UniEdit-Flow: Unleashing Inversion and Editing in the Era of Flow Models
Authors: Guanlong Jiao
Biqing Huang
Kuan-Chieh Jackson Wang
Renjie Liao
Venue: ICLR 2026 Poster (2026)
Keywords: Image Inversion
Image Editing
Rectified Flow Models
Iterative Generation Models
Diffusion Models
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Flow matching models have emerged as a strong alternative to diffusion models, but existing inversion and editing methods designed for diffusion are often ineffective or inapplicable to them. The straight-line, non-crossing trajectories of flow models pose challenges for diffusion-based approaches but also open avenues for novel solutions. In this paper, we introduce a predictor-corrector-based framework for inversion and editing in flow models. First, we propose Uni-Inv, an effective inversion method designed for accurate reconstruction. Building on this, we extend the concept of delayed injection to flow models and introduce Uni-Edit, a region-aware, robust image editing approach. Our methodology is tuning-free, model-agnostic, efficient, and effective, enabling diverse edits while ensuring strong preservation of edit-irrelevant regions. Extensive experiments across various generative models demonstrate the superiority and generalizability of Uni-Inv and Uni-Edit, even under low-cost settings.

Published as a conference paper at ICLR 2026
UNIEDIT-FLOW: UNLEASHING INVERSION AND EDIT- ING IN THE ERA OF FLOW MODELS
Guanlong Jiao Biqing Huang", Kuan-Chich Wang', Renjie Liao
'The University of British Columbia TTsinghua University
SSnap Inc.
Vector Institute SCanada CIFAR Al Chair
(gjiac, riliso]8ece. salaaoo@cceeubc.comm abyetsinghua.edi
kejackssnnangg@@ail.ccom
ABSTRACT
Flow matching models have emerged as strong alternative to diffusion mod- els, but existing inversion and editing methods designed for diffusion are often ineffective inapplicable to them. The straight-line, non-crossing trajectories flow models pose challenges for diffusion based approaches but also open av- enues for novel solutions. In this paper, introduce predictor-correcte corrector--baedd framework for inversion and editing flow models. First, we propose Uni-Inv, effective inversion method designed for accurate reconstruction. Building on this, we extend the concept of delayed injection to flow models and introduce Uni- Edit, region-aware, robust image editing approach. Our methodology tuning- efficient, and effective, enabling diverse edits while ensuring strong preservation of odit-irrelevant regions. Extensive experiments across vari- ous generative models demonstrate the superiority and generalizability of Uni-Inv and Uni-Edit, even under low-cost settings. Project page.
free,
Diverse Existing Tasks
Vanilla Flow
UniEdit-Flow
Full Promot/ / Null Prompt Inversion
Large Reconstraction Errors
Exact Inversion and Reconstruction
GCM
Text-driven Image Editing
Existing Flow Editing
Uni-Inv Uni-Edit
Water
Horse
Rish -irgg
X Deviation from the Driginal Image
Satisfectory Castomized Editing
Figure 1: UniEdit-Flow for image inversion and editing. Our approach proposes a highly accu- rate and efficient, model-agnostic, training and tuning-free sampling strategy for flow models to tackle image inversion and editing problems. Cluttered scenes are difficult for inversion and recon- struction, leading to failure results various methods. Our Uni-Inv achieves exact reconstruction even such complex situations (1st line). Furthermore, existing flow editing always maintain un- desirable affects, region-aware sampling-base based Uni-Edit showcases excellent performance for both editing and background preservation (2nd line).
INTRODUCTION
Diffusion models have revolutionized the field of image generation and created well-known text- to-image foundation models (Ramesh et al., 2022; Rombach al., 2022: Ho et al., 2020; Peebles & Xie, 2023). They have also enabled suite of applications ranging from personalized image generation (Gal et al., 2022; Ruiz et al., 2023; Ye et al., 2023; Wang et al., 2024c), image edit- ing (Avrahami et al., 2022; 2023; Zhang et al., 2023; Bar-Tal et al., 2023), using image models as a prior for 3D generation (Poole et al., 2022; Tang et al., 2023; Yi et al., 2024; Ren et al., 2023), to even non-generative tasks (Sepehri et al., 2024; Yin et al., 2023; Li et al., 2023; Di et al., 2023). Among these, the application of real image editing uniquely leverages the fact that diffusion models learn mapping between the prior (noise) distribution and the distribution of real images. By simply

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adding noise to realeimage, which mimicks the training process, and performing denoising with new condition (e.g. a modified prompt), a pre-trained diffusion model can be naturally repurposed to an image editor. This has led to a multitude of inversion methods (Song et al., 2020a;b; Lu et al., 2022; Wallace et al., 2023; Wang et al., 2025), and a myriad of training-free, inversion-based image editing methods (Avrahami et al., 2022; Han al., 2023; Couairon al., 2022; Bai et al., 2024; Qian et al., 2024; Brack et al., 2023).
Recently, a new class of models similar to diffusion models known as flow models have gained fa- vor and dominated lext-to-image tasks such as Stable Diffusion 3 (SD3) (Esser et al., 2024) and Flux (Labs, 2024). These models differ from the the previous generation of models based on diffu- sion in two key aspects:
1. Formulation Change Flow models are based on deterministic probability flow ordinary differential equations (PF-ODEs), in contrast to diffusion models which are based on SDEs. More specifically, SD3 and Flux uses the rectified flow formulation which models straight lines between the two distributions.
2. Architecture Change - In conjunction, there was a shift in architecture from U-Nets with cross-/self-attentts layers to using DiTs (Peebles & Xie, 2023) and MM-DiTs (Esser et al., 2024) to improve their data scaling ability.
As result, many methods effective in diffusion models face challenges when applied to flow mod- els. To bridge this gap, recent works have introduced specialized modifications to joint attention in MM-DiTss (Xu et al., 2024b; Avrahami et al., 2024; Wang et al., 2024b; Dalva et al., 2024). Mean- while, other approaches attempt to reintroduce stochasticity into flow models (Rout et al., 2024; Wang et al., 2024a; Singh & Fischer, 2024), effectively aligning them with diffusion models. How- ever, many of these methods ultimately retrace the trajectory of diffusion models, raising questions about their differences with respect to diffusion and long- term impact.
Our paper aims to re-design inversion and editing by explicitly accounting for the two design changes in the foundation model. We first examine the diffusion- based techniques that fail to transfer effectively to flow models, analyzing their behavior across different architectures. Specifically, we investigate the degradation of the so-called "delayed injection" in flow models, along with the im- pact of trajectory properties- -hhee vanilla inversion leads to localized sampling errors, as shown in Fig. 1. We argue that the straight-line and non-crossing trajectories of flow models make them prone to accumulating significant errors and even collapsing when velocity estimation is inaccurate during inversion and reconstruction. Furthermore, these properties complicate conditional trajectory guid- ance, posing challenges for tuning-free editing. Despite these difficulties, we focus on leveraging these characteristics strategically, aiming to unlock the unexplored potential of flow models.
We introduce a novel predictor-corrector-bee rate and stable reconstruction. Furthermore, we propose a robust sampling-based editing strategy with region-adaptive guidance and velocity fusion, enabling effective and interpretable text-driven image editing. Through both theoretical and empirical analyses, we validate our approach on several benchmarks and demonstrate tfate-of-the-art performance across diverse generative models, includ- ing flow models (Stable Diffusion 3 (Esser et al., 2024) and FLUX (Labs, 2024)) as well as diffusion models (results in Appendix E).
inversion method for flow models, aiming for accu-
2 RELATED WORK
Inversion. Modern generative models, particularly diffusion models, aim to map a standard Gaus- sian distribution to the real data distribution (Goodfellow et al., 2020; Ho al., 2020). Inversion, the reverse of the generation process, seeks to recover the latent noise corresponding to a given image by reconstructing the diffusion trajectory (Song et al., 2020a; Wallace et al., 2023). The introduction of DDIM (Song et al., 2020a) marked significant step forward, inspiring a series of high-precision solvers designed to enhance sampling efficiency and minimize inversion errors (Zhang et al., 2024; Lu et al., 2022; Wang et al., 2025; Lu et al., 2022). To further improve alignment between input images and their reconstructions, tuning- buning-based methods have been developed to mitigate reconstruc- tion bias (Mokady et al., 2023; Garibi et al., 2024; Ju et al., 2024; Tumanyan al., 2023; Yang et al., 2025). More recently, the emergence of flow models has driven the adoption of deterministic sam- plers, introducing alternative approaches inversion (Rout et al., 2024; Wang al., 2024b; Deng
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A Mon
Het
Joker
Watercolor
Figure 2: Delayed injection, which retains the source condition during the early denoising steps and intro- duces the edit condition at middle timestep (illus- trated in the bottom part), is widely used technique diffusion- based editing (top row). However, when applied to flow models (second row), it is ineffective. While flow-based editing exhibits a mild tendency to- ward the target edit, it fails to produce sufficiently strong effects.
Diffusion
Flow
Synthes's
Delayad Injaction
X:
4
(y(
et al., 2024; Song & Lai, 2024; Singh & Fischer, 2024). These methods design sampling strategies to reduce discretization errors in inversion. However, their non ron-rcconstruttion ooriented designs and restrictions on sampler selection limit their applicability downstream tasks such as image editing. In this work, we focus on the inversion and reconstruction process, designing Uni-Inv to achieve high reconstruction reliability and local exactness, making well-suited for applications.
