited grounding provided by textual information alone. To address this, we introduce a visual reference signal by extracting a reference noise vector Get from the source image latent 20, which is obtained via pretrained VAE encoder E. The reference noise is defined as:
Gref
(2(2))
The Gre serves anchor to guide the correction of prompt-based noise estimation. To enforce alignment between the prompt-predicted noise and the image-derived reference, we define reference alignment loss:
Liet = 2rww - G
(6)
Equation 6 penalizes the discrepancy between the two noise vectors. A one-step gradient-based correction then applied to refine the noise prediction:
Erow
Vien Crer-
(7)
where À is hyperparameter that controls the correction strength. This update adjusts the predicted noise a direction that reduces its divergence from the reference signal, effectively grounding the inversion in visual structure. As result, this correction improves reconstruction fidelity and ensures that the denoising trajectory remains semantically and perceptually consistent with the original image, particularly in scenarios where the prompt is ambiguous or underspecified.
5

---

3.2.2 Fixed-Point Latent Refinement
After correcting the noise estimate, we proceed to update the latent variable 2, using the DDIM inversion formula. This step changes the inversion trajectory from timestep t, based on the corrected noise E:
2t = Cr,1 žt-1 + Ct,2 é,
ch-1 0=- -1) and Oe is the noise schedule. While this deterministic update follows the DDIM trajectory, it remains sensitive to error accumulation during the inversion process. As such, it may introduce perturbations into the latent dynamics, ultimately affecting reconstruction and editing fidelity. To improve stability and enforce consistency with the forward generative process, DCI introduces fixed-point refinement step that iteratively corrects the latent by treating it as a fixed-point problem of the DDIM inversion at each timestep. Specifically, we define the latent update function:
where C₁,1- v0r-1 and Ct2 - Vac V 11-1--
fo(z)) = Ct,1 2t-1 + Ct. co(21,t,ps).
(9)
The objective is to find latent 2, such that:
2x = fz(2).
(10)
To achieve this, we minimize the following fixed-point self-consistency loss:
Lax (z)) zell2
(11)
We iteratively refine using gradient descent:
2t = 2x - Cix:
(12)
where 7) is the learning rate of refinement process. This fixed-point update step is repeated for up to K iterations or until the convergence criterion Lax < 5 is satisfied. In practice, our method converges rapidly within few iterations usuully more than 10 iterations), which ensures computational efficiency without compromising reconstruction quality. By explicitly enforcing this self-consistency constraint, DCI stabilizes the inversion trajectory and reduces artifacts that arise from misaligned latents. This refinement step not only enhances reconstruction quality but also improves the reliability and flexibility of downstream editing operations.
Algorithm Dual-Conditional Inversion (DCI)
Input: Source image latent 20, DDIM steps T, source prompt P., maximal optimization rounds K threshold 8, image guidance strength fixed-point learning rate 7), reference noise Get
Output: Inversion noise 2T
for 1 to T do
2: for = to K do
3: Get 2, from 2;-1 based on
4:
Predict noise ém based on 44h
5:
Compute Cief = |r Eref 2
6:
Apply correction: é Eraw A. V kn Lref
7:
Update 2, using ē
8:
Caleulate Lıx (2() zell2
9:
Update 21 = 2, - 77 V., Lix
10: if Lix < å then break end if
11: end for
12:
end for
3.2.3 Algorithm Summary
The complete Dual-Conditional Inversion (DCI) process is summarized Algorithm 1 At each DDIM timestep. DCI first performs Snffernne-Guiilee Noise Correction to obtain visually grounded noise estimate by combining prompt-based prediction and reference-derived supervision. Then it is followed by Fixed-Polis Latent Refinement, which iteratively updates the latent 2; to satisfy

---

Source
DDIM
NTI
DirectInv
SPDInv
h-edit
DCI
round cake with orange... a square cake with orange...
plate with steak it plate with salmon it
woman holding pink flower woman holding teddy bear
house with Hightning-and-re and-rain on it a house with rain on it
girl with is holding phone - a girl with is holding a coffee
Figure 3: Visual results of different inversion methods with P2P PIE-Bench. Each method is identified at the top of its respective column, while detailed editing information appears beneath each corresponding row. DCI(ours) demonstrates significant enhancements over existing methods.
self-consistency condition defined by the DDIM inversion dynamics. The dual conditioning on both the source prompt p, and the reference image (via Get) ensures that the final inverted latent 2y closely approximates the ideal generative noise zy, which leads to reliable reconstruction and high-fidelity, better structure- structure-preserving editing.
Experiments
We conduct extensive experiments to evaluate the effectiveness of Cual-Conditional Inversion (DCI). This section is organized follows. In Section 4.1 introduce the datasets, evaluation metrics and experimental settings. Section 4.2 compares DCI with representative inversion methods across multiple aspects quantitatively and qualitatively. Section 4.3 we investigate how DCI reduces both the latent noise gap and the reconstruction error. Finally, Section 4.4 presents an ablation study to assess the impact of key hyperparameters and design choices.
4.1 Experimental Setups
Evaluation Metrics. We mainly use DINO score [5]. Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Structural Similarity Index (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) (58) to evaluate the performance of DCI from multiple perspectives. We use the DINO score to evaluate the overall structural similarity of the generated images, while the CLIP score 39 is employed to quantify the alignment between the generated image and the given prompt. For background preservation and image fidelity, we report PSNR, MSE, SSIM, and LPIPS, with all metrics computed specifically over the annotated regions in the dataset from DirecInv (20 Both the DINO and CLIP scores are calculated over the entire image to capture global consistency, whereas the remaining metrics focus local quality within specified regions.

---

Table 1: Performance comparison of inversion praseed methods under the Compptto--Pommy (P2P)
editing engine [T] on PIE- PIE-Bench. Metrics include DINO (4), PSNR (t), LPIPS (4), MSE (L), SSIM
( and CLIP (t). Best and second-best results are highlighted in red and blue, respectively. DCI
(ours) achieves the best performance across all metrics.
DINO x10
LPIPS x10
MSEJ X x10
SSIM x102
Inversion
Editing Engine
PSNR
CLIP
DDIM
45
P2P
69,43
17.87
208.80
219.88
71.14
25.01
NTI 33
P2P
13.44
27.03
60.67
35.86
84.11
24.75
NPI 32
P2P
16.17
26.21
69.01
39.73
83.40
24.61
AIDI
P2P
12.16
27.01
56.39
36.90
84.27
24.92
NMG 6
P2P
23.50
25.83
81.58
107.95
82.31
24.05
DirectINV
P2P
11.65
27.22
54.55
32.86
84.76
25.02
ProxEdit [4]
P2P
11.87
27.12
45.70
32.16
84.80
24.28
SPDInv 22
P2P
8,81
28.60
36,01
24.54
86.23
25.26
h-Edit 35
P2P
11.17
27.87
48.50
85.40
84.80
25.30
DCI(ours)
P2P
6.07
29.38
33.01
21.28
87.14
25.52
Datasets. We verifies the effectiveness of our proposed DCI method mainly the PIE-Bench (20]. which comprises 700 images featuring 10 distinct editing types. It provides five annotations on each image: source image prompt, target image prompt, editing instruction, main editing body, and the editing mask. The calculation of region-specifis metries heavily relies on the editing mask, as the
editing expected to occur only within the annotated region. We also use the COCO2017 28 to test the application of our method in a wider range of scenarios.
Other Settings. In our experiments, we utilize Stable Diffusion v1.4 as the base model with DDIM sampling steps 50 and Classifier-Free Guidance (CFG) scale 7.5. These settings are the same as those used in the baselines. For DCI, we set the hyper-parameters to K = 5, À = 2, and 0.001. All experiments and validations are conducted on single NVIDIA RTX 4090 GPU.
4.2 Comparisons with Inversion- -Based Editing Methods
We compare DCI with several inversion- methods quantitatively and qualitatively. These methods includes DDIM inversion [5], Null-text inversion (NTI) [33], Negative prompt inversion
(NPI) 32 AIDI 377, Noise Map Guidance (NMG) [6. Direct Inversion (DirectNV) (0), Prox-
Edit 14 SPDInv 25 and h-Edit (35] We mainly evaluate under the Prompi-to-Prompt (P2P)
Table shows, DCI (ours) outperforms all methods across DINO, PSNR, LPIPS, MSE, SSIM. and CLIP metrics. Compared to the second-bess method, SPDInv, DCI achieves significant improvements, including a 31.1% % reduction in DINO (6.07vs.8.81), 8.3% reduction in LPIPS 33.00vs.36.011 and 13.3% reduction in MSE (21.28vs.24.55 24..44). At the same time, is also higher than SPDInv other metrics method has greater improvement. These results underscore DCI's superior
editing engine on PlE-Bench.
Compared with other methods
listed in Table accuracy and robustness for high-fidelity image editing.
Figure 3 presents visual comparison with the P2P engine. The first row presents cake images frequently used for comparative analysis in existing methods. Most approaches show satisfactory results. In contrast, the second row demonstrates that our method enhances detail representation in salmon. The third row illustrates when modifying features such as hands or mouth, previous methods will fail. However, the fourth row, our method achieves better background color fidelity and reduces lighting artifacts compared to others. The fifth row highlights our method's robust performance in local part editing while preserving overall consistency across other image regions.
DCI achieves this task while maintaining high-quality output. In
For image editing task, human preference is an important part of evaluation metrics. We provide table 2alleidenninngg both user study and human preferences metrics to demonstrate the effectiveness of our approach. For the user study, we collect 40 comparisons from 25 participants (aged to 50). The table shows the mean scores for each participant (min:1, max:5). For human preferences metrics, ImwgeReward [55] and PickScore (33 are human preference-based
Human Preference Results