Text-driven Image Editing, For image editing tasks, early triiiin-basee approaches explored gen- erative models to achieve controllable modifications (Zhu al., 2017: Karras et al., 2019). With the advancement of generative models, the focus has shifted toward training-fre frree editing meth- ods, which offer greater flexibility and efficiency. Tuning-bass basend methods have demonstrated im- pressive results but require iterative optimization during generation, leading to increased compu- tational costs (Ju et al., 2024; Parmar et al., 2023; Dong et al., 2023; Wu et al., 2023). Mean- techniques leverage multi-branch frameworks for precise con-
while, attention-manne manipulation- trol, but their applicability is often restricted to specific model architectures (Cao et al., 2023; Xu et al., 2024a). Sampling-baid -baeed methods introduce controlled randomness guidance mechanisms to achieve more flexible editing (Wang et al., 2023; Tsaban & Passos, 2023; Brack et al., 2024; Huberman- Huberman-Spiegelgee Spiegelglas et al., 2024; Kulikov et al., 2024; Mao et al., 2025). More recently, the rise of flow models built on MM-DiTs (Esser et al., 2024) has attracted significant attention the edit- ing domain due their strong generative capabilities (Patel al., 2024; Liu al., 2023; Sun al., 2024; Martin et al.. 2024; Hu et al., 2024). In this work, we rethink the design of efficient and gen- eralizable image editing methods in the era of flow models, introducing Uni-Edit, a model-agnostic and adaptable approach tailored for text-driven image editing tasks.
3 BACKGROUND
3.1 FLOW MATCHING
Generative models aim generating data that follows the real data distribution To from noise that follows some known distribution Ti (e.g., Gaussian distribution). Flow matching (Lipman et al., 2022; Liu et al., 2022; Albergo et al., 2023) proposed to learn a velocity field that is parameterized by a neural network to move noise to data via straight trajectories. The training objective is to solve the following optimization problem:
min B
Ez. . [1 Zo) - vo (Ze, 13)
(1)
Z. 1Z + (1 Z). t [0, 1).
where data Zn E mg and noise Z, E me Zi - Zo is the target velocity and vo(-) is the learn- able velocity field. The trained model is expected to estimate velocity field to map randomly sampled Gaussian noise Zi E (O, I) to generated data Zo in a deterministic way. This genera- tion process can be viewed as solving ordinary differentiable equation (ODE) characterized by dz.- = 0o ZZ. t) dt. This ODE can be discretized and solved by solvers such as the Euler method:
Z Zt + (fs-1 44) vy ZZ., fx))
where i E (N,...,0). 0), monotonically increases with i, to 0. and iN 1.
3.2 DELAYED INJECTION
Previous works have introduced delayed injection, a simple yet effective technique that helps main- tain image consistency during editing. This method preserves the original conditions reuses the
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Description: colorful bird standing branch.
Vanilla laversion
Uni-lav Inversion
7,
Pi..
2,
?o 9)
2
2
Z,
2
Editing: red bird standing on branch.
Conditional Denoising My(Zee (l)
Uni-Edit Editing
2,
2,
Falled Recon.
Our Recon.
Recon.: colorful bird standing on branch.
ODF Denoising
va(
ODE Denoising
Z,
7,
(a...ttt
7,
Vanilla Flow Inversion and Editing
UniFdiz-Flow Inversion and Editing
Figure An overview of our proposed Uni-Inv and Uni-Edit (bird red bird). (a) indicates that vanilla flow inversion is incapable for both exact image inversion and controllable editing. (b) demonstrates our proposed Uni-Inv and Uni-Edit, which perform efficient and effective inversion and editing. ú:-1 indicates the previous velocity. v.. v." u." c -conditioned, c -conditioned, and fused velocities. Z₁ Zi, Z, denote samples of sampling, inversion, and editing, respectively.
latent representations from the inversion process before specific timestep while injecting the edit- ing conditions afterward, enabling a balanced trade-off between content preservation and targeted modifications (Xiao et al., 2024; Couairon et al., 2022; Wu & De la Torre, 2023). As illustrated Fig. 2, the top row demonstrates diffusion-based example, where delayed injection enables an effective trade-off between preserving editing-irrelevant content and achieving the intended editing. intersecting sampling trajectories of diffusion models, modifying condi-
Due to the non-linear tions midway allows for trajectory transitions, facilitating more flexible and localized image editing (Patel et al., 2024). However, flow models exhibit straight-line and non-intersecting trajectories, which makes it difficult for points way (Liu et al., 2022). Such attributes fundamentally hinder the effectiveness of delayed injection, particularly in image editing. As shown in the middle of Fig. 2, applying delayed injection under similar conditions leads to limited improvements in flow-based editing. In this work, we take deeper look into how design effective guidance strategies for delayed injection, aiming unlock more controllable and reliable flow-based image editing.
sampling trajectory to transfer other trajectories mid-
4 METHOD
Fig. provides a brief illustration of image inversion, reconstruction, and editing based on vanilla ODE sampling methods, well overview of our approach. In Fig. (a), due to the mismatch between Z and used each corresponding forward step, it's difficult for direct inversion to ensure consistency between the reconstructed image and the original without proper guidance cannot enable controllable image editing and leads to undesirable results. In Fig. 3 (b), implement the idea of correction in two distinct forms for inversion and editing, respectively. In this section, we will present our proposed Uni-Inv and Uni-Edit with the technical contributions for inversion and editing, respectively.
Besides, conditional denoising
4.1 UNI-INV
The motivation of Uni-Inv is to conduct an accurate inversion capable of inverting ODE solutions back to the initial value for particular deterministic samplers. take the flow model (Liu et al., 2022; Esser et al., 2024) with an Euler method solver simple instance of deterministic iterative generation methods facilitated by its concise formula. Denote Z, the latent in the inversion process. Eq. (2) describes one iteration step in which we estimate Z from Z₁, by a denoising step. Through this formulation, given the initial value 2n Zo. the exact value of the inverted latent 2,, can be derived by the implicit Euler method:
Zr, Z:-11 (4--1 20 Zi, 44)
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However, since there is access Z1 in the inversion process, vo (Ze, (2) in Eq. (3) is unknown. Previous tuning-free approaches replace val (t, .t) with an approximation, e.g., Ve Z:,_.: : in DDIM Inversion (Song et al., 2020a;b). Such an approximation assumes that model predictions are consistent across timesteps, which bound to have errors. Empirically, we find that evaluating vy Z.....)) using f, which is similar to the implicit Euler method, instead of t;-1, which is adopted in DDIM Inversion, yields more accurate inversion results. This is because eliminating the error of t in function by can lower the error bound between inversion and sampling. As shown in Fig. 4, using
vo So.,...., 14-1) constantly achieves a larger local error compared to vo (Z.... t2) (D), and ulti- mately it reconstructs the noise with mismatched background. Nevertheless, the eπror accumulation of both strategies is non-trivial, as inaccurate velocity estimates continually deviate from the original trajectory. Thus, we propose that the key of inversion for gaining accurate reconstruction lies in find- ing an approximation Zt, of Zi, via Z to align the velocity of Inversion = Reconstruction
To estimate a proper Zr methods like ReNoise (Garibi et al., 2024) suggest utilizing recur- sive sampling, but this approach significantly increases computational cost. Inspired by the straight trajectories of Rectified Flow (Liu et al., 2022), we propose reusing the previously ob- tained velocity Ū at the current time step t; to push the previous sample Z as Zr for the im- plicit Euler evaluation while avoiding extra model forward. We first use the previous velocity backtrack the sample from 4-1 to 4 as a correc- tion, then obtain the velocity using Ze, which is better aligned with the current timestep t,.
0.8
Inversion
Various Z
0.6
Z,
0.4
02 11
8. 02 vitmmine)
dv| v(Uniiov)
0.0
Figure 4: Per-step error of the velocities and samples of vanilla inversions. We first syn- thesis image Zo from random noise Z1, then conduct inversions to get various inverted noises 2, with velocity of Un vo(Zz..: (+), Bo :t) (m), and Uni-Inv (+), respectively. We plot the per-step local error of samples (AZ) velocities (Av). The right shows vari- ous inverted Z,, while their border colors cor- respond to different inversion methods.
Alg. 1 provides an overview of Uni-Inv. Intu- itively, Uni-Inv introduces a correction procedure before performing the inversion step. It first tran- sitions to the high-noise step and estimates the ve- locity by simulating a denoising procedure. Then it returns to the original low-noise step and per- forms inversion using the latest "denoising- like" velocity, which can be lution of the implicit Euler method. Fig. 4 also provides the error accumulation curves of Uni- Inv (+), showcasing its superior accuracy. We have the following proposition that bounds the lo- cal error, i.e., the one-step error, of Uni-Inv for inversion and reconstruction. It gives the theoret- ical justification for the high quality of Uni-Inv for reconstruction. The proof is in Appendix A.
closed-form so-
Algorithm 1 Uni-Inv (Euler)
Input:
Velocity Function va, Initial Image Zo ~ To. Time Steps 1 = (lo...., to 0, (N 1.
Initial:
Do 1o (Z)
Z Zn
For i = 1 to N do
1: Z, + Z 1 - -1i--1-::00- 1;)00-1
Proposition 4.1. Suppose the velocity field vo is Lipschitz, and there is a constans C such that Iz Z,, < C' l|ttp (1ttp-tt tyll Vt,,t, E [0, where Ztp and Zi, come from the same sam pling process. Then for any two consecutive steps 4 and t, the local error of inversion and reconstruction using Uni-Inv is O (At?), where
2: 0; <- Vo Ze, 4:)
3: Z Zi (1:-1 1:)0,
End for Output: 2,
At. -4--1
4.2 UNI-EDIT
Sec. 3.2 provides a case study that highlights the challenges faced by flow models in image editing tasks. As shown in Fig. 5, delayed injection aims to establish intermediate state between the original and editing trajectories. However, due to the non-intersecting nature of flow trajectories, is difficult to obtain significantly different directions from middle steps, often resulting in inchoate
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results. On the other hand, unrestricted direct sampling with editing conditions often leads un- desirable edit, as the generated sample diverges from the original trajectory from the outset.