---

reward models to quantitatively evaluate the quality of image generation and editing. Both of them higher metries that represent better performance.
Table 2: Human preference results
DDIM
SPDInv
DCI
Pickscore
2
0.4416
0.4954
0,5547
ImageReward 55
-0.0120
0.1564
0.3674
User
2.14
3.48
4.10
Time Consumption The running time of DCI is tied to the number of optimization iterations and the error threshold. However, since our method primarily focuses on nudging the inversion process back onto the correct path, we' empirically found that often only a few optimization steps at specific timesteps are needed to achieve significant improvements. As a result, the additional computational overhead compared with DDIM remains minimal.
Table 3: Comparison of inversion times (in seconds) across different methods.
DDIM
NTI
NPI
AIDI
DirectINV
SPDInv
DCI(ours)
Time(s)
11.55
137.54
11.75
87.21
19.94
27.04
12.13
Results under different editing engines and base models. Other editing engines and other architec- tures of diffusion models can also be adopted for our DCI. We use LEDITS+ [2] as the multi-subject editing model and apply it with both DDIM and our DCI method for fair comparison. In our paper, the reported MSE is calculated for non-edited regions, thanks to the availability of appropriate mask annotations within the dataset. However, for multi-subject editing, we could only calculate the MSE between the edited image and the entire original image. Under these conditions, the MSE metric is always accurate reflection of editing quality, DDIM frequently fails to produce any changes or only generates very minor alterations. Beyond multi- subject editing, we also test our method with Stable-Flow, flow-based diffusion image editing method. The experimental results clearly indicate that our approach significantly enhances performance flow-based methods as well.
Table 4: Performance under different editing engines and base models.
DINO x10
LPIPS x10
MSEJ x10
SSIM x102
Inversion
Editing Engine
PSNR
CLIP
DDIM 45
LEDITS+ 2
21.20
21.18
136.3
76,00
83.95
19.01
DCI(ours)
LEDITS+
12.10
21.19
127.5
125.00
84.41
21.62
DDIM 45
Stablc-Flow 19.00
24.30
91.70
37.00
91.60
23.29
DCI(ours)
Stable-Flow 4.40
24.32
68.40
37.00
92.75
23.64
Due to the page limit, we provide more visual and quantitative results under different editing engines in the supplementary material. We can draw similar conclusions to the above.
4.3 Reduction of Noise and Reconstruction Gap by DCI
Latent Gap
We conduct experiments and confirm that our method can reduce the gap between noise and reconstruction (Dros and Drec as depicted in Figure Π.. We randomly select 100 captions from the PIE- BeE-Bench and use Stable Diffusion V1.4 to generate images.
0.20
0.88
0.16
DOIM
0.14
0.12
0.10
We initialize 2n with a fixed random seed, treating the ideal noise input for every image at the initial timestep of the diffusion process. The final generated image serves as reference for reconstruction accuracy assessment. We visualize and evaluate the performance of our method with DDIM [45] and SPDInv (25).
0.08
0.06
Index sorted by MSE
Figure 4: Illustration of Latent Gap.

---

Table 5: Ablation study the
of DCI with PIE-Bench.
Hyper-parameter
DINO x10*
PSNR
LPIPS 109
MSE x10*
SSIM 1
CLIP
K = 2
6. 13
29.32
33.10
21,50
87.11
25.49
=
6.07
29.38
33.01
21.28
87.14
25.52
=
6.17
29.29
33.17
21.56
87.12
25.51
À
6. 69
29.29
33.12
21.62
87.12
25.53
À =
6.07
29.38
33.01
21.28
87.14
25.52
À = 5
9.29
28.25
41.05
26.18
86.26
25.38
f) 0.0001
6.72
28.80
35.93
23.72
86.70
25.50
7 0.001
6.07
29.38
33.01
21.28
87.14
25.52
7 0.01
35.29
23.05
88.90
83.84
81.18
25.02
Default
6.07
29.38
33.01
21.28
87.14
25.52
For latent gap analysis, we visualize the 2T gap obtained by these methods in figure 4 The con- centration of the data shows that our method is closer to the ideal noise. For reconstruction gap evaluation, we use both MSE and CLIP scores. DDIM yields MSE of 1.32 10 SPDInv achieves 1.21 10 10-4, while DCI obtains the lowest error at 1.12 x 10 10-4. 4 The CLIP Scores are 26.91 for DDIM, 26.92 for SPDInv, and 26.94 for DCI. Comparatively, our technique demonstrates superior performance over DDIM and SPDInv based on these metrics.
4.4 Ablation Study
Table presents an ablation study on three key hyper-parameters of DCI: the number of optimization
rounds (K E {2, 5, 10J), the reference-guide noise correction weight (A E 2,),,5), and the learning rate (17 E {0.0001, 0.001, 0.01)). The method converges quickly, even a small number of rounds (K = 2) shows competitive results, and performance saturates by K = 5. X achieves the lead significant degradation across all metries, indicating over-dependence on inversion constraints. The learning rate = 0.001 provides the most stable and effective optimization; both smaller and larger values reduce reconstruction quality, with y 0.01 causing severe performance collapse. These results support the choice of the default configuration (K" 5, À 2, rj = 0.001) as optimal for fidelity and stability.
best trade-off, while higher values such as
Conclusion
In this paper, we introduce Dual- CoullConditional Inversion (DCI), a novel method that combines both the source prompt and the reference image guide the inversion process. By formulating inversion as a dual-conditioned fixed-point optimization problem, DCI reduces both latent noise gap and reconstruction errors in diffusion models. Notably, DCI exhibits strong plug-and-play capability: it can be seamlessly integrated into existing diffusion-baa editing pipelines without requiring model retraining or architecture modification. Extensive experiments demonstrate that our method achieves superior edit quality on benchmark datasets. Overall, DCI provides a robust, flexible, and easily deployable foundation for future research in diffusion-based tasks.
Acknowledgements
This research supported by the National Natural Science Foundation China 62372033, U24B20179, 92470203, U23A20314), Natural Science Foundation of Beijing, China(No.1.252222 No. 1.242022) and the Fundamental Research Funds for the Central Universities (No. 2024XKRC082).
10

---

References
[1] Omri Avrahami, Or Patashnik, Ohad Fried, Egor Nemchinov, Kfir Aberman, Dani Lischinski, and Daniel Cohen-Or. Stable flow: Vital layers for training-free image editing. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 7877-7888, 2025.
[2] Manuel Brack, Felix Friedrich, Katharina Kornmeier, Linoy Tsaban, P. Schramowski. K. Kerst- ing, and Apolin' ario Passos. Ledits++: Limitless image editing using text-to-image models. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2861-8870, 2023.
[3] Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 18392-18402, 2023.
[4] Mingdeng Cao, Xintao Wang, Zhongang Qi. Ying Shan, Xiaohu Qie, and Yinqiang Zheng. Masactrl: Tuning-free mutual self-attention control for consistent image synthesis and editing. arXiv preprint arXiv:2304, 230.00666,, 2023.
[5] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski. and Armand Joulin. Emerging properties in supe-vuue vision transformers. In Proceedings of the JEEE/CVF international conference on computer vision, pages 9650-9660, 2021.
[6] Hansam Cho, Jonghyun Lee, Seoung Bum Kim, Tae-Hyun Oh, and Yonghyun Jeong. Noise map guidance: Inversion with spatial context for real image editing. arXiv preprint urXiv:2402.04625, 2024.
[7] Jiwoo Chung, Sangeek Hyun, and Jac-Pil Heo. Style injection in diffusion: A training-freee approach for adapting large-scale diffusion models for style transfer. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages
2024.
[8] Guillaume Couairon, Jakob Verbeek, Holger Schwenk, and Matthieu Cord. Diffedit: Diffusion- based semantic image editing with mask guidance. arXiv preprint 1rX /427, 2022.
[9] Valentin De Bortoli, James Thomton, Jeremy Heng, and Arnaud Doucet. Diffusion schrödinger bridge with applications to score-based generative modeling. Advances in Neural Information Processing Systems, 34: 14415795-7770, 2021.
[10] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances Neural Information Processing Systems (NeurIPS), 34:8780-8794 2021.
[11] Wenkai Dong, Song Xue, Xiaoyue Duan, and Shumin Han. Prompt tuning inversion for text- driven image editing using diffusion models. In Proceedings of the IEEF/VVF International Conference on Computer Vision, pages 7430-7440, 2023.
[12] Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint ar arXiv:2208.006 01618, 2022.
[13] Daniel Garibi, Or Patashník, Andrey Voynov, Hadar Averbuch-Elor, and Daniel Cohen-Or. Renoise:
Real image inversion through iterative noising. In European Conference on Computer Vision, pages 995-413. Springer, 2024.
[14] Ligong Han, Song Wen, Qi Chen, Zhixing Zhang, Kunpeng Song, Mengwei Ren, Ruijiang Gao, Yuxiao Chen, Di Liu, Qilong Zhangli, al. Improving regative-promll inversion via proximal guidance. arXiv preprint
2023.
[15] Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfrr Aberman, Yael Pritch, and Daniel Cohen-OU Or. Prompt-to-prompt image editing with cross attention control. arXiv preprint rxXiv:2208.01626 2022.
[16] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 1556840-6851,, 2020.

---

[17] Nisha Huang, Fan Tang, Weiming Dong, Tong- Yee Lee, and Changsheng Xu. Region-aware diffusion for zero-shot text-driven image editing. arXiv preprint arXiv:2302.11797, 2023.
[18] Ziqi Huang, Tianxing Wu, Yuming Jiang, Kelvin CK Chan, and Ziwei Liu. Reversion: Diffusion- based relation inversion from images. In SIGGRAPH Asia 2024 Conference Papers, pages 1-11, 2024.
[19] Inbar Huberman- Spiegellla,, Vladimir Kulikov, and Tomer Michaeli. An edit friendly ddpm noise space: Inversion and manipulations. arXiv preprint 004102200.0061400 2023.
[20] Xuan Ju, Ailing Zeng, Yuxuan Bian, Shaoteng Liu, and Qiang Xu. Direct inversion: Boosting diffusion-based editing with 3 lines of code. arXiv preprint arXiv:2310.01 5066 2023.
[21] Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Hui-Wen Chang, Tali Dekel, Inbar Mosseri, and Michal Irani. Imagic: Text-based real image editing with diffusion models. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
[22] Gwanghyun Kim, Taesung Kwon, and Jong Chul Ye. Diffusionclip: Text-guided diffusion models for robust image manipulation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2426-2435, 2022.
[23] Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy. Pick-a-pic: An open dataset of user preferences for text-to-image generation. Advances in neural information processing systems, 36:36652-36663, 2023.
[24] Mingi Kwon, Jaescok Jeong, and Youngjung Uh. Diffusion models already have a semantic latent space. arXiv preprint arXiv:2210. 10960, 2022.
[25] Ruibin Li, Ruihuang Li, Song Guo, and Lei Zhang. Source prompt disentangled inversion for boosting image editability with diffusion models. In European Conference on Computer Vision, pages 404 421. Springer, 2024.
[26] Senmao Li, Joost Van De Weijer, Taihang Hu, Fahad Shahbaz Khan, Qibin Hou, Yaxing Wang, and Jian Yang. Stylediffusion: Prompt-embedding inversion for text-based editing. arXiv preprint arXiv:2303. 15649, 2023.
[27] Zixiang Li, Yue Song, Renshuai Tao, Xiaohong Jia, Yao Zhao, and Wei Wang. Unsuper- vised region- based image editing of denoising diffusion models. In Proceedings of the AAAI Conference Artificial Intelligence, volume 39, pages 18638-18646 2025.
[28] Tsung- Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer vision-ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, proceedings, part 13, pages 740-755. Springer, 2014.
[29] Xinqi Lin, Jingwen He, Ziyan Chen, Zhaoyang Lyu, Bo Dai, Fanghua Yu, Yu Qiao, Wanli Ouyang, and Chao Dong. Diffbir: Toward blind image restoration with generative diffusion prior. In European Conference on Computer Vision, pages 430-448. Springer, 2024.
[30] Barak Mciri, Dvir Samuel, Nir Darshan, Gal Chechik, Shai Avidan, and Rami Ben-Ari. Fixed- point inversion for text-to-image diffusion models. CoRR, 2023.
[31] Chenlin Meng. Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun- Yan Zhu, and Stefano Ermon. Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.011 01077, 2021.
[32] Daiki Miyake, Akihiro Iohara, Yu Saito, and Toshiyuki Tanaka. Negative- prompt inversion: Fast image inversion for editing with text-guided diffusion models. arXiv preprint arXiv:2305.1690 2023.
[33] Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Null-text inversion for editing real images using guided diffusion models. In Proceedings of the IEEF/CVF Conference Computer Vision and Pattern Recognition, pages 6038-6047, 2023.
12

---

[34] Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, and Jian Zhang, Diffeditor: Boosting accuracy and flexibility on diffusion-based image editing. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 8488 8497, 2024.
[35] Toan Nguyen, Kien Do, Due Kieu, and Thin Nguyen. h-edit: Effective and flexible diffusion- based editing via doob's 's h-transform. arXiv preprini arXiv:2503.02 2025.
[36] Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint urXiv:21 10741, 2021.
[37] Zhihong Pan, Riccardo Gherardi, Xiufeng Xie, and Stephen Huang. Effective real image editing with accelerated iterative diffusion inversion. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15912-15921, 2023.
[38] Gaurav Parmar, Krishna Kumar Singh, Richard Zhang, Yijun Li, Jingwan Lu, and Jun- Yan Zhu. Zero-shot image-to-image translation. In ACM SIGGRAPH 2023 conference proceedings, pages 1- 2023.
[39] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning. PMLR, 2021.
pages
[40] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 1(2):3 2022.
[41] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björ Ommer. High- resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference computer vision and pattern recognition, pages 10684-1069) 2022.
[42] Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 22500-22510, 2023.
[43] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily I. Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Bureu Karagol Ayan, Tim Salimans, al. Photorealistic text-to-image diffusion models with deep language understanding, Advances in Neural Information Processing Systems, 35:36479-36494, 2022.
[44] Yujun Shi, Chuhui Xue, Jun Hao Liew, Jiachun Pan, Hanshu Yan, Wenqing Zhang, Vincent YF Tan, and Song Bai. Dragdiffusion: Harnessing diffuusion models for interactive point-based image editing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8839-8849, 2024.
[45] Jiaming Song. Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In International Conference on Learning Representations, 2020.
[46] Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:201 1, 13456, 2020.
[47] Linoy Tsaban and Apolinário Passos. Ledits: Real image editing with ddpm inversion and semantic guidance. arXiv preprint arXiv:2307.00522 2023.
[48] Narek Tumanyan, Michal Geyer, Shai Bagon, and Tali Dekel. Plug-and-play diffusion features for text-driven image-to-image translation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1921-1930, 2023.
[49] Bram Wallace, Akash Gokul, and Nikhil Naik. Edict: Exact diffusion inversion via coupled transformations. Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pages 22532-22544 2023.
13

---

[50] Haofan Wang, Matteo Spinelli, Qixun Wang, Xu Bai, Zekui Qin, and Anthony Chen. In- stantstyle: Free lunch towards style-preserving in text-to-image generation. arXiv preprint arXiv:2404.027735 2024.
[51] Jianyi Wang, Zongsheng Yue, Shangchen Zhou, Kelvin CK Chan, and Chen Change Loy. Exploiting diffusion prior for real-world image super-resolution. International Journal of Computer Vision, 132(11):5529-5944 2024.
[52] Zhizhong Wang. Lei Zhao, and Wei Xing. Stylediffusion: Controllable disentangled style transfer via diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7677-7689, 2023.
[53] Yuxiang Wei, Yabo Zhang, Zhilong Ji, Jinfeng Bai, Lei Zhang, and Wangmeng Zuo. Elite: Encoding visual concepts into textual embeddings for customized text-to-image generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15943-
15953, 2023.
[54] Rongyuan Wu, Lingchen Sun, Zhiyuan Ma, and Lei Zhang, One-step effective diffusion network for real-world image super-resolution. Advances in Neural Information Processing Systems, 37:92529 2024.
[55] Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. Imagereward: Learning and evaluating human preferences for text-to-image generation. Advances in Neural Information Processing Systems, 36: 36:15903-15935, 2023.
[56] Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models. arXiv preprint arXiv:2308, 06721, 2023.
[57] Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. Proceedings of the IEEF/CVF international conference on computer vision, pages 3836--877,2 2023.
[58] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unrea- sonable effectiveness of deep features as perceptual metric. In Proceedings of the IEEE conference computer vision and pattern recognition, pages 586-595, 2018.
[59] Yuechen Zhang, Jinbo Xing, Eric Lo, and Jiaya Jia. Real-world image variation by aligning diffusion inversion chain. Advances in Neural Information Processing Systems, 36:30641- 30661, 2023.
14

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
The abstract and introduction summarize the core contributions behind Dual- Conditional Inversion (DCI), the methodological innovation of incorporating both source prompt and image reference as dual conditions, and the demonstrated improvements both reconstruction and editing performance. They accurately reflect the paper's described scope and contributions.
Justification:
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. It is fine to include aspirational goals as motivation long it is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations the work performed by the authors?
Answer: [No]
Justification:
Due to page limit, we present this part in supplementary materials.
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g.. independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or speech-to-text system might not be used reliably provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairesss.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't acknowledged
the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
15

---

Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [Yes]
Justification: The cited literature and experimental results fully confirm our theoretical hypothesis and proof.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Question:
Answer: [Yes]
Justification:
We disclosed all of the main experimental results in the part of the experiments.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to model. In general. releasing code and data often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
specific model and empirical evaluation, may
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make it clear how reproduce that algorithm. (b) If the contribution is primarily
model architecture, the paper should describe the architecture clearly and fully.
(c) the contribution is a new model (c.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should possible for other researchers to have some path to reproducing verifying the results.
16

---

5. Open access to data and code
Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
Question:
Answer: [Yes]
Justification:
The data and code of our paper convenient for reproducing the experimental results.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://nips.com cc/
public//quide/CodeSuuiiii wtt.bbmmsssiooooccciilo.. for more details.
While we encourage the release of code and data, we understand that this might not be possible, "No" an acceptable answer. Papers cannot be rejected simply for including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurlPS code and data submission guidelines (https for more details.
//nips ctttlps.ccpuubicc//// guides/ http:///wwlll.....ioooo///aaaaaa
The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification:
We demonstrated all the training and test details.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [No]
Justification: Due to the resource limitation, we do not report error bars. Our experiments on models and hyperparameters are so numerous that they are impossible to repeat.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by emror bars, confi- dence intervals, or statistical significance tests, least for the experiments that support the main claims of the paper.
17

---

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
It should be clear whether the error bar is the standard deviation or the standard error of the mean.
is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CL, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that out of range (e.g. negative error rates).
If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification:
In the experimental section, we declare the setups.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs as well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary failed experiments that didn't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics
Answer:
[Yes]
Justification:
All experiments comply with ethical standards.
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No. they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., there a special consid- eration due to laws regulations in their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer:
[Yes]
Justification:
Our work already discusses the impacts of society.
Guidelines:
The answer NA means that there no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has no societal impact why the paper does not address societal impact.
18

---

Examples of negative societal impacts include potential malicious unintended uses (e.g., disinformation, generating fake profiles, surveillance), faireess considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms monitor how system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper deseribe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: Our work carries no risk.
Guidelines:
The answer NA means that the paper poses such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: Our work is based on public datasets and code.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset used and, possible, include URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g., website). the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms use in package should be provided. For popular datasets, paperswithcone com/ datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
19

---

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if has changed) should be provided.
If this information is not available online, the authors are encouraged to reach out to the asset's s creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification:
This paper does not release new assets.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/moddl as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create anonymized URL include anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?
Answer: [Yes]
Justification:
We have an human-preference experiment.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information in the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included in the main paper.
According the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based the requirements of your country or institution) were obtained?
Answer: [No]
Justification: We have no crowdsoureing research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurlPS Code of Ethics and the guidelines for their institution.
For initial submissions, not include any information that would break anonymity (if applicable), such the institution conducting the review.
20

---

16. Declaration of LLM usage
Question: 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, or formatting purposes and does not impact the core methodology. scientific rigorousness, originality of the research, declaration is not required.
Answer: [NA]
Justification: In this study, the LLM is not the target of this paper.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to L.LM policy http:pac/eaaarrsssec//////eeeeee for what should or should not be described.
21