To address these issues, an intuitive strategy is to inject editing conditions earlier while mitigating excessive modifications using information from the current latent state. Instead of simply inject- ing conditions earlier, we propose additional cor- rection steps during the editing procedure solely based on the current latent Zt,- At the injec- tion step, this variable is initialized as the inver- sion latent, i.e., Z = Zi. where a denotes the delay rate and N is the total number of sam- pling steps. As shown in Fig. 3 and 5, given a source condition c that describes the source image and a target condition c" that specifies the editing objective, we compute two different velocity estimates, o.. = vg (2,,, cs) and of = vo(Zr, I c²),, via the velocity field va- To introduce correction step, Z,, is first transi- tioned to a previous step with higher noise along the direction of e... is then mapped back to Zt, via v/', which is aligned with the current timestep 4. Thus, the sample Zz, is corrected by the cor- rection step 8; ~ (t:-1 - (:)(!!" adit-friendly stage Zr. Z: + 8t, as the visual- ization in Fig. 5. This procedure corrects unde- sirable components introduced in early sampling steps that may hinder effective editing. Then, we apply of" (introduced in next part) to move from Zt, to Zi... and finally achieving a proper edit.
One-Step Visualization
Uni-Edit Correction
Direct
Edit
Z
Corrected Sample
Our Edit
Zo
Pi
Delay- Inject.
Ze
v
Recon. Z.
2.
Figure 5: Demonstration of various sampling- based image editing methods (dog + lion). Directly utilizing c² as condition leads to an undue editing. Leveraging delayed injection, which widely used in diffusion- based meth- ods, inevitably results in an inchoate perfor- mance when using deterministic models. Our Uni-Edit mitigates early steps obtained com- ponents that are not conducive to editing, ulti- mately achieving satisfying results.
w;") to an
4.3 REGION-ADAPTIVE GUIDANCE AND VELOCITY FUSION
To further precisely correct concepts that need to be edited while avoiding excessive damage to the background, simple idea is to use mask to determine the edit-relevant regions. Previous works have observed that the difference between latents conditioned on different prompts high- lights regions crucial for editing (Couairon et al., 2022; Han et al., 2024). Building on this insight, leverage this difference v, to construct a mask m, MASK(2; ). which serves as regional guidance for correction and velocity prediction, thereby improving the controllability of Uni-Edit. Here, M.SK(.) denotes the min-max normaliza- tion of the channel-wise mean map. To guide the correction step, we first apply a weighting factor of m)), encouraging edit-relevant regions to backtrack with larger stride, thereby enhancing the removal of original concepts crucial for the intended modification. The region guided correc- tion step is 8; = w(4-1 (1((1 + m.) @ v,, where w indicates the guidance strength. For the subsequent sample update, we fuse the target and source velocities using m; and (1 m.) as re- spective weights, thus forming the velocity fusion: uf" m, @ of + (1- m,) O vf. Then the sample is updated by Z = Z, + (t:-1- - t.)of. The complete editing procedure is outlined in Alg. 2. The delayed injection framework, parameterized by the delay rate a, strikes balance
Algorithm 2 Uni-Edit (Euler)
Input:
Velocity Function vg. Initial Image Zo ~ To.
Source Condition c². Target Condition c², Guidance Strength w, Delay Rate a, Time Steps t fto...., to 0, tan S 1.
Initial:
Z + Uni-Inv(wa, Zu.
Z,nN + Zi.N
For oN to 1 do
I: u.a vf" + vs Zi, to c0), 1,, Vss lc²)
2: Uj t v._ -.
3: m. Mask(v,) )
4: s, +- w (1:-1
1i) (1 +m:) U.,
5: Z. Zi, + si
6: vf +- m: O v. + (1 11-ma m.) 2 o
7: Z Zea + (t t.)y
End for Output: Zo

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Source
Houn
RP Seiverver
FireFlow
Oars
Houn
RF Solv
FireFlow
Ours
Diiturion
Unconditional
Conditional
Figure 6: Qualitative comparison on inversion & reconstruction. Our method ensures stable reconstruction results in both situations with description accessible (conditional) and unaccessible (unconditional), while taking into account both overall and detail consistency.
between preserving background details and achieving effective modifications, while simultaneously reducing inference costs. By integrating regionally enhanced guidance with velocity fusion, we ulti- mately obtain an adaptive and computationally efficient editing approach. Furthermore, our velocity fusion method offers advantages over existing latent fusion techniques (Couairon et al., 2022; Han et al., 2024) by providing better performance without additional memory overhead, as illustrated in Fig, 8. We further provide the theoretical analysis of Uni-Edit in Appendix B and details of our model variants in Appendix D.
5 EXPERIMENTS
5.1 SETUP
We conduct experiments on two tasks: 1) image inversion and reconstruction; 2) text- driven image editing. For image inversion and reconstruction, we compare our Uni-Iny with the Fuler and the Heun method, well as flow-based methods like RF-Solver (Wang et al., 2024b) and FireFlow (Deng et al., 2024). For text-driven image editing, we compare our Uni-Edit with diffusion- based methods: P2P (Hertz et al., 2022), PnP (Tumanyan et al., 2023), PnP-Inversion (Ju et al.. 2024), EditFriendly (Huberman- SentermannSSiegeggghee IntEdit (Xu et al., 2024a), along with the aforementioned two flow-based methods. More tuning- based (Mokady et al., 2023; Garibi et al., 2024) and training- baaning-based methods (Wu et al., 2024; Brooks et al., 2023; Shi et al., 2024; Wei et al., 2024) discussed in Appendix E.
Baselines:
et al., 2024), MasaCtrl (Cao et al., 2023), and
Benchmarks and Metrics: For inversion and reconstruction, we report the average MSE, PSNR (Huynh-Thu & Ghanbari, 2008), SSIM (Wang et al., 2004), and LPIPS (Zhang al., 2018) of reconstructed images on the Conceptual Captions validation dataset (Sharma et al., 2018), which consists of 13.4K images annotated with captions. These metrics are evaluated in both conditional (using image captions as prompts) and unconditional (using null text only) settings. For text-driven image editing, we conduct experiments on PlE-Bench (Ju et al., 2024), which contains 700 images with 10 different editing types. To evaluate edit-irrelevant context preservation, we use structure distance (Tumanyan al., 2022), along with PSNR and SSIM for annotated unedited regions. The performance of the edits is assessed using CLIP similarity (Radford et al., 2021) for both the whole image and the edited regions. More ablations and results are provided in the Appendix C, D, and H.
Implementation: We primarily conduct experiments using stable-di foelleddiffus fus on-3-mediid um (SD3) (Esser et al., 2024) and F FUUX. --dv (Labs, 2024) models. For inversion and reconstruction, set the sampling step to 50 for SD3 and 30 for FLUX, while for image editing, we use 15 steps with a delay rate a of 0.6 or 0.8. The relationship between the delay rate and NFE is NEE = No.N + 1. The guidance strength is fixed at 5 for all experiments. Additional results of our method applied to diffusion models (Podell et al., 2023) and various datasets (Hui et al., 2024; Zhao et al., 2024) provided in Appendix
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Table 1: Quantitative results for inversion and reconstruction. For Stable Diffusion (SD3) (Esser et al., 2024), we keep each method's NFE close to 100, which means we set sampling step to 50 for once-forward methods (i.e., Euler. FireFlow, and Ours) and 25 for twice-forward methods (i.e., Heun and RF-Solver). For FLUX (Labs, 2024), we keep NFE close to 60 (i.e., 30 for once- forward methods and 15 steps for twice-forward methods). We adopt the official implementations of baselines for FLUX, and reimplement their methods for SD3. The best and second best results bolded and underlined, respectively. Cells highlighted from worse
better
Unconditional
Conditional
Method
Model
MSE 10a
PSNR
SSIM 10a
LPIPS
MSE
4 10a
PSNR
SSIM
LPIPS
10a
10a
10a
Euler
29.98
16.54
58.97
29.03
29.14
16.80
57.54
29.92
Heun
25.34
16.98
67.25
26.63
26.32
16.89
64.14
27.70
RF-Solver
SD3
45.24
15.43
56.57
33.67
26.54
18.52
64.10
26.38
FireFlow
20.27
19.60
66.96
25.29
16.95
20.85
68.89
22.83
Ours
11.52
21.81
78.89
12.85
7.86
23.41
82.23
9.53
Euler
90.59
11.10
40.51
43.13
85.69
11.43
37.64
44.31
Heun
83.04
11.77
42.10
39.96
76.79
12.17
40.17
41.18
RF-Solver FLUX
29.32
16.97
57.17
31.75
34.71
16.38
55.64
32.43
FireFlow
23.31
18.15
63.85
27.96
30.78
17.59
62.51
28.30
Ours
8.85
22.15
79.45
17.10
14.36
20.91
77.09
20.27
Source
P2P
PaP
PaP-avv
Infidit
RF-tov.
RF-Solver
FireFlow
Ours (FLUX)
Ours (SD3)
Lipstick
Watercolor
Hot
Moon
Figure 7: Qualitative comparison image editing. Our method consistently achieves more ap- propriate editing with better background preservation across various flow models.
5.2 REAL IMAGE INVERSION & RECONSTRUCTION
Quantitative Comparison: Tab. provides the quantitative results for reconstruction across various flow-based methods. We reconstruct images using only the inverted noise, without utilizing latent features from the inversion process. The results demonstrate that our proposed Uni-Inv consistently outperforms the baselines across different models and settings, including the unconditional case where text description is absent.
Qualitative Comparison: Fig. shows the qualitative comparison of inversion and reconstruc- tion across methods. Our method achieves nearly identical reconstruction results in both uncondi- tional and conditional settings for various flow models. In contrast, other methods struggle with reconstruction without text conditions and show weaker performance even when image captions are available. These results strongly highlight the effectiveness of our Uni-Inv.