---

In this supplementary file, we provide the following materials:
Proof of Dual-Conditional Inversion
Experimental Results on Different Datasets and Editing Engines
Inversion Time Comparison
Failure Cases
Limitations and Future Work
A Proof of Dual-Conditional Inversion
A.1 Stage-Wiss Wise Convergence Analysis
The Cnal-Conditional Inversion (DCI) algorithm operates in two alternating stages: (1) reference- guided noise correction and (2) fixed-pont poid-ppintttio latent refinement. Since these two stages target separate objective functions and update different variables (fraw and 2t, respectively), their convergence must be analyzed independently.
A.1.1
Stage 1: Reference-Guided Noise Correction
This stage minimizes the noise alignment loss:
Gra((Eraw) = |1āraw Geell2
(13)
via one-step gradient descent update:
Gaa
Leef (1 A)εraw AEref-
(14)
This is equivalent to a convex interpolation between énww and éref, and always satisfies:
Lia(3) < Lier(Eraw,,
E (0,
(15)
Hence, the correction step is guaranteed reduce noise misalignment in a single iteration, and can be seen as a contractive projection in noise space.
A.1.2 Stage Fixed-Point Latent Refinement
Given the corrected noise é, we update the latent 2, using the DDIM inversion formula:
=t C1,1 + C2.2
(16)
Then, refine by minimizing the self-consistency fixed-point loss: Lax (z)) [[f(2)) zell2, where fo(z)) G1,1 + co(2,,
(17)
We apply gradient descent:
(k+1) = -(()
(nx(22(1)) (K))
(18)
Assume:
c0(2) is L-Lipschitz smooth;
fo(2) is locally contractive near the solution;
Cyx is bounded below.
Then from smooth non-convex optimization theory:
Theorem A.1 (Local Convergence of Fixed-Point Refinement). The sequence (2(0)) satisfies:
2(Lxr(2) (0)) Lmin)
Vaae(z (k) )0
min osk<K
(19)
n
and converges to stationary point 2 as K + 20.
22

---

A.1.3 Alternating Convergence
In DCI, the noise correction step anchors the predicted noise toward semantically and visually meaningful reference, ensuring that the initialization for the latent refinement step falls within the contraction region of fz).. Thus, the two-stage alternating optimization benefits from mutual regularization:
Stage reduces semantic deviation from image-derived noise:
Stage 2 reduces structural inconsistency via fixed-point updates:
This design avoids the need to jointly optimize non-separable loss and enables fast convergence with high reconstruction fidelity.
A.1.4 Additional Analysis
Beyond convergence guarantees, analyze several critical properties of the two-stage optimization process to better understand the behavior of DCI in practice.
Stability of the Correction Step. The reference-guided noise correction step performs convex interpolation between érow and Eje,, ensuring that the corrected noise é remains within the convex hull of the input and the reference:
é Conv (érws Erer)
(20)
This guarantees bounded updates and avoids divergence, when faw contains large Moreover, by interpreting À as a soft trust coefficient, we can view the update as a controllable balance between prompt semantics and visual fidelity.
Propagation of Residual Noise Error. Let ő; ;= ē ef denote the residual noise error at timestep t with respect to the ideal generative noise ef. The DDIM update propagates this noise error linearly into the latent space:
24 27 + C2,2 8e,
(21)
where z; denotes the latent corresponding to ideal inversion. Hence, even if Cref is not minimized to zero, the resulting latent perturbation is bounded and scales linearly with ||,.], which DCI attempts to iteratively reduce via fixed-point refinement.
Editability Preservation. Unlike optimization methods that overly constrain 2, toward recon- struction, DCI balances reconstruction and generative semantics. The fixed-point loss Lix enforces consistency with the model's forward trajectory rather than a hard projection to a reconstruction target, which helps preserve the generative flexibility required for downstream editing. Formally, if fa(z) approximates forward generative trajectory, minimizing [[])(z)) zz!l ensures that z, lies semantically meaningful denoising path, rather than collapsing to static reconstruction point.
Numerical Robustness. Empirically, the fixed-point refinement converges within 3-10 iterations under moderate learning rate 1 E [10-4, 10-2), As V involves only first-order derivatives of the denoiser ea, the update is numerically stable under automatic differentiation and does not amplify high-frequency errors.
Impact of À and on Optimization Dynamics. DCI offers explicit knobs to trade off visual grounding (A) and convergence aggressiveness (1)) Large À may overfit the reference signal and degrade semantic consistency; large 7) may induce oscillation overshoot in latent updates. As shown the ablation (Table 2), default values and r = 0.001 yield stable equilibrium across editing tasks.
B Experimental Results on Different Datasets and Editing Engines
We first present additional results of our method the PIE-Bench benchmark. As shown in Figure 6 our approach clearly outperforms existing baselines. The circles highlight undesirable artifacts and imprecise edits introduced by other methods, while method achieves target edits with high
23

---

fidelity. These results demonstrate that our approach excels in terms of editing precision, artifact suppression, and background consistency.
We then evaluate our method on a broader dataset, specifically COC02017 [28 to assess general- ization to open-world scenarios. We employ our Dual-Conditional Inversion (DCT) for the inversion stage and adopt P2P [1] as the editing engine. The text prompts are generated by a large language model, with only the desired editing attribute manually modified. As shown in Figure 7 our method maintains strong editing performance even in diverse and unconstrained real-world contexts.
Finally, we examine the compatibility of our inversion method with alternative downstream editing engines. We use the popular Masactrl [4] framework as a representative case. As shown in Fig- u the results demonstrate that method performs robustly across different editing pipelines, highlighting its generalizability and adaptability.
C Inversion Time Comparison
Despite involving an iterative optimization procedure, our DCI method maintains competitive runtime performance. On single NVIDIA RTX 4090 GPU, a full DCT inversion-editing cycle takes only average 12.1 seconds per image, which is comparable to the baseline DDIM inversion method [45]. This efficiency is largely due to the lightweight design of our fixed-point refinement and the use of one-step noise correction per iteration. Compared other methods that involve intensive text
Table 6: Comparison of inversion times (in seconds) across different methods.
Inversion Method
Inversion Time (s)
DDIM
11.55
NTI
137.54
NPI
11.75
AIDI
87.21
NMG
16.71
DirectINV
19.94
ProxEdit
11.75
SPDInv
27.04
DCI(ours)
12.13
embedding optimization or complex auxiliary modules, such as Null-text inversion (NTI) 33
Negative prompt inversion (NPI) [32], AIDI
(37. Noise Map Guidance (NMG) [6], Direct Inversion
(DirectINV) 200, ProxEdit [4] and SPDInv 25
-oour method achieves a favorable balance between
quality and speed. Some of these baselines require additional optimization rounds or rely extra network branches. DCI requires only small number of optimization steps and converges quickly, while still avoiding heavy architectural changes, making it more practical for real-world deployment.
D Failure Cases
While DCI significantly improves inversion quality and editing controllability, it still exhibits limita- tions in certain scenarios. Since method is designed for an independent optimization method of inversion process without being aligned with downstream editing objectives. Its performance can be adversely affected by the characteristics and limitations of the editing engine itself. In particular, failure cases may arise when the editing model lacks sufficient semantic alignment or spatial precision, resulting in incomplete edits or distorted outputs.
As shown Figure one failure example occurs when the target edit conflicts with the original content. In this case, the edited image either fails to reflect the desired changes or introduces unwanted artifacts. Such outcomes highlight the dependency of DCI on the quality and specificity of downstream editing models.
24

---

spring field with trees and blue sky outumn...
womon with brown hoir
blue hair
lion in the sky with blue
a monkey wearing colorful goggles and a colorful scarf-+ man
background -+... red background
with sunflowers and pears baranas...
play with black and white the a big pink yarn ball.
blue and white bird sits on branch -... butterfly
bird standing en top rock leopard
Figure 5: Failure cases
E Limitations and Future Work
Limitations. While our proposed Qual-Conditional Inversion (DCI) framework demonstrates superior performance in diffusion- image editing, several limitations remain.
First, DCI introduces additional computational overhead due to its dual-stage optimization process, including reference-guided noise correction and fixed-point refinement. Although this leads to increased inference time compared purely rred-forward inversion methods, find the overhead to be acceptable in most practical applications, especially those prioritizing editing fidelity over real-time speed. Second, the effectiveness of DCI depends the quality and semantic alignment of both the source prompt and the reference image. In cases where the prompt is overly ambiguous or poorly aligned with the image content, the dual-conditioning mechanism may lead suboptimal inversion performance or conflicting guidance signals. Lastly, the extension of DCI to other modalities such as video, 3D scenes, or multi-view data remains unexplored.
Applicability Conditions
Editing with ambiguous weak prompts. The incorporation of reference images enables stable inversion when text prompts alone are insufficient for guiding precise edits.
Applications requiring high reconstruction fidelity. Tasks such identity preservation, localized image edits, or photo-realistic retouching benefit from the semantic and structural anchoring provided by DCI.
DDIM-based architectures. DCI is currently implemented and evaluated with DDIM. Compatibility with other samplers (e.g., DPM-Solver) may require re-derivation empirical verification.
We plan to explore several promising directions: (1) improving computational efficiency through adaptive early stopping or learned refinement modules: (2) extending DCI to higher- resolution pipelines and multi-modal inputs such as text-image-mast triplets; and (3) evaluating applicability of DCI in diverse generative backbones, including DiT and other transformer-based diffusion models. We also aim to conduct user studies in practical editing tools to assess robustness, usability, and real-world performance.
Future Work.
25

---

Source
DDIM
DirectInv
SPDInv
DCI
a cat sitting next to... +a tiger sitting next to...
bbooks and a cup - ...okss and a candle
...a cat is playing with...-> ...a dog is playing with...
boy ..., laughing happily _... boy ... crying sadly
white tiger on brown ground white cat on brown ground
...red trees in flat style ...ppink trees in a flat style
white bulldog is walking on... a white rat is walking on... Figure 6: More visual results PIE-Bench with P2P
26

---

A white vase holds colorful bouquet flowers a sunlit railing A grey...
white dog sleeps peacefully quiet street beside bicycle A yellow
smiling woman a swimsuit holds a pink umbrella by the lakeside sad...
black bear walks through dry grass and rocky terrain the wild- brown.
A zebra grazes green grass under the bright daylight in the open field- A horse
A playful cat bites a brown shoe while lying on green grass A playful dog
Figure 7: Visual results on COC02017 dataset
monkey wearing colorful goggles and colorful scarf-+ man
painting of rat with red eyes - pig
house the woods monster in the woods
squirrel sitting top of wooden fence- nabbit
a poster of a bus driving down road with mountains the background a poster a road
a brownish grey knitted bunny with three painted eggs-+ brownish grey knitted bunny
Figure 8: Visual results of DCI with Masactrl
27