5.3 TEXT-DRIVEN IMAGE EDITING
Quantitative Comparison: Tab. presents the quantitative results for text-driven image editing. Our method, Uni-Edit, consistently outperforms other approaches, particularly excelling in CLIP
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Table 2: Text-driven image editing comparison on PIE-Bench (Ju et al., 2024). We report the peer-reviewed results of each baseline, and evaluate our proposed Uni-Edit using the relatively lightweight Stable Diffusion (SD3) (Esser al., 2024) and FLUX (Labs, 2024) to demonstrate the effectiveness. We set = 5 and mark (N, a) in the subscript. The best and second best results are bolded and underlined, respectively. Cells are highlighted from worse to better
Struc.
BG Preservation
CLIP Sim.t
Method
Model
Steps NFE
Dist. L
PSNR
LPIPS
MSE
SSIM 10a
Whole Edited
10s
104
P2P
Diff. 69.43
17.87
208.80
219.88
71.14
25.01
22.44
50
100
PnP
Diff.
28.22
22.28
113.46
83.64
79.05
25.41
22.55
50
100
PnP-Inv.
Diff.
24.29
22.46
106.06
80.45
79.68
25.41
22.62
50
100
EditFriendly
Diff.
24.55
91.88
95.58
81.57
23.97
21.03
50
100
MasaCtrl
Diff.
28.38
22.17
106.62
86.97
79.67
23.96
21.16
50
100
InfEdit
Diff.
13.78
28.51
47.58
32.09
85.66
25.03
22.22
12
72
RF-Inv. RF-Solver
FLUX
40.60
20.82
159.62
96.01
71.92
25.20
22.11
28
56
FLUX
31.10
22.90
146.11
80.70
81.90
26.00
22.88
15
60
FireFlow
FLUX
28.30
23.28
130.61
71.01
82.82
25.98
22.94
15
32
Ours (15, 0.6)
SD3
21.40
24.96
89.78
49.20
86.11
26.39
22.72
15
28
Ours (15, 0.8)
FLUX
26.85
24.10
112.71
61.30
84.86
26.97
23.51
15
37
Ours (15, 0.6)
FLUX
10.14
29.54
64.77
18.30
90.42
25.80
22.33
15
28
Zo
2 EIf
Zo
HHat
Corgi + Cat
Latent
Fusion
Figure 8: Visualization of Uni-Edit process. The guidance mask of each denoising step is shown at the upper right of the image. We also demonstrate the "Sphinx" phenomenon that existing latent fusion approaches may cause at the lower left of the figure.
similarity. Based SD3, Uni-Edit achieves balance between background preservation and editing effectiveness, outperforming existing flow-based methods in both areas. On FLUX, it surpasses the inherent limitations of training-free approaches in background preservation while maintaining competitive editing performance. More results on diffusion models are given in Appendix E.
Qualitative Comparison: Fig. compares the visual editing outcomes across methods. Attention- based approaches often transfer attributes to unrelated areas (e.g., P2P, PnP, and PnP-Inversion add purple to the overall image rather than focusing on specific regions, like lipstick). Sampling- methods without regional constraints suffer from mis-edits or insufficient editing (e.g., RF-Solver and FireFlow). In contrast, our method provides precise guidance for both local modification and global stylization. Additional results on various datasets (Zhao et al., 2024; Hui et al., 2024) are provided in Appendix
Procedure Analysis: Fig. 8 visualizes the editing procedure. The superscript 0 indicates the result is directly transitioned from the sample to = using its current velocity. Early steps focus on broader areas with stronger editing intensity to eliminate original concepts, while later steps refine details, reducing the influence of m, We also show results from the existing latent fusion method (Couairon al., 2022; Han et al., 2024), which uses masks to fuse inversion and edit latents. These results lead to unnatural, "Sphinx"-Iie -liee outputs, highlighting the adaptability and efficiency of our approach. More ablation studies, applications, and video editing results are shown in the Appendix.
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6 CONCLUSION
We introduce novel, tuning-free, model-agnostic methodology that combines the reconstruction- effective inversion method Uni-Iny with region-aware, training-free image editing strategy Uni- Edit. To exploit the properties of flow models, we design robust, region-adaptive guidance in Uni- Edit enhance the delayed injection framework, supported by Uni-Inv. Extensive experiments validate the effectiveness of our approach, demonstrating remarkable results while maintaining low inference costs. We will explore more diverse conditions (e.g., adopt an image as a personalization prompt) that can be injected for further customized image editing in the future.
7
ACKNOWLEDDEMENNT
This work was supported, in part, by the NSERC DG Grant (No. RGPIN-2022-04636), the Vector Institute for AI, the Canada CIFAR AI Chair program, and gift fund from Snap Inc. Resources used in preparing this research were provided, in part, by the Province of Ontario, the Government of Canada through the Digital Research Alliance of Canada alliance can, ca. and compa- nies sponsoring the Vector Institute www. vectorinstitun
ai/ htl//partnerss. and Advanced Research Computing at the University of British Columbia. Additional resource was provided by the Canada Foundation for Innovation (CFI) via the John R. Evans Leaders Fund (JELF). G.J. is supported by UBC Four Year Doctoral Fellowship.
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Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control text-to-image diffusion models. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 3836-3847, 2023.
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 586-595 2018.
Haozhe Zhao, Xiaojian Ma, Liang Chen, Shuzheng Si, Rujie Wu, Kaikai An, Peiyu Yu, Minjia Zhang, Qing Li, and Baobao Chang. Ultraedit: Instruction-based fine-grained image editing at scale, 2024. URL https::///// //ariai..oorgg.eeeee wwwp///rrgga//aaaaae///e
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APPENDIX
A Proof of Proposition 4.1
2
Theoretical Analysis of Uni-Edit
Ablation Studies of Uni-Edit
C.I Component Ablations
C.2 Hyper-parameter Selection
D Uni-Inv on Different Generation Methods
D.1 Heun Method based Uni-Inv
D.2 DDIM based Uni-Inv
D.3 Comparison between Iterative Inversion Methods and Uni-Inv ................................
E Uni-Edit on Diffuusin Models
Diverse Applications
Application Utilizing Diversified Plugins
G.1 Introducing of New Conditions
G.2 Enhancement of Controllability
H Additional Qualitative Comparison
11
H.I Uni-Inv
H.2 Uni-Edit
12
I Additional Results on Editing Tasks
15
L1 Image Editing
15
I.2 Video Editing
18
Limitations and Future Works
18
K LLM Usage Statement
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A PROOF OF PROPOSITION 4.1
Prop. 4.1: Suppose the velocity field Vg is Lipschitz, and there is a constant C such that Zi -Zi,1 Cl 1ilp-tay tall Vtpsty by [0, 11, where Zt and Zt come from the same sampling process. Then for any two consecutive steps fi-1 and iú the local error of inversion and reconstruc- tion using Uni-Inv is O (At)), where Al; 14-4-11 4-11-
Assumption The velocity function va(-,T) is Xi-Lipschitz for V E [0, 11, i... given a T, [[o((), oD (Q, r)ll S lls1 forl S2,
Assumption 2. The velocity function vo(C, is X2-Lipschitz for A S. i.e., given a S. X2 |1 -72ll for Tı, T2-
(8((, (l
Assumption 3. Zt, Z,, s llt tyll v. E [0, when Zi and Zi, come from the same trajectory.
Proof. Given a deterministic solver, egg. Euler's method:
Z Ze + (4:-1 - t,) wo (Zi, fz).
(A.1)
The corresponding inversion step of Uni-Inv is denoted by:
i. 2 (ti-1 - t,) Üs.
(A.2)
where G is obtained via Algo. and can be expressed as:
i; 1o (2c--1 (1:-1 (;) 11))
(A.3)
Define the estimation error & as & Zi; 2..11. Bringing Eq. A.1 and Eq. A.2 into it, obtain that:
E, = (ti-1 t₂) 00; vy (Zu, t.)ll
(A.4)
Denote E = =/00 vo tz)ll, can bring in Eq. A.3:
E p0 (Z.... (t tc) t.) Uo (Ze, tt)
(A.5)
Using the Lipschitz continuity of v)( we have:
E S X1 Z (ti-1 - -t4)0,11- -Zr,
0;-1
Zri1
(A.6)
Bring in Eq. A.I for Ze there is:
E < X1
(z,_. - -Z:..) + (ti_1 - tt) (wo (z) - B(-1)
(A.7)
s X1 2 - -Z + X1 (ti-1 - t4) 1wo (Zi,, t4) - -b-1ll.
The first term is the accumulative error of the previous steps. We denote it E/1 _ Z Ze,_1 and it should be neglected for local error analysis. We further denote E, |0 (z) ---1 To analyse this item, we first consider a second-order case, i.e., utilizing an additional function
evaluation step to calculate ū;-1 Uo to-1. Then we have:
E = =||00 (Z t;i) - vo (Zn...tt-1)
(A.8)
Using the Lipschitz continuity of vn(-, T) and wo -), we have:
E7 = Vo (Z,, t2) - Uo ZZe t + we (z,, t.-1) - Vo (
$ 000 (Z,,, (.) - 2i ZZ, t 1-11) + vo (Z., t4-1) - vo (Zmm))
(A.9)
< X1 Zz - 2, .| + X2 |14 4-1111-
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We denote At, t. 1,-1. Using the Assumption 3, we have:
8 s Xi (1 Zz. Zr i1l 2 Z 1)) X2
(A.10)
2 (CX1 + X2) At; + XIE;
Ultimately, the estimation enror is as follows:
& S A1, Ale (X18)) +
3 At (x1,, + X1 ((CX, + x2) At, + x1E())
(A.11)
X1 (CX1 + X2) A + (Xf + E,
For local error analysis, we neglect the global accumulated error E then we have the local error Et:
8 S X1 ( =
+
(A.12)
Furthermore, since the step count of the iterative algorithm is O (1/At,) we can have the global emror: E 0 (max (AR)).
Now, let's go back to the second-order case assumption ō;-1 = vo 4-11 we mentioned earlier. From practical perspective, Algo. provides an additional function evaluation in the initialization stage, making its first step the standard second-order case. After that, in the ideal case, each i; should converge to n; and thus the first-order approximation of the algorithm does not significantly affect the error. Theoretically, since that:
0 th (4-1),
(A.13)
Z. 2:-2 (4i-2 14-1) Ū:-2,
neglecting the last-step accumulated error, derive that
Ž 2 Z
At,-1 16 - 8 0.-21
(A.14)
Using the Lipschitz continuity of (.( we can get:
| - ú;-2: - X1 22-22- - Zi-2
(A.15)
Neglecting the last-step accumulated error of velocity estimation for local error calculation, we note that the one-order approximation brings no change to the conclusion of E. = (Ar?).
This local error serves the dual processes of inversion and reconstruction, theoretically ensuring the effectiveness of our proposed inversion practical applications. Moreover, our approach does not utilize the derivative approximation to achieve the result, only expects the velocity function to have good mathematical properties.
B THEORETICAL ANALYSIS OF UNI-EDIT
Conducting mathematical perspective, our proposed Uni-Edit be compressed into single arithmetic representation. As shown in Algo. 2, we have the editing result:
Že,, Żτ + (t:-1 14) v.
Z₁ + 8; + (|4- (4) va
Zr, + w (t:-1 t4) (1 + m.) 2 (v, -.,)
(B.16)
+ (4i-1 - t,) (m, 3 o. + (1-m;) 3 v.
- + (t:-1 - t.) vi
and the reformed velocity is:
n, (1 m,) 3 (0. vن mm 3 oa + (1 m.) 3 m
(B.17)
= 5. + (w + m:) + m.) 3 (v.f - -;

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Published as a conference paper at ICLR 2026
Source Bread Meat
Delayed Inj. CLIPw= 24.26
Corr. w/o 1+ CLIPw= 25.16
Ours
m Corr. =
m. SSIM=82.52
SSIM=83.13
ICLIP==25, insufficient editing]
[SSMM<84, obvious BG changel
Figure C.I: Illustrations of insufficient editing and background destruction. CLIP indicates the whole CLIP similarity.
Table C.I: Component ablation studies of Uni-Edit on PIE-Bench using Stable Diffusion We set step 15, a = 0.6, and w = 5.0 in these experiments. "w/o Uni-Inv" means using DDIM Inversion- like Euler inversion to replace Uni-Inv in the editing procedure. "w/o Uni-Edit" indicates using naive delayed injection (using of after inversion) for editing. "Corr." represents the Correction 8; in Uni-Edit. "Corr. w/o 1+" indicates using m, as the mask weight of 8, instead of (1 mz)- "m V.F. 1" means using 1 to replace the mask m, in Velocity Fusion of (vf" v."), which can be seen as Uni-Edit without Velocity Fusion. "m VF 0" means using 0 to replace the mask m; in of" (v." v!') ). "m Con: = 1" indicates using 1 to replace the mask m, in the Correction which can be seen as Uni-Edit without Correction. "m; 1" claims performing editing without region-adaptive guidance, which is equívalent to using simple classifier-free guidance (CFG).
Structure
Background Preservation
CLIP Similarity?
Method
Distance 103
PSNR
SSIM 102
Whole
Edited
w/o Uni-Inv
40.87
21.93 (-3.03)
74.90 (-11.21
25.54 (-085)
21.93 (40.79)
w/o Uni-Edit
9.78
27.92 (+2.9)
89.62
(43.5))
24.26 (-2.13)
20.92 (-1.80
w/o Corr.
9.45
28.00 (+3.04)
89.67
(43.56)
23.78 (-2.61)
20.52 (-2.20)
Inv. v(. li)
36.95
23.82 (-1.14)
80.25 (45.6)
25.99 (-0.40)
22.08 (-0.64)
Corr. w/o
11.33
27.44 (+2.48)
89.31 (+3.20)
25.16 (-1.23)
21.72 (-1.00)
m V.F. 1
22.92
24.62 (-0.34)
85.50 (-0.61)
26.39 (-0.00)
22.74 (40.02)
m; V.F. 0
21.78
25.01 (+0.05)
86.13 (+0.02)
26.22 (-0.18)
22.57 (-0.15)
m, Cor.. 1
28.98
23.36 (-1.60)
83.13 (2298)
26.55 (+0.16)
22.80 (40.08)
m.
30.48
23.07 (-189)
82.52 (33.99)
26.53 (40.14)
22.83
(40.11)
Ours
21.40
24.96
86.11
26.39
22.72
It's interesting that we finally obtain a velocity oy which is very similar to the classifier-free guid- ance (CFG) (Chung et al., 2022) but with a per-pixel-variant weight instead of a single constant value. Some previous works consider CFG as predictor-corrector (Song et al., 2020a; Bradley & Nakkiran, 2024). From this perspective, whereas we take different yet more interpretable approach to conduct predictor-correcto and eventually obtain method with adaptive guidance strength for different regions. In the manuscript, we experimentally validate that the mask obtained from our designed sampling strategy is rationally adaptive to vary with the editing objective and the itera- tion step. Therefore, our method ensures to achieve per-pixel adaptive guidance strength within framework of predictor-corrector, which in turn confers effectiveness for text-driven image editing to flow models.
C ABLATION STUDIES OF UNI-EDIT
C.I COMPONENT ABLATIONS
Preliminary, shown in Fig. C.2, text-driven image editing tasks pursue trade-off between editing effect and background preservation. The result we hope for to preserve non-editing regions while also achieving the editing requirements of the image. Empirically, PlE-bench (Ju et al., 2024),
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30
SD3
FLUX
0.90
32
PSNR
FLUX
SD3
0.93
25
0.80
30
0.90
PSNR
SSIM
20
27
0.88
FLUX
0.70
FLUX
SSIM
15
SD3
25
SD3
0.85
0.4
0.5
0.6
0.8
1.0
27
23.5
FLUM
FLUX
SD3
Edit
23.0
26
SD3
Edit
22.0
26
22.5
25
FLUX
22.0
FLUX
21.0
25
Whole
SD3
21.5
24
Whole
SD3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0
2
4
(a) Ablation Studies an
(b) Ablation Studies on
Figure C.2: Ablation studies of Uni-Edit on (a) delay rate and (b) guidance strength w. The top
indicates the background preservation, while the bottom refers to CL.IP scores of editing.
CLIP < 25 always means the editing impact is insignificant (as the blue box in Fig. C.2), while SSIM < 84 usually indicates the background is destructed (as the red cirele in Fig. C.2).
We provide ablation studies of the main components of Uni-Edh Uni-Edit in Tab. C.I, discussing the impacts of Uni-Iny, Uni-Edit, Correction, Velocity Fusion, and the mask in image editing tasks. Without Uni-Inv (w/o Uni-Inv), the background preservation decreases significantly, indicating that inverted noisy latent, which is capable of accurate reconstruction, is necessary for controllable editing. The results of naive delayed injection (w/o Uni-Edit) show the importance of well-designed guidance for flow-based image editing, which just as discussed in the manuscript. Meanwhile, the Correction provides guidance targeted to the editing objective, thus unleashing image editing of flow models. When disabling the Correction (w/o Corr.), the result shows almost no editing. Regarding the mask m, we can demonstrate through relative experiments that it plays an important role in the trade-off between background preservation and editing effect. The mask m, enhances the correction and editing strength of sciting-related regions, while avoiding undesirable influence of these effects uditing-unrelated regions, thereby improving the editing effect and avoiding serious damage to the background. No matter which component disables the mask, it will cause the background preserva- tion to evidently worse, while the editing effect only has a marginal improvement, showcasing the effectiveness and necessity of the region-adaptive guidance.
To make the ablation studies clearer, we further provide qualitative visual comparisons of the editing results in Fig. C.3. The results in (a) indicate that, without the accurately reconstructable latent inversion method), editing using flow models
provided by Uni-Inv (utilizing DDIM-Inversion- likely crash. Even though inversion using v(-, t4) has appropriately improved the inversion accuracy, it is still insufficient to support reliable real image editing. Meanwhile, the simple delayed injection without Uni-Edit provides low editing effects, resulting in unchanged images.
Furthermore, (b) shows the results after replacing the fused velocity of with oe or e ! illustrates that directly utilizing u" move the sample can lead to the background not remaining unchanged. 2 demonstrates that if we adopt only v as the velocity, even with the correction step, the results can be unnatural (it should have turned into cup but did not). Additionally, the second row of (b) also indicates that velocity fusion can make the details of the results more reliable (velocity fusion provides the strawberry with a fuller color). Although the velocity itself may not have strong impact editing (just like the failure of delayed injection), velocity fusion still enhance the editing details and provide regional sensitivity, thus making editing more precise and reliable.
Subsequently, (c) provides the editing results of using different inversion velocity functions. The velocity functions here are consistent with Fig. 4. is evident that without precise inversion like Uni-Iny, achieving success in image editing difficult, especially for flow models that are suscepti-
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(a) Ablation of Main Parts
(b) Ablation of Velocity Fusion =
Source
w/o Uni-lnv
w/o Uni-Edit
Ours
Source
F
p pf
Our
+Lion
CCup
+Strawberry
-Moon
(c) Ablation of Inversion
(d) Ablation of Correction
Source
P(s.t)
Ours
Source
w/o Corr.
Mi Corr.