Reviews:
[{"review_id": "WJk9jyzGof", "reviewer": "Reviewer_1usR", "summary": "This paper addresses the problem of inversion in text-to-image (T2I) diffusion models, conditioned on both a text prompt and a reference image. The proposed framework, Dual-Conditional Inversion (DCI), employs an iterative refinement strategy that alternately minimizes the latent noise gap and the reconstruction error. Experimental results demonstrate improved reconstruction quality and enhanced fidelity in downstream editing tasks compared to several existing inversion methods.", "questions": "In addition to the weaknesses I have mentioned above, I have several other questions.\n\n- Q1: How sensitive is the proposed method to the number of diffusion steps? As noted in the paper, DDIM inversion quality is known to improve with more denoising steps. Since the proposed method fixes the number of steps to 50 and relies on DDIM dynamics during the Fixed-Point Latent Refinement stage, it would be helpful to understand how performance varies with different step settings.\n\n- Q2: What is the computational cost of the proposed method, and how does it compare to existing approaches? Some quantitative comparisons in terms of runtime or FLOPs would help evaluate the method’s practical applicability.", "limitations": "The current main manuscript lacks an explicit discussion of its limitations and broader impact, which is strongly recommended for inclusion.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "The paper presents both strengths and weaknesses.\n\nWhile the idea of iterative refinement based on both the text prompt and the reference image is intuitive and well-motivated and the methodological section clearly conveys the core technical design, there are several concerns.\n\n**W1:** Within the line of diffusion inversion tasks, there appears to be an intuitive and potentially stronger alternative objective that has not been discussed: recovering the initial noise given both the text prompt and reference image, such as [a]. The authors should clarify the relationship between their method and prior works following this direction. From the reviewer's perspective, noise recovery represents a more direct and theoretically grounded goal for inversion problems in this context, compared to solely the reconstruction performance.\n\n**W2:** The experimental setup is somewhat limited in terms of base models, datasets, and editing methods. The current study uses only Stable Diffusion v1.4 as the backbone, one editing engine (P2P), and evaluates mainly on a single benchmark (PIE-Bench). Broader validation would strengthen the claims.\n\n**W3:** Additional evaluation of general image quality would help reinforce the work. The current evaluation lacks comprehensive quality assessment, metrics such as ImageReward or PickScore could provide a more complete picture.\n\n**W4:** Regarding the claim of improved editing fidelity, the manuscript lacks sufficient empirical justification. While qualitative examples are presented in Figure 3, more rigorous and quantitative evaluation of this downstream editing task is needed to substantiate the claim.\n\nMinor issues:\n\n- There are recurring formatting issues, such as missing spaces before parentheses.\n\n- Inconsistent figure and table references, e.g., Line 266 refers to “Figure fig.3,” which is redundant.\n\n---\n[a] On Exact Inversion of DPM-Solvers, CVPR 2024", "was_revised": true, "final_justification": "Taking the strengths and weaknesses into account, I am willing to raise my score to 4. However, this is a somewhat cautious recommendation, as I believe the paper would still require significant revisions to reach a fully “ready-to-be-accepted” state.", "rebuttal": "We thank the reviewer for the valuable review. We address your concerns and questions below:\n\n**W1：Within the line of diffusion inversion tasks, there appears to be an intuitive and potentially stronger alternative objective that has not been discussed: recovering the initial noise given both the text prompt and reference image, such as [a]. The authors should clarify the relationship between their method and prior works following this direction. From the reviewer's perspective, noise recovery represents a more direct and theoretically grounded goal for inversion problems in this context, compared to solely the reconstruction performance.**\n\n**A.** We would like to clarify that the fundamental distinctions between our method and other inversion methods like [1]. The core difference lies in **whether or not using text conditions as guidance during the inversion process**. While [1] overlooks text guidance, our approach explicitly integrates it. We posit that an ideal generation and inversion process should be perfectly reversible.(meaning the latents at the same timestep are identical whether in generation or inversion process) To achieve this, we employ fixed-point iteration with **explicit text guidance and image reference**. It's true that [1] raises concerns about the potential non-convergence of fixed-point iteration, leading them to abandon it. However, we have theoretically **demonstrated the convergence of fixed-point iteration under our specific conditions**, as detailed in **Section A.1.2** of our supplementary materials. Furthermore, since using only text guidance can lead to deviations in the generation process, we employ reference image correction. This theoretical and methodological improvement represents our advancement.\n\nFor noise recovery quality, **Figure 4** in our main paper(specific experimental settings detailed in **Section 4.3**) shows an experiment of noise restoration quality. The results in this figure demonstrate that our method **achieves closer noise gap** than other methods.\n\n**W2: The experimental setup is somewhat limited in terms of base models, datasets, and editing methods. The current study uses only Stable Diffusion v1.4 as the backbone, one editing engine (P2P), and evaluates mainly on a single benchmark (PIE-Bench). Broader validation would strengthen the claims.**\n\n**A.** We use Diffusion v1.4 and P2P **for a fair comparison** with other methods. Since Stable Diffusion V1.5, 2.1 and XL are structurally similar to V1.4, our method can be directly applied to these base models. Furthermore, we present experimental results on the COCO-2017 dataset using the Masactrl editing engine in the **Appendix.** We also provide results for the **flow-based diffusion method**. Due to the inability to present images, we use quantitive metrics instead. We use **Stable-Flow** [2] as the editing engine. The results show that our method is also effective when applied to flow-based models.\n\n|     | DINO ↓ | PSNR ↑ | LPIPS ↓ | MSE ↓ | SSIM ↑ | CLIP ↑ |\n| --- | --- | --- | --- | --- | --- | --- |\n| Ours | **0.0044** | **24.323** | **0.0684** | **0.0037** | **0.9275** | **23.64** |\n| default | 0.0190 | 24.304 | 0.0917 | 0.0037 | 0.9160 | 23.29 |\n\nWe also use **LEDIT++[3] as the multi-agent editing engine** for testing. The MSE reported in the paper is calculated for the non-edited regions (due to the availability of appropriate mask annotations in the dataset). However, for multi-subjects editing, we can only calculate the MSE between the edited image and the entire original image. Under these conditions, this metric does not reflect the quality of editing very objectively. The other metrics we report are better than DDIM.\n\n|     | DINO ↓ | PSNR ↑ | LPIPS ↓ | MSE ↓ | SSIM ↑ | CLIP ↑ |\n| --- | --- | --- | --- | --- | --- | --- |\n| Ours | **0.0121** | **21.19** | **0.1275** | 0.0125 | **0.8441** | **21.62** |\n| DDIM | 0.0212 | 21.1785 | 0.1363 | 0.0076 | 0.8395 | 19.01 |\n\n**W3: Additional evaluation of general image quality would help reinforce the work. The current evaluation lacks comprehensive quality assessment, metrics such as ImageReward or PickScore could provide a more complete picture.**\n\n**A.** We add experiments on **human preferences metrics** and **user studiy**, and the results demonstrate that our approach achieves significant advantages. For the user study, we collected 40 comparisons from 25 participants (aged 19 to 50). The table shows the mean scores for each participant (min: 1, max: 5). For human preferences metrics, higher metrics represent better performance.\n\n|     | DDIM | SPDInv | DCI |\n| --- | --- | --- | --- |\n| Pickscore[4] | 0.4416 | 0.4954 | **0.5547** |\n| Image-Reward[5] | -0.0120 | 0.1564 | **0.3674** |\n| User | 2.14 | 3.48 | **4.10** |\n\n**W4: Regarding the claim of improved editing fidelity, the manuscript lacks sufficient empirical justification. While qualitative examples are presented in Figure 3, more rigorous and quantitative evaluation of this downstream editing task is needed to substantiate the claim.**\n\n**A.** We show quantitative metrics such as DINO, PSNR, LPIPS, MSE, SSIM, and CLIP in Table 1. Some human preferences and user study results are also shown above. For other image editing methods based on diffusion models, our **quantitative metrics largely reflect the effectiveness of our approach.**\n\n**Minor issues ：**\n\n**A.** Thank you for your suggestion, we will carefully review and revise the paper.\n\n**Q1: How sensitive is the proposed method to the number of diffusion steps? As noted in the paper, DDIM inversion quality is known to improve with more denoising steps. Since the proposed method fixes the number of steps to 50 and relies on DDIM dynamics during the Fixed-Point Latent Refinement stage, it would be helpful to understand how performance varies with different step settings.**\n\n**A.** We test the results with different inversion steps settings.\n\n| Step | DINO×10³↓ | PSNR ↑ | LPIPS×10³↓ | MSE×10⁴↓ | SSIM×10²↑ | CLIP ↑ |\n| --- | --- | --- | --- | --- | --- | --- |\n| 10  | 6.87 | 27.81 | 37.12 | 22.82 | 85.97 | 24.9732 |\n| 20  | 5.72 | 28.92 | 34.85 | 21.55 | 86.67 | 25.3197 |\n| 50  | 6.07 | 29.38 | 33.01 | 21.28 | 87.14 | 25.52 |\n| 100 | 7.81 | 29.48 | 37.61 | 23.14 | 86.66 | 25.8322 |\n| 200 | 10.35 | 28.81 | 43.40 | 24.61 | 85.97 | 26.5868 |\n| 500 | 16.06 | 28.37 | 54.81 | 28.07 | 84.68 | 27.3474 |\n\nThe table shows that although inversion quality improves with an increase in denoising steps, the overall image editing performance is, to some extent, **a trade-off between inversion quality and the specific editing method used**. Comparatively, our method consistently surpasses the original DDIM approach at all tested step counts, clearly demonstrating its effectiveness.\n\n**Specifically:**\n**At lower step counts** (10 and 20 steps), the editing performance is primarily determined by the initial inversion quality. **When the step count reaches 50**, our method achieves its best performance in LPIPS and SSIM, indicating that it can produce high-quality editing results even at this moderate number of steps. **As the steps further increase to 100, 200, and even 500**, the CLIP score, which measures text-image semantic consistency, shows better results. This suggests that with a sufficiently high number of steps, and thus more refined editing, the semantic alignment improves. However, most other metrics (like MSE, SSIM, DINO, LPIPS) might show a performance decrease. This could be because these metrics primarily compare the edited image to the original. When editing is highly effective and fine-grained, it naturally introduces significant differences from the original image, which these metrics penalize.\n\nDespite the common belief that inversion quality generally improves with more steps, our results reveal an interesting phenomenon in image editing: **for our method, 50 steps appears to be the sweet spot**. It achieves an excellent balance between image fidelity and semantic consistency. This highlights that our fixed-point latent optimization effectively enhances the inversion process, yielding high-quality results with a relatively modest number of steps.