Ours
+Castle Heart-
Cat Stained
Figure C.3: Qualitative comparison for ablation studies of Uni-Edit. (a) Comparison of ablation
in the main parts. (b) Comparison of different kinds of editing velocities. (c) Comparison of different kinds inversion methods. (d) Ablation of correction in Uni-Edit.
ble cumulative errors. Additionally, (d) presents the visualization of correction's 's ablation studies. "w/o Corr." means setting the correction step as 8; O, and m. Curr. 1 denotes turning the cor- rection step into s; (o(t,-1-t;)) (1+1)) (instead of using fused velocity), which represents weakening the editing ability of Uni-Edik while the latter means maintaining the editing ability of Uni-Edit but eliminating ance. In these cases, 3 shows that without correction the method will significantly reduce editing ability. and ④ indicates that correction without region-adaptive guidance can easily cause changes in editing-irrehe irrelevaat regions. Not only that, as shown in @ correction without regional restrictions can also easily cause overexposure, which is similar to large classifier-free guidance (CFG). These visualizations ablation results further demonstrate the roles and significance of the components in our proposed method.
The former is similar to the simple delayed injection
addioive guid-
C.2
SELECTION
Additionally, we present the ablation studies of our proposed Uni-Edit with step = 15 for various hyper-paramen papee-parameters in Fig. C.2. The results demonstrate that different hyper-parameter bring rational skews to the trade-off between background preservation and editing effectiveness. Nevertheless, our approach improves the overall level of the trade-off, making effortless to bring benefits both aspects.
D UNI-INV ON DIFFERENT GENERATION METHODS
D.1 HRUN METHOD BASED UNI-INV
To validate the transferability and effectiveness of our method on different samplers, we reimplement our Uni-Inv based on the Heun method. To be specific, since the Heun method is formulated as:
Z,__, = Z,, + (t:-1 - t2) Uo (Ze, 4)) + vy (ve (Zr (4)44)
(D.18)
we directly reform the velocity function as:
1 (C, 2o (C.T) vo (wo (C, T) 7)
(D.19)
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Table D.2: Quantitative results for inversion and reconstruction our Uni-Inv based on Heun method with Flow models and DDIM with Diffusion models. We set the step to 50 for SDXL (Podell al., 2023), 25 for SD3 (Esser et al., 2024), and 15 for FLUX models to conduct the experiments. The best results are bolded.
Unconditional
Conditional
Method
Model
MSE 10a
PSNR
SSIM
LPIPS
MSE
PSNR
SSIM
LPIPS 10a
102
102
10a
102
DDIM
8.99
22.19
75.57
12.76
7.35
23.21
77.73
10.20
SDXL
Ours (DDIM)
6.32
24.18
79.05
8.60
5.17
25.04
80.63
6.95
Heun
25.34
16.98
67.25
26.63
26.32
16.89
64.14
27.70
SD3
Ours (Heun)
20.23
20.10
76.38
15.76
12.75
22.31
79.62
12.43
Heun
83.04
11.77
42.10
39.96
76.79 32.35
12.17
40.17
41.18
FLUX
Ours (Heun)
57.39
13.63
$7.45
26.95
16.79
67.33
21.25
Table D.3: Inversion comparison between iterative inversion methods and Uni-Inv on the first 500
images of CC3M.
Unconditional
Conditional
Method
Model
Steps NFE
PSNR t SSIM 102
LPIPS
PSNR
SSIM 10= LPIPS
102
ReNoise
Diffusion
24.14
78.71
11.99
24.29
78.95
11.66
50
150
GNRI
Diffusion
23.90
78.07
8.68
23.88
78.03
8.79
50
150
Ours
Diffusion
24.41
79.67
7.96
25.24
81.08
6.31
50
101
ReNoise
SDXL- Turbo
17.59
57.23
28.43
16.81
55.10
29.27
4
16
GNRI
SDXL-Turbo
13.46
49.43
39.82
13.20
48.56
38.68
12
Ours
SDXI--Tubo
20.08
71.15
14.32
20.63
71.86
13.14
4
then using vff to replace the original velocity function in Algo. 1. As shown in Tab. D.2, our approach improves the reconstruction accuracy of the Heun method across the board. This demon- strates flexibility and adaptability Uni-Iny to different samplers and reflects effectiveness.
D.2 DDIM BASED UNI-INV
DDIM (Song et al., 2020a) provides an efficient sampling method for the tochastic-difffrentiaa equation- based diffusion models and allows access to the sampling strategy with the form of ordi- nary differential equations. Benefiting from this, we are able to migrate Uni-Inv to diffusion models by simply treating the predicted initial noise as a velocity, utilizing the cached last-step predicted noise push forward the current samples to the next timestep. thus performing our inversion. We evaluate the above strategy on SDXL (RealVisX)) shown in Tab. D.2. Though DDIM has already demonstrated strong fcasibility in numerous appli- cations, our approach can still take it to the next level and bring about an overall improvement in reconstruction accuracy. It also indicates that work does not just face particular methodology. We expect to build approaches that can continuously provide insights into the developing trend of generative models.
0) (Podell et al., 2023). The results are
D.3 COMPARISON BETWEEN ITERATIVE INVERSION METHODS AND UNI-INV
We further compare iterative inversion methods (Garibi et al., 2024; Samuel et al., 2025) with our proposed Uni-Inv on inversion and reconstruction experiments. As there are flow-based imple- mentations of these methods, we compare inversion on diffusion using official code and settings in D.3. For faimnsss, set the optimization steps SDXL-Turie of ReNoise (Garibi et al., 2024) to 2 as GNRI (Samuel et al., 2025) does. We adopt the same experimental settings as the manuscript, except for the sampling steps (50 for diffusion models and 4 for SDXL-Turbo). The experiments

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Published as a conference paper at ICLR 2026
the first 500 samples of the CC3M (Sharma al., 2018) dataset further provide Uni-Inv's superiority compared with the mentioned iterative inversion methods.
E UNI-EDIT ON DIFFUSION MODELS
Table E.4: Text-driven image editing comparison PIE-Bench (Ju et al., 2024) based on Diffu- sion models. We evaluate our proposed Uni-Edit using SDXL (Podell et al., 2023). We keep the same typer-parameter setting with our main experiments (f.e., 0.6 and 5), and adopt 50 and 15 as steps. Besides tuning-based methods are marked in gray, the best and second best results are bolded and underlined, respectively.
Struc.
BG Preservation
CLIP Sim.
Method
Model
Steps NFE
Dist. 10a
PSNR
LPIPS 0a)
MSE
SSIM 10a
Whole Edited
104
Null-Text Inv
Diff. Diff.
13.44
27.03
60.67
35.86
84.11
24.75
21.86
50
ReNoise
27.11
49.25
31.23
72.30
23.98
21.26
50
P2P
Diff. 69.43
17.87
208.80
219.88
71.14
25.01
22.44
50
100
PZP-Zero
Diff.
61.68
20.44
172.22
144.12
74.67
22.80
20.54
50
100
PnP
Diff. 28.22
22.28
113.46
83.64
79.05
25.41
22.55
50
100
PnP-Inv.
Diff.
24.29
22.46
106.06
80.45
79.68
25.41
22.62
50
100
EditFriendly
Diff.
24.55
91.88
95.58
81.57
23.97
21.03
50
100
MasaCtrl
Diff. 28.38
22.17
106.62
86.97
79.67
23.96
21.16
100
IniEdit
Diff. 13.78
28.51
47.58
32.09
85.66
25.03
22.22
12
72
Ours
Diff.
15.59
25.64
78.83
43.05
83.42
26.33
22.78
15
28
Similar Uni-Inv, since our proposed Uni-Edit is completely sample-based and model- agnostic, it is also capable of migrating to diffusion models effortlessly. We adopt the SDXL RRealVisXL.Va 0) (Podell al., 2023) as our base model, conducting evaluation experiments on PIE-Bench (Ju et al., 2024). The results are shown in Tab. E.4. Here we enumerate the previous SOTA of diffusion-baase baasd approaches, wherein compared to the manuscript, we additionally present the results of tuning-based inversion (Mokady et al., 2023; Garibi et al., 2024) applied to editing. In contrast to these approaches. the CLIP similarity metrics exemplify our proposed Uni-Edit's capability to drive the diffusion-baset editing to heights and maintain highly competitive back- ground preservation. Meanwhile, we significantly improve the editing efficiency by reducing NFE extremely compared previous fffffusion-bseed approaches. These experiments strongly demon- strate the effectiveness, adaptability, and generalizability of our proposed approaches, providing new insights into image inversion and editing in the era of flow models.
Furthermore, there are still many training-based methods (Wu et al., 2024; Brooks er al., 2023; Shi al., 2024; Wei et al., 2024) to learn how reasonably edit images from the provided training data. Most of them focus on conducting flexible editing through user-provided instructions. Such ideas are very practical and effective. Nonetheless, before embarking on this kind of approach, is crucial to clearly learn about the properties of the base generation methods. This is also the main concentration of this paper.
F DIVERSE APPLICATIONS
In addition to general text-driven image editing, the interpretable design of our method enables a wide range of applications. Fig. F.4 showcases its use for sketch-to-image (Ist line) and stroke-to- image (2nd line) tasks (Yu et al., 2015; Chowdhury et al., 2022). For these applications, we set a 0.8 to enhance the editing effect. By exploiting the binary nature of sketches and fixing m, their grayscale value, we achieve more robust results for sketch-to-iman to-image tasks. Moreover, thanks to the advanced flow matching- based video generation model Wan (WanTeam al., 2025), further test Uni-Edit on video editing tasks. We directly consider the latent containing the temporal dimension Z, and apply Uni-Edit to Wan's sampling process without any modification. Setting 0.8 and
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Pigeon
Sandwich
Candle
Bridge
Kitchen
Tower
BMX bike Motorcycle
Koala Cat
Figure F:4: Diverse application of Uni-Edit. The top is sketch to image, and the bottom stroke image. The left of image pair is the source image, and the right is the editing result.
N = 25, we achieve reliable editing results (3rd and 4th parts) using Wan2. --V--- 33 model. These results further highlight the generalizability and effectiveness of our approach.