\n\n**Q2: What is the computational cost of the proposed method, and how does it compare to existing approaches? Some quantitative comparisons in terms of runtime or FLOPs would help evaluate the method’s practical applicability.**\n\n**A.** Our time computation comparision is shown **in Table 1 in the supplementary material**. On average, our method is only about **0.58 seconds slower than DDIM**, representing less than **50% of SPD's computational cost.**\n\n[1] On Exact Inversion of DPM-Solvers, CVPR 2024\n\n[2] Stable flow: Vital layers for training-free image editing, CVPR 2025\n\n[3] Ledits++: Limitless image editing using text to image models, CVPR 2024\n\n[4] Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation, NeurIPS 2023\n\n[5] ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation, NeurIPS 2023"}, {"review_id": "5iT9jewT1S", "reviewer": "Reviewer_VjZz", "summary": "This paper proposes an optimization-based inversion method for real image editing with diffusion models, aiming to balance image reconstruction accuracy and editing flexibility. The authors name this approach Dual-Conditional Inversion (DCI), which utilizes conditions on both the source prompt and reference image to guide the inversion process for diffusion-based image editing. According to the quantitative and qualitative experiments provided by the authors, this method demonstrates advanced capabilities in both reconstruction and editing.", "questions": "1. In the paper, the source prompt is used to make the method compatible with P2P editing. However, during the reconstruction process, I think a complete reconstruction can be accomplished even if the source prompt is empty, and editing can be performed using other editing methods that do not rely on the source prompt. In this case, is conditioning on the source prompt really necessary?\n2. The two-stage optimization process could be time-consuming, but according to your reported results, it is only 0.58 seconds slower than DDIM inversion. Is the runtime related to any hyperparameters? Additionally, does using different source prompts for the same image result in different computation times?\n3. As mentioned in the weaknesses, how much influence do different source prompts written by different users have on the editing results? In addition, providing more editing examples, especially complete results on the PIE dataset, would help to more intuitively evaluate the effectiveness of your method, but these are not included in the supplementary material.\n4. Is the ‘E’ in Equation 5 trained by the authors themselves? It appears that $z_{0}$ is already the latent obtained through VAE encoding of the image. Is the pretrained VAE used in ‘E’ the same as the one used to convert the image into the latent?", "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": "Strengths:\n1. This work brings a new optimization perspective for image editing methods. The approach of optimizing the image latent with both the source prompt and the reference image is also intuitive.\n2. The examples and experimental results in the paper show that the proposed method outperforms other inversion methods in both image reconstruction and editing performance, while also exhibiting some advantages in inversion time.\n3. The paper is well-structured and written clearly and concisely.\n\nWeaknesses:\n1. The method requires a source prompt. However, in practice, real images rarely have corresponding source prompts, and different users creating source prompts may impact the stability and performance of editing results.\n2. Although the authors provide quantitative experiments with multiple evaluation metrics, the ultimate quality of editing results is best verified by human evaluation, even though this may be resource-intensive. \n3. the paper lacks a diverse set of editing examples for better gauging the effectiveness of the method. Most examples provided can be edited well by mainstream methods; including more discriminative editing examples would better highlight the value of this work.", "was_revised": true, "final_justification": "Thanks to the authors for their rebuttals, which answered my questions. After reading the rebuttals and other reviewers' comments, I maintain my original rating of 4.", "rebuttal": "Thank you for your detailed feedback and the encourging comments. Please see the detailed response below.\n\n**W2: Although the authors provide quantitative experiments with multiple evaluation metrics, the ultimate quality of editing results is best verified by human evaluation, even though this may be resource-intensive.**\n\n**A.** We add experiments on **human preferences metrics** and **user study**, and the results demonstrate that our approach achieves significant advantages.\n\n|     | DDIM | SPDInv | DCI |\n| --- | --- | --- | --- |\n| Pickscore[1] | 0.4416 | 0.4954 | **0.5547** |\n| Image-Reward[2] | -0.0120 | 0.1564 | **0.3674** |\n| User | 2.14 | 3.48 | **4.10** |\n\nFor the user study, we collected 40 comparisons from 25 participants (aged 19 to 50). The table shows the mean scores for each participant (min: 1, max: 5). For human preferences metrics, higher metrics represent better performance.\n\n**W3: The paper lacks a diverse set of editing examples for better gauging the effectiveness of the method. Most examples provided can be edited well by mainstream methods; including more discriminative editing examples would better highlight the value of this work.**\n\n**A.** We'd like to clarify that our method's primary aim is to significantly enhance downstream editing engines. This means not only improving the quality of edited images but also sometimes enabling editing capabilities that the original engines couldn't achieve on their own. We have proposed some image results in supplementary materials. Since we can't directly display images here, we've included additional quantitative experimental results here.\n\nWe use **LEDIT++** [3] as our multi-subjects editing model and apply it with both DDIM and our DCI method for fair comparision. In our paper, the reported Mean Squared Error (MSE) is calculated for non-edited regions, thanks to the availability of appropriate mask annotations within the dataset. However, for multi-subjects editing, we could only calculate the MSE between the edited image and the entire original image. Under these conditions, the MSE metric isn't always an accurate reflection of editing quality, as DDIM frequently fails to produce any changes or only generates very minor alterations. (In these cases, they even perform better due to minimal changes.)\n\n|     | DINO ↓ | PSNR ↑ | LPIPS ↓ | MSE ↓ | SSIM ↑ | CLIP ↑ |\n| --- | --- | --- | --- | --- | --- | --- |\n| Ours | **0.0121** | **21.19** | **0.1275** | 0.0125 | **0.8441** | **21.62** |\n| DDIM | 0.0212 | 21.1785 | 0.1363 | **0.0076** | 0.8395 | 19.01 |\n\nWe're pleased to report that when integrated with existing editing methods, our approach consistently leads to quantifiable improvements in both quantitative and perceptual metrics. This underscores its effectiveness in making these advanced editing tasks more robust and higher quality.\n\n**Q1: In the paper, the source prompt is used to make the method compatible with P2P editing. However, during the reconstruction process, I think a complete reconstruction can be accomplished even if the source prompt is empty, and editing can be performed using other editing methods that do not rely on the source prompt. In this case, is conditioning on the source prompt really necessary?**\n\n**A.** We want to clarify that our method **targets the inversion process itself**, **which is a fundamental component in almost all diffusion model-based image editing tasks, rather than being specific to a particular method like P2P**. In other words, our approach is **plug-and-play** and applicable to the inversion stage of any editing engine.\n\nFor instance, the original P2P method uses DDIM for inversion without any source prompt, performing edits during the generation process. In contrast, Table 1 clearly shows our method significantly outperforms DDIM when used as a substitute. This improvement isn't limited to P2P. Even for other editing methods that don't explicitly require a source prompt, our method can substantially boost results by replacing their default DDIM inversion. This holds true even if only a rough source prompt is available (e.g., user-provided or those generated by a language model).\n\nThis broad applicability means our research can widely improve the performance of downstream tasks. What's more, the additional time and computational costs are minimal compared to DDIM, and in some cases, our method even proves to be more efficient than other alternatives!\n\n**Q2: The two-stage optimization process could be time-consuming, but according to your reported results, it is only 0.58 seconds slower than DDIM inversion. Is the runtime related to any hyperparameters? Additionally, does using different source prompts for the same image result in different computation times?**\n\n**A.** You're correct: runtime is indeed tied to the **number of optimization iterations $K$ and the error threshold $\\tau$.** However, because our method primarily focuses on \"nudging\" the inversion process back onto the correct path, we've empirically found that often **only a few optimization steps at specific timesteps are needed** to achieve significant improvements. This means the additional time overhead compared to DDIM is quite minimal.\n\nWe've also added experiments to address the very question you raised. Our analysis of inversion times for a single image, using both short prompts (under 10 words) and long prompts (over 10 words), revealed that **longer prompts do slightly increase computation time.**\n\n|     | TIME |\n| --- | --- |\n| Long Prompt | 12.21s |\n| Short Prompt | 11.91s |\n\n**W1&Q3:**\n\n- **The method requires a source prompt. However, in practice, real images rarely have corresponding source prompts, and different users creating source prompts may impact the stability and performance of editing results.**\n  \n- **As mentioned in the weaknesses, how much influence do different source prompts written by different users have on the editing results? In addition, providing more editing examples, especially complete results on the PIE dataset, would help to more intuitively evaluate the effectiveness of your method, but these are not included in the supplementary material.**\n  \n\n**A.** We've conducted extensive testing on the editing effects of long and short source prompts. We find that **even providing a rough source prompt yields more positive results than DDIM**. For editing quality, we've observed that if both a long and a short prompt adequately describe the core features of an object. For instance, \"a round cake with orange frosting on a wooden plate\" versus \"a round cake with orange frosting\" , the resulting image quality doesn't change much. However, when the short prompt fails to clearly describe essential object characteristics, such as \"a round cake with orange frosting on a wooden plate\" compared to just \"a round cake,\" the difference in quality becomes significant, with the longer prompt yielding superior results.\n\nDue to the inability to display images directly here and the vast number of images in the PIE dataset, we can't provide all editing results. (However, we commit to releasing all results on an open-source platform for verification after the paper is accepted.) We've included more results in the supplementary materials, including those on the COCO-2017 dataset. It's worth noting that the metrics reported in our paper are evaluated **across the entire datase**t, which further attests to our method's overall effectiveness.\n\n**Q4: Is the ‘E’ in Equation 5 trained by the authors themselves? It appears that  is already the latent obtained through VAE encoding of the image. Is the pretrained VAE used in ‘E’ the same as the one used to convert the image into the latent?**\n\n**A.** You are correct: **E** is the pre-trained VAE, and we make no additional training on it. The pretrained VAE used in **E** the same as the one used to convert the image into the latent.\n\n[1] Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation, NeurIPS 2023\n\n[2] ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation, NeurIPS 2023\n\n[3] Ledits++: Limitless image editing using text to image models, CVPR 2024"}, {"review_id": "9fsEscicVV", "reviewer": "Reviewer_VkjL", "summary": "The paper introduces DCI, a training-free framework that conditions on both the source prompt and the reference image simultaneously. DCI proceeds in two stages: (1) reference-guided noise correction, which aligns the predicted noise with the reference noise, and (2) fixed-point latent refinement, which iteratively removes inversion errors. Experiments demonstrate that DCI outperforms previous methods, both qualitatively and quantitatively.", "questions": "- What is the actual novelty of this work? The overall framework closely resembles SPDInv, with reference-guided noise correction seemingly the only addition.\n\n- The claim of “encoding the source image and setting a noise anchor” is unclear. The encoded image is not noise, and each diffusion timestep has a different noise level, but the method seems to reuse a single anchor across all steps. This design choice requires clearer justification and explanation.\n\n- A more comprehensive quantitative comparison that includes additional editing engines is necessary.\n\n*Minor Suggestions*\n- Lines 158 and 159 have a typo “))”.", "limitations": "It is encouraged to include a discussion of the potential societal impacts. Since this work focuses on image editing, it carries the risk of misuse by malicious actors (e.g., generating manipulated or deceptive content), and it may also contribute to the displacement of creative professionals through automation.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**[Strengths]**\n- DCI outperforms prior inversion baselines on every reported metric and produces more faithful edits in the visual comparisons.\n- The paper is well-written and easy to follow.\n\n**[Weaknesses]**\n- The contribution is incremental. The overall approach closely resembles SPDInv.\n- The paper should better explain why reference-guided noise correction improves inversion performance.", "was_revised": true, "final_justification": "The authors’ response has resolved my concern, so I am raising my score accordingly.", "rebuttal": "We thank the reviewer for their thorough and constructive review. We address the concerns and questions below:\n\n**W1&Q1:**\n\n- **The contribution is incremental. The overall approach closely resembles SPDInv.**\n  \n- **What is the actual novelty of this work? The overall framework closely resembles SPDInv, with reference-guided noise correction seemingly the only addition.**\n  \n\n**A.** We want to clarify that our DCI method, along with prior works like SPDinv and the even earlier FPI [1], all share the fundamental goal of **fixed-point optimization.** However, where we diverge is in the specific strategies employed to locate and confirm this ideal fixed point.\n\nThe earliest methods rely on self-iterating the noise. However, they only ensure noise consistency, clearly didn't lead to an ideal noise or fixed point. SPDinv advances this by optimizing the denoising process by introducing source prompts as guidance during inversion process for better reconstruction. Yet, this perspective is also incomplete. As evidenced by some related works such as Comat[2] and Enhancing Semantic Fidelity in Text-to-Image Synthesis: Attention Regulation in Diffusion Models[3], relying solely on the original text prompt is often insufficient to achieve a reconstruction highly faithful to the original image. This, in essence, suggests that their optimization objective might not converge to the most ideal fixed point. Our method represents a **deeper exploration** of this problem. We employ fixed-point iteration with text guidance and image reference. We approach it from the perspective of reconstruction, combinating inversion and generation processes. As we state in our paper, incorporating information from the original image into the reconstruction task demonstrably yields better results. (Naturally, reconstruction inherently involves using the source prompt, leading to some similarities with SPDinv.) **In this context, by introducing more detailed information, the improved reconstruction quality directly signifies that the fixed point we've identified is more ideal.**\n\n**W2&Q2:**\n\n- **The paper should better explain why reference-guided noise correction improves inversion performance.**\n  \n- **The claim of “encoding the source image and setting a noise anchor” is unclear. The encoded image is not noise, and each diffusion timestep has a different noise level, but the method seems to reuse a single anchor across all steps. This design choice requires clearer justification and explanation.***\n  \n\n**A.** We'd like to start by highlighting a foundational aspect of our work: prior research, specifically \"Understanding DDPM Latent Codes Through Optimal Transport\" (NeurIPS 2022) [4] and \"Understanding the Latent Space of Diffusion Models through the Lens of Riemannian Geometry\" (NeurIPS 2023) [5], have established that the latents obtained after encoding **share similar properties** with the original image or noise space. This insight serves as a crucial theoretical underpinning for our approach.\n\nWe posit that an ideal generation and inversion process should be perfectly reversible, meaning the latents at the same timestep are identical whether derived from generation or inversion. Our method aims to introduce **information from the original image during the inversion process**. Since inversion operates within the latent space, we use the encoded original image as guidance. As mentioned previously, building on this, we explicitly consider the generation process during inversion, effectively integrating **both generation and inversion processes**. This integration has often been overlooked in prior work.\n\nOur guidance is applied within the iterative \"generation\" process. Ideally, the image produced by the generation process should be identical to the encoded image. Therefore, we use the encoded original image as an anchor for noise correction. As we prove in Section **A.1.1** of our supplementary materials and the experimental results in **Section 4.3**, our method demonstrably **reduces the gap to ideal noise**, thereby confirming that our reference-guided noise correction significantly enhances inversion performance.\n\n**Q3: A more comprehensive quantitative comparison that includes additional editing engines is necessary.**\n\n**A.** We've further demonstrated the versatility and effectiveness of our method by integrating it with various image editing engines. Our results on editing engines such as masactrl are provided in **Section B** of the Supplementary Materials.\n\nAs we cannot display images directly, we provide **quantitative results for editing in more complex scenarios**. We use **LEDIT++** [6] as our multi-subjects editing model and apply it with both DDIM and our DCI method for fair comparision. In our paper, the reported Mean Squared Error (MSE) is calculated for non-edited regions, thanks to the availability of appropriate mask annotations within the dataset. However, for multi-subjects editing, we could only calculate the MSE between the edited image and the entire original image. Under these conditions, the MSE metric isn't always an accurate reflection of editing quality, as DDIM frequently fails to produce any changes or only generates very minor alterations. (In these cases, they even perform better due to minimal changes.)\n\nDespite this, all other metrics we report show our method outperforming DDIM, demonstrating its continued effectiveness when applied to complex scenarios like multi-subjects editing.\n\n|     | DINO ↓ | PSNR ↑ | LPIPS ↓ | MSE ↓ | SSIM ↑ | CLIP ↑ |\n| --- | --- | --- | --- | --- | --- | --- |\n| Ours | **0.0121** | **21.19** | **0.1275** | 0.0125 | **0.8441** | **21.62** |\n| DDIM | 0.0212 | 21.1785 | 0.1363 | **0.0076** | 0.8395 | 19.01 |\n\nBeyond multi-subject editing, we also test our method with Stable-Flow, a **flow-based diffusion image editing method**. The experimental results clearly indicate that our approach significantly enhances performance in flow-based methods as well.\n\n|     | DINO ↓ | PSNR ↑ | LPIPS ↓ | MSE ↓ | SSIM ↑ | CLIP ↑ |\n| --- | --- | --- | --- | --- | --- | --- |\n| Ours | **0.0044** | **24.323** | **0.0684** | **0.0037** | **0.9275** | **23.64** |\n| default | 0.0190 | 24.304 | 0.0917 | **0.0037** | 0.9160 | 23.29 |\n\nIn a word, our method **enhances downstream tasks** rather than competing with them. Our approach consistently yields **improved results across nearly all image editing methods that incorporate an inversion process**, thereby demonstrating its broad effectiveness.\n\n**Minor Suggestions&Limitation：**\n\n**A.** We'll carefully review and revise our paper. We're very grateful for your suggestion and will add a discussion about the potential societal impacts of our work. We believe this will be a positive step for the long-term application of this technology.\n\n[1] Effective real image editing with accelerated iterative diffusion inversion, ICCV2023\n\n[2] Comat: Aligning text-to-image diffusion model with image-to-text concept matching, NeurIPS 2024\n\n[3] Enhancing semantic fidelity in text-to-image synthesis: Attention regulation in diffusion models, ECCV24\n\n[4] Understanding DDPM Latent Codes Through Optimal Transport, NeurIPS 2022\n\n[5] Understanding the Latent Space of Diffusion Models through the Lens of Riemannian Geometry, NeurIPS 2023\n\n[6] Ledits++: Limitless image editing using text to image models, CVPR 2024"}, {"review_id": "Bop8Tqa7BS", "reviewer": "Reviewer_ZiyA", "summary": "This paper discusses a new method called Dual-Conditional Inversion (DCI) for improving image generation and editing using diffusion models. Diffusion models convert images into a noise representation for tasks like reconstruction and editing. However, existing methods often struggle to balance accuracy and flexibility. DCI addresses this by using both a source prompt and a reference image to guide the process, leading to more accurate and editable results. Experiments show DCI performs better than other methods in various editing tasks and also excels in reconstruction, demonstrating robustness and generalizability.", "questions": "In a word, I think the DCI method itself is relatively solid. However, the impact on the entire community is very limited.\\\nClarify the advantages of the DCI method for the performance of those data-driven image editing methods in specific scenarios or the synergy when applying DCI to other editing model instead of the foundation t2i model, as well as applying DCI to flow-based (ODE) will solve the above consideration.", "limitations": "The authors list the limitation in Appendix. I suggest testing DCI in more scenarios is also needs to be implemented in this version to make it meet the requirements and influence of a NeurIPS25 paper instead of living it in the future.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "### Strength \n1.The method DCI demonstrates enhancement in both reconstruction and editing tasks, which is supported by experimental results across multiple metrics.\\\n2.The pseudocode for DCI in the paper is given very clearly and easy to understand.\n### Weakness\n1.What worries me the most is the influence of this work on the editing community. Due to the success of GPT-4o-Image and Flux-kontext, as well as their strong generalization and unity, the research interests of most communities have shifted to the data-driven approach. Although DCI is a training-free method, since the paper does not compare it with the data-driven method, the advantages of DCI except that it does not require training are unclear.