G APPLICATION UTILIZING DIVERSIFIED PLUGINS
Since our proposed method model-agnostic, various plugins that can insert flow models can be applied to provide different editing conditions or to achieve specific editing objectives. These plu- gins can generally provide new conditions or help enhance controllability enabling image editing to meet different specific needs. Therefore, in the era of rapid development of generative models represented by flow models, our method can stably and continuously integrate into stronger models or more complex tasks.
G.1 INTRODUCING OF NEW CONDITIONS
Many previous works achieve personalized generation based on images by inserting image features into prompt embeddings attentions, among which IP-Adapter (Ye al., 2023) of the most

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Reference Style
Source Image
Figure G.5: Applications of Uni-Edit utilizing IP-Adapter (Ye et al., 2023; Team, 2024) for reference-based style transfer. The first column is the source image, and the first row the refer- ence style image.
representative approaches. Taking inspiration from this, we first attempted to apply an IP-Adapter that facilitates style transformation (Team, 2024) to our pipeline. During the editing process of Uni- Iaeee--I--aappee
Edit, we load InstantX maaatX/FFAn X/FXXX. between source and target conditions by distinguishing among different image inputs.
into the FL.UX model and differentiate
Specifically, we first employ the original Uni-Inv conditioned on null text, and then modify the vy and of Uni-Edit. After adopting vo Zz Cru: Cing), where Cing denote the input image of the IP-Adapter. Subsequently, in or- der to make the image editing focus on style transfer, keep the text conditions of of and ef' consistent with c without introducing any changes to the content:
IP-Adapter, velocity function becomes
2 = Ugl Zt, L, G Ging); of vo (Z, l
Cixts Gimg
(G.20)
Fig. G.5 shows the results of style transfer using proposed Uni-Edit on innjeeeter-ijjeeted FLUX model. We set a = 0.6, w 5.0, N = 15 for Uni-Edit here. We adopt the source image eiyy and the reference style image 'ing It be clearly observed that the editing results accurately capture the style of the reference style image (such 3D rendering style, colorful style, etc.). At the same time, our method does not cause excessive damage to the source image, allowing the edited results maintain both the targeted style features and the original content.
In addition, under the same paradigm, we have also adopted another type of IP-Adapter, InstantChar- acter (Tao al., 2025), which has the ability customize the characters in the generated images. T We utilize the source image as e and the reference character image as Cng to achieve image char- acter editing, and set a = 0.7, w = 3.0, N 30 here. The results are shown in Fig. G.6, which showcases the abilities of our method using InstantCharacter for effective face editing.
These experiments not only demonstrate the strong flexibility and diverse application scenarios of proposed method, but also illustrate the promising scalability of such sampling based strategy.
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Source
Image
ID
Image
Source Image
ID Image
Figure G.6: Applications of Uni-Edit utilizing InstantCharacter (Tao al., 2025) for face edit- ing. In each group, the upper left is the source image, the lower left is the reference character image, and the right the editing result.
G.2 ENHANCEMENT OF CONTROLLABILITY
Previous work has also proposed many modules that inject additional control conditions, among which ControlNet (Zhang et al., 2023) is the most representative. We can introduce effective control during the editing process of Uni-Edit by treating these injected conditions as part of the velocity function, i.e.:
4 (Z., || Cra) = vo Zz, Cixt: Cen))
(G.21)
where Cant denotes the input condition of the ControlNet. By replacing vp in Alg. 2 with 1/g it can be ensured that the control conditions are preserved the image during the inversion and editing process.
Fig. G.7 shows the editing results with enhanced controllability. We utilize Stable Diffusion with Canny Canny-conditioned ControlNet (InstantX/Si SateeaatttsD3/oCoooae roinet
as the base model for our Uni-Inv and Uni-Edit, and set a 0.9, w 5.0, N 30. These images are from the GTAV dataset (Richter et al., 2016), and the Canny edges used for control are the Canny edges of segmentation labels these images. We utilize null text as the source prompts and the word describing environment ("Snowy", "Rainy", "Foggy", and "Night") as the target prompts in these experiments. The results indicate that after introducing the control of ControlNet, Uni-Edit exhibits strong semantic information retention abilities and also achieves significant editing of environmental features the image. This application can be used autonomous driving scenarios or world model building processes to provide more diverse while reliable data for training semantic segmentation and detection recognition models.
H ADDITIONAL. QUALITATIVE COMPARISON
H.1 UNI-INV
We represent more qualitative comparison results our Uni-Inv and recent flow-based approaches (Wang et al., 2024b; Deng et al., 2024) in Fig. H.8 and Fig. H.9. These figures contain wide variety of image samples, including landscape photographs, object photographs, numan-centered daily photographs, photographs in extreme lighting, group photographs of large numbers of people,
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Source Image
Canny of Label
"I "Sowy"
"Rainy"
"Foggy" "
"Night"
Figure G.7: Application of Uni-Edit for reliable environment style transformation for au- tonomous driving tasks using ControlNet (Zhang et al., 2023). The first column is the source image, and the second column is the reference canny image, which is obtained from the ground truth segmentation label of the source image. The first row provides editing prompts of such editing tasks (only one discription word is enough).
black and white photographs, posters, pencil drawings, oil paintings, etc. of varying resolutions. Our method well maintains the overall image color (last line of Fig. H.8), texture style (6th line of Fig- H.8), content details including text (8th and 9th lines of Fig. H.9) during inversion & reconstruction, achieving consistent superiority both conditional and unconditional settings.
H.2 UNI-EDIT
We further perform additional qualitative comparisons with existing state-of-the-an methods (Hertz al., 2022; Tumanyan al., 2023; Ju et al., 2024; huberman-Spicgelllae
al., 2024; Cao al., 2023; Xu et al., 2024a; Rout et al., 2024; Wang et al., 2024b; Deng et al., 2024) on text-driven image editing as shown in Fig. H. Fig. H.ll, and Fig. 12. We extensively compared the different approaches under conditions of editing categories, materials, properties, motions, backgrounds, and types of adding or removing items or concepts, as well as stylization.
First, in the task of regional editing. our method demonstrates significant local perception and back- ground preservation capabilities. It worth noting that our approach model-agnostic, i.e. it does not require the involvement of the attention mechanism. This leads to more oriented regional editing. For example, attention-base methods such as PnP (Tumanyan et al., 2023) often impose attributes that need to be used for regional editing on irrelevant regions (4th line of Fig. H.ll). Due to these methods disassembling prompts and attempting to utilize a single token about the editing to exert guidance, inappropriate semantic understanding comes since the wholeness of prompts is destroyed. On the contrary, our approach is model aggossi,,, thus better capitalizing on the text comprehension capabilities learned from the model.
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Source
RF-Solver
FireFlow
Ours
RF-Solver
FireFlow
Ours
ACKITT
shurlesbock
Unconditional
Conditional
Figure H.8: Additional qualitative comparison inversion & reconstruction the Conceptual Captions validation dataset (Sharma et al., 2018).
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Source
RF-Solver
FireFlow
Ours
RF-Solver
FireFlow
Ours
ALUTIL EC
Unconditional
Conditional
Figure H.9: Additional qualitative comparison on inversion & reconstruction on the Conceptual Captions validation dataset (Sharma et al., 2018).
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Source
72P
PoP
PnP-Jnv.
InfBdis
RF-Jaw
RF-Solver
FireFloow
Ours (PLUX0
Ours (SD3)
9% Rousty
Car
N Mouth
Stoke
Egess
Salnson
Angel
Crystal
Dress
Figure H010: Additional qualitative comparison on image editing PlE--ench (Ju al., 2024).
Subsequently, compared to the same compling-based kind of approaches (i.e., RF-Inversion (Rout et al., 2024), RF-Solver (Wang al., 2024b), and FireFlow (Deng et al., 2024)), our method demon- strates significant advantages in terms of image structure and background preservation while main- taining robust editing (Ist and 9th lines of Fig. H.10, 3rd and last lines Fig. H. 1, 3rd and 7th lines of Fig. 12) On the one hand, this is due to proposed Uni- Inv theoretically ensuring small local error in the inversion process that can support accurate reconstruction. On the other hand, our deep exploration and re-empowerment of delayed injection make it easy for proposed Uni-Edit to strike satisfying balance hetween editing and the preservation of aditing-irrelevan concepts.
I ADDITIONAL RESULTS ON EDITING TASKS
1.1 IMAGE EDITING
Fig. L.1 and Fig. I.14 represent additional qualitative results our proposed Uni-Edit on image editing tasks. These results indicate that when it comes to diverse targets and diverse image domains, our approach still remains very effective. It is worth noting that each edit in Fig. 1.13 contains multiple different editing objectives (e.g., changing the time, removing the crowd, and adjusting the lighting). Our approach is able to simultancously achieve these various targets single round, using only the original prompt and the target prompt guidance. Benefiting from the sampling-
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Source
F2P
PaP
PnP-Inv.
larBdii
RU--Jaw
RE-SSllver
FireFllow
Ours (FLUX
Ours (SD3)
Angry
Glass
Makeup
Sad Morkey
Candy
Rose
Grrafiiie
Earring
Bird
Figure H.11: Additional qualitative comparison on image editing PIE-Bench (Ju al., 2024).
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Source
P2P
PaP
PnP-Jnv.
InfBidii
TUP-Jav
BJ -T--Sleee
FireFllow
Ours (FLUX)
Ours (SD3)
Sculpture
Metal
Skow
Galaxy
Pixel Art
Staines
Comic
Digital Art
Sketch
Watercolor
Figure H.12: Additional qualitative comparison on image editing PIE-Bench (Ju al., 2024).
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based design, approach is able to capture diverse objectives once through the relationship between the latents obtained from different conditions. Compared to methods that rely on cross attention manipulations, this design is simpler, more robust, and less likely cause confusion.