\\\n2.The qualitative experiments provided by the author were all single-subject editing and did not illustrate the effect of DCI in more complex scenarios, such as multi-subjects editing.\\\n3. Currently, the flow-based Diffusion model is gradually replacing the denoised one. The new foundation t2i diffusion models are all flow-based like SD3 and FLUX. However, the effect of DCI on flow-based models has not been tested in this paper, which limits the applicability of DCI.", "was_revised": true, "final_justification": "In the rebuttal and further discussion part, most of my concerns have been addressed, so I decided to raise the score to 4.", "rebuttal": "We would like to greatly thank the reviewer for involving in the discussion and providing us with some important feedback. Below, we further address your concerns:\n\n**W1: What worries me the most is the influence of this work on the editing community. Due to the success of GPT-4o-Image and Flux-kontext, as well as their strong generalization and unity, the research interests of most communities have shifted to the data-driven approach. Although DCI is a training-free method, since the paper does not compare it with the data-driven method, the advantages of DCI except that it does not require training are unclear.**\n\n**A.** The key point to clarify upfront is that our method enhances downstream tasks rather than competing with them. While downstream editing techniques are increasingly powerful, demonstrating strong generalization and coherence, they still fall short in some scenarios. We believe this limitation is caused by suboptimal noise levels during the upstream inversion process.\n\nThe experimental results presented in our paper are primarily obtained using **Stable Diffusion V1.4**. However, our findings can be extended to models with similar architectures, such as **V1.5, V2.1, and so on**. The results consistently show that our method significantly boosts the performance of downstream tasks. We've observed numerous instances where the original approach failed, but incorporating our method led to successful outcomes. For data-driven methods, incorporating our DCI method also improves performance. This underscores the critical importance of upstream inversion, which is our core strength: improving the capabilities of all downstream tasks.\n\nTo further substantiate this claim, we've included additional experiments. As we can not display images directly, we provide tables detailing both **user study** and **human preferences metrics** to demonstrate the effectiveness of our approach.\n\n|     | DDIM | SPDInv | DCI |\n| --- | --- | --- | --- |\n| Pickscore[1] | 0.4416 | 0.4954 | **0.5547** |\n| Image-Reward[2] | -0.0120 | 0.1564 | **0.3674** |\n| User | 2.14 | 3.48 | **4.10** |\n\nFor the user study, we collect 40 comparisons from 25 participants (aged 19 to 50). The table shows the mean scores for each participant (min: 1, max: 5). For human preferences metrics, higher metrics represent better performance.\n\n**W2: The qualitative experiments provided by the author were all single-subject editing and did not illustrate the effect of DCI in more complex scenarios, such as multi-subjects editing.**\n\n**A.** As we cannot display images directly, we provide **quantitative results for editing in more complex scenarios**. We use **LEDIT++** [3] as our multi-subjects editing model and apply it with both DDIM and our DCI method for fair comparision. In our paper, the reported Mean Squared Error (MSE) is calculated for non-edited regions, thanks to the availability of appropriate mask annotations within the dataset. However, for multi-subjects editing, we could only calculate the MSE between the edited image and the entire original image. Under these conditions, the MSE metric isn't always an accurate reflection of editing quality, as DDIM frequently fails to produce any changes or only generates very minor alterations. (In these cases, they even perform better due to minimal changes.)\n\nDespite this, all other metrics we report show our method outperforming DDIM, demonstrating its continued effectiveness when applied to complex scenarios like multi-subjects editing.\n\n|     | DINO ↓ | PSNR ↑ | LPIPS ↓ | MSE ↓ | SSIM ↑ | CLIP ↑ |\n| --- | --- | --- | --- | --- | --- | --- |\n| Ours | **0.0121** | **21.19** | **0.1275** | 0.0125 | **0.8441** | **21.62** |\n| DDIM | 0.0212 | 21.1785 | 0.1363 | **0.0076** | 0.8395 | 19.01 |\n\n**W3&Q: Currently, the flow-based Diffusion model is gradually replacing the denoised one. The new foundation t2i diffusion models are all flow-based like SD3 and FLUX. However, the effect of DCI on flow-based models has not been tested in this paper, which limits the applicability of DCI.**\n\n**Clarify the advantages of the DCI method for the performance of those data-driven image editing methods in specific scenarios or the synergy when applying DCI to other editing model instead of the foundation t2i model, as well as applying DCI to flow-based (ODE) will solve the above consideration.**\n\n**A.** Thanks for your constructive feedback. To show our method works for flow-based diffusion models, we add **a proof** and **experimental results** applying our DCI method to a flow-based model as follow.\n\n---\n\n## Derivation and Proof of DCI Fixed-Point Loss in Flow-based Diffusion Models\n\n### Core Variable Definitions\n\n- $z_t$: Latent variable at time $t$ in a Flow model (corresponds to DCI's latent noise).\n- $v_t(z_t)$: Velocity field of the Flow model (describes the evolution direction of $z_t$ over time).\n- $f_\\theta(z_t)$: Latent update function of the Flow model (defined by the velocity field).\n- $\\psi_t$: Flow transformation of the Flow model (maps initial noise to the latent at time $t$).\n- $a_t, b_t$: Affine coefficients of the Flow model (control the linear evolution of the flow transformation).\n- $z_0$: Ideal latent of the reference image ($z_0 = E(x_0)$, where $E$ is the encoder).\n- $z_1$: Initial noise variable.\n\n### Evolution of $z_t$ and Update Function in Flow-based Diffusion\n\nIn a Flow model, the latent at time $t$ is generated by the flow transformation:\n\n$z_t = \\psi_t(z_1) = a_t \\cdot z_0 + b_t \\cdot z_1$\n\nThe velocity field $v_t(z_t)$ describes the instantaneous rate of change of $z_t$:\n\n$v_t(z_t) = \\frac{dz_t}{dt} = \\dot{a}_t \\cdot z_0 + \\dot{b}_t \\cdot z_1$\n\nWhere $\\dot{a}_t = \\frac{da_t}{dt}$ and $\\dot{b}_t = \\frac{db_t}{dt}$ are the time derivatives of the coefficients.\n\nThe update function of $z_t$ in a Flow model is defined by ODE Euler discretization:\n\n$f_\\theta(z_t) = z_t + v_t(z_t) \\cdot dt$\n\n($dt$ is the time step; $dt > 0$ for forward diffusion, $dt < 0$ for inversion).\n\n### Definition of $\\mathcal{L}_{fix}$ in Flow-based Models\n\nIn Flow models, the **fixed-point loss**  $\\mathcal{L}_{fix}$ constrains $z_t$ to satisfy the self-consistency of the flow model dynamics:\n\n$L_{fix} = || f_ \\theta(z_t) - z_t ||_2^2 = || v_t(z_t) * dt ||_2^2$\n\n**Physical Meaning:** This minimizes the change in the latent due to the velocity field, ensuring that $z_t$ stays on the trajectory of the flow model.\n\n### Proof of Convergence: $z_t$ Converges to a Fixed Point\n\n#### 1. Convexity of the Objective Function\n\nFrom equations (1) and (2), $v_t(z_t)$ can be rewritten as a linear function of $z_t$:\n\n$v_t(z_t) = \\frac{\\dot{a}_t}{a_t} \\cdot z_t + \\left( \\dot{b}_t - \\frac{\\dot{a}_t \\cdot b_t}{a_t} \\right) \\cdot z_1$\n\nTherefore, $\\mathcal{L}_{fix} = \\| v_t(z_t) \\cdot dt \\|_2^2$ is a quadratic function with respect to $z_t$, possessing convexity and a unique minimum point.\n\n#### 2. Gradient Descent Update\n\nTo minimize $\\mathcal{L}_{fix}$, we perform gradient descent on $z_t$:\n\n$z_t^{\\text{new}} = z_t - \\eta \\cdot \\nabla_{z_t} \\mathcal{L}_{fix}$\n\nGradient calculation:\n\n$\\nabla_{z_t} \\mathcal{L}_{fix} = 2 \\cdot dt^2 \\cdot v_t(z_t) \\cdot \\frac{\\partial v_t(z_t)}{\\partial z_t}$\n\nWhere:\n\n$\\frac{\\partial v_t(z_t)}{\\partial z_t} = \\frac{\\dot{a}_t}{a_t}$\n\nThe update process is equivalent to:\n\n$z_t^{\\text{new}} = z_t - \\eta' \\cdot v_t(z_t)$\n\nWhere $\\eta' = 2 \\eta \\cdot dt^2 \\cdot \\frac{\\dot{a}_t}{a_t}$ is the effective step size.\n\n#### 3. Convergence to the Fixed Point\n\nWhen $\\mathcal{L}_{fix} = 0$,\n\n $v_t(z_t^*) = 0$,  at which point the gradient is 0 and updates cease. Since $\\mathcal{L}_{fix}$ is a convex function, gradient descent will necessarily converge to:\n\n$z_t^* = f_\\theta(z_t^*)$\n\nBecause: \n\n$f_\\theta(z_t^* ) = z_t^* + v_t(z_t^* ) * dt = z_t^*$\n\nWe also test our approach on **Stable-Flow**[4], a flow-based diffusion image editing method. The results show that **our method is also effective when applied to flow-based models.**\n\n|     | DINO ↓ | PSNR ↑ | LPIPS ↓ | MSE ↓ | SSIM ↑ | CLIP ↑ |\n| --- | --- | --- | --- | --- | --- | --- |\n| Ours | **0.0044** | **24.323** | **0.0684** | **0.0037** | **0.9275** | **23.64** |\n| default | 0.0190 | 24.304 | 0.0917 | **0.0037** | 0.9160 | 23.29 |\n\n---\n\nAs we state earlier, our method **enhances downstream tasks** rather than competing with them. Our approach consistently yields **improved results across nearly all image editing methods that incorporate an inversion process**, thereby demonstrating its broad effectiveness.\n\n**Limitations: The authors list the limitation in Appendix. I suggest testing DCI in more scenarios is also needs to be implemented in this version to make it meet the requirements and influence of a NeurIPS25 paper instead of living it in the future.**\n\n**A.** In the supplementary materials, we present experimental results from **various editing engines** and across **different datasets**. We've also included **human preference model evaluations and a user study**, with results consistently demonstrating the **significant advantages of our method**.\n\nAs shown above, we've incorporated experiments on **multi-subject scenarios** and tested our method across **various editing engines**, as well as its application to **flow-based diffusion models**. Additionally, we've evaluated our approach using **human preference metrics and conducted a user study**. We will integrate these comprehensive results into the final manuscript to enhance its quality and impact.\n\n[1] Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generation, NeurIPS 2023\n\n[2] ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation, NeurIPS 2023\n\n[3] Ledits++: Limitless image editing using text to image models, CVPR 2024\n\n[4] Stable flow: Vital layers for training-free image editing, CVPR 2025"}]

Author rebuttal:

Meta-review: This paper tackles the inversion problem in text-to-image diffusion models and introduces DCI (Dual-Conditional Inversion) to guide the inversion process jointly conditioned on the source prompt and the reference image. Under the guidance, DCI minimizes the latent noise gap and the reconstruction error by an iterative refinement strategy.

Initially, this paper received mixed reviews. Some major concerns raised by reviewers include: 1. The experimental setup is somewhat limited in several aspects. 2. Evaluations on image quality, editing fidelity, and human evaluation are insufficient. 3. The contribution is incremental. 4. Some requirements, design choices, claims, and relations with prior works require clearer justification and explanation. 5. Factors affecting the running time are not clear. The rebuttal addressed most of these concerns, especially, additional experiments demonstrate the generalization ability of the proposed method across different base models and editing engines. Finally, all reviewers recommended borderline accept. Given the merits identified by reviewers, the AC follows this unanimous recommendation. Reviewers did raise valuable concerns and the authors are encouraged to make necessary changes in the camera-ready version.
Decision: Accept (poster)