1.2 VIDEO EDITING
Moreover, we directly adopt Uni-Edit to conduct video editing tasks using the flow matching baashing-bbase video generation model Wan (WanTeam al., 2025). Qualitative results are shown in Fig. 1.15. Since our method is model-agnostic, we can achieve reliable video editing results without additional design or complex parameterization. is further strong evidence of our approach's generalizability.
LIMITATIONS AND FUTURE WORKS
The core issue plaguing us now is that our Uni-Edit designed for image-text pair inputs. is not capable of accepting more than one image the condition. This results in direct way for to contribute to the personalization generation problems. the future, we would like to develop editing methods that are more general and oriented more diverse tasks. The accurate inversion of Uni- Inv helps to capture image information. With this facilitation, we hope to develop sampling- -asse editing strategies capable of injecting image conditions based re-enabled delayed injection framework. We leave as an interesting future work.
K LLM USAGE STATEMENT
In this paper, LLMs were not used for polishing writing, discovery and retrieval, research ideation, and other aspects. All paper writing, scientific content, and interpretations the authors'
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Change into vibrant raindow
superkaro.
Reptaos the bicycle motorbike.
Replace bananas
camera magical orystal
Add rainbow over the cityscape.
Change interior magical forest setting.
Replace the beach with show mountain.
Replace the green apple with tennis ball.
Tkr into snowy winter wanderland.
Adla window lookover snaway mountain.
Change the dog into dolphin.
Replacs the tanmis racket with lightsaber:
Add ancient Egyption Kieraglaphies
Hose
Give piselated, b-bit video look.
the chicken into splcy sausage.
dag with pink kat.
Figure I.13; Additional qualitative results on image editing on UltraEdit dataset (Zhao et al., 2024) and wild images. each image pair, the left the original image and the right the result of our editing. The text captions at the top of images are descriptions of the editing objectives and are not the input to the model. We still maintain the paradigm of using the original prompt and the target prompt as conditions. These images are obtained by FLUX using Uni-Edit with 0.6, and step = 15 which is consistent with the main experiments.
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the hammerr polish metal to shine, and replace the worn handle with wooden
Change the time of day to night, crowd.
market appear closed, and adjust the lighting to reflect quieter
atmosphere.
Colorize the sketch and add transform it life oil painting.
Transfonm the cat into mechanical version glowing blue eyes.
Change the season to spving by adiing blooming flowers ground and the trees, increase number of pigeons and add more flying in the sky.
Change weather to dramatic storm with dark, swirling clouds and muttipie lightning strikes kitting the sea.
Remove the engravin
the
gold name
Transform the cottage into half-timbbeed house with white walls dark wooden beams. Replace the landscape with vibrant cherry blossoms in full bloom.
tag.
Change the
reflection
on the
teapot
show clear blue sky
instead of
the bright
kitchen
window.
Figure L.14: Additional qualitative results on image editing on HQ-Edit dataset (Hui al., 2024).
The visualization setup is the same as Fig. I.13.
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F + Sung lasses
Rock
- Crystal ball
W Snowy sky
A Snowy like
Scooter
Hockey Sweep
Hot
Figure L.1: Additional qualitative results on video editing on DAVIS dataset (Pont-Tuse et al., 2017). We set a 0.8, 5 5, N 25 for the experiments.
21


Reviews:
[{"review_id": "UVfV4zKpo5", "reviewer": "Reviewer_J9uw", "summary": "The paper introduces UniEdit-Flow, a training-free, model-agnostic pipeline for flow models that tackles (i) exact inversion problem that aims to transitions from a data to the corresponding latent via a predictor–corrector scheme (Uni-Inv) that aligns an implicit-Euler step and proves an bound for error of the proposed method, , and (ii) editing method (Uni-Edit) using region-adaptive and delayed injection for locality preservation while changing the target subject.", "questions": "– Please define $v_{i}^{F}$ when it first appears and clarify how it\nis constructed from $v_{i}^{S}$ and $v_{i}^{T}$ (e.g., fusion rule,\nweights, timing), or at least indicate what it is and where it will\nbe specified, rather than only in the Fig. 3 caption.\n\n– Specify precisely what “current editing velocity” refers to at the step that updates $\\tilde Z_{t_{i-1}}$ and provide the explicit update equation.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "– Mostly well written and easy to follow. \n\n– Problem and solution are well-motivated; the design is simple yet effective. \n\n– Impressive results: consistent gains for inversion (Table 1) and editing (Table 2), with strong qualitative examples on SD3 and FLUX. \n\n– Demonstrations across varied applications suggest good generality and a principled approach.", "weaknesses": "– The transition from §4.2 to §4.3 is hard to track. In Fig. 5 and the surrounding text, a correction step uses $v_{i}^{S}$ to move the latent to a higher-noise state, then $v_{i}^{T}$ to correct under the new prompt; however, it’s unclear which velocity is applied next—the phrase “apply the current editing velocity to move the latent to $\\tilde Z_{t_{i-1}}$ is ambiguous.\n\n– Fig. 5 introduces $v_{i}^{F}$ without prior definition; its relation to $v_{i}^{S}$ and $v_{i}^{T}$, and its role in updating $\\tilde Z_{t_{i-1}}$, are not explained at that point in the main text. I would prefer an explicit introduction in the text rather than only in the caption (as in Fig. 3).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "efqxeeHc7g", "reviewer": "Reviewer_aUpR", "summary": "This submission proposes novel inversion and editing methods specifically designed for flow-based models. For inversion, the paper introduces Uni-Inv, which incorporates an additional correction procedure prior to the inversion step. For editing, leveraging delayed injection, the paper presents Uni-Edit, featuring a predictor-corrector mechanism and a velocity fusion step to enable effective edits while preserving regions irrelevant to the desired changes. The key contributions of this work are the introduction of an extra correction procedure in both inversion and editing processes, as well as the use of velocity fusion in the editing step.", "questions": "1. What is the difference between “m^{in Corr.}_i = 1” and “w/o Corr.” in the ablation study presented in Table C.1? Both configurations appear to represent Uni-Edit without Correction, yet they yield significantly different results in PSNR/SSIM and CLIP similarity. How can this discrepancy be explained?\n\n2. A more detailed analysis of these key components, supported by visual examples, should be provided to better elucidate the ablation study results. Specifically, it would be helpful to understand why certain metrics increase or decrease when specific components are removed.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper introduces a predictor-corrector procedure to enhance performance in both inversion and editing tasks. This approach is original and has been extensively evaluated. The method employs delayed injection, which is a widely used technique in image editing and is not an original contribution of this work. The velocity fusion technique, adapted from prior research on region-aware editing, is incorporated into the proposed method. Overall, the paper achieves a satisfactory level of originality and has the potential to inspire future research.\n\n2. The submission presents comprehensive and well-designed experiments, particularly in the Appendix, demonstrating the advantages of the proposed method over baseline approaches in both inversion and editing. Various backbone models and conditioning scenarios are considered and evaluated. In addition, the authors extend their method to video editing and diffusion models, showcasing its flexibility and generalization capabilities.\n\n3. The paper is clearly written and well organized. Figures such as Figure 3 and Figure 5 help readers better understand the algorithm through effective demonstrations and diagrams. Overall, the readability is excellent.", "weaknesses": "1. The main concern lies in the ablation study of key components, which is crucial for verifying their effectiveness. The submission presents an ablation study in Table C.1. For unedited regions, metrics such as PSNR and SSIM appear reliable for measuring the accuracy of background preservation. However, regarding the edited regions, relying solely on CLIP similarity may be insufficient to fully demonstrate editing performance. Including human evaluation or visual results in the ablation study would provide a more comprehensive assessment.\n\n2. The implementation of “w/o Uni-Inv” is unclear. To effectively ablate Uni-Inv, experiments similar to those in Figure 4 with different velocity settings could be conducted in the evaluation.\n\n3. Additionally, velocity fusion does not appear to have a significant impact. Its removal results in only a slight decrease in PSNR and SSIM, with CLIP similarity remaining almost unchanged.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Pzndo0fPS1", "reviewer": "Reviewer_xe1o", "summary": "The paper proposes a new method for image inversion and then editing for flow based models. Compared with previous DDIM inversion style 1st order inversion, the proposed method can be as a high order method. Results show improvement on image editing benchmark PIE-bench.", "questions": "I include the questions in the weaknesses part. Currently I feel the performance is good but I do not fully understand the difference between the proposed method and other high-order methods (or Is it just a reuse of heun-like method for inversion?). \n\nI'll consider to increase my score if my concerns are addressed in the rebuttal.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. In general, the paper is well-written and easy to follow.\n2. The results on PIE-Bench are quite good compared with previous methods.", "weaknesses": "1. Overall the proposed inversion method seems a special type of Heun. The difference is: Heun uses the average slope (algo 1 line 3 in the \"for\" loop) while the proposed method only uses the corrector slope. \n\n2. Continue from 1, therefore, I do not fully understand why the proposed method can do better than Heun. Based on Prop 4.1 the local error is of O(t^3). Heun should have the same bound. But based on the visualizations and results from the tables, Heun is not as good as the proposed method. \n\n3. Can you explain appendix section D. Why the inversion method should be related to samplers? The purpose of inversion and inversion based editing is to find the noise given an existing real world image and we are not suppose to know how the image is generated. \n\n4. Continue from 3, what is the data you used in Table 1? Are they generated or real world data? Are they generated with a certain type of solver? \n\n5. Some of the claims are questionable. For example, the trajectory of flow models is not straight, only the conditional trajectory is straight.  \n\n6. The authors may consider to discuss some related to works for inversion in flow based models. For example [1][2]\n\n[1] inversion free image editing (ICCV 25)\n[2] text-to-image rectified flow as plug-and-play priors (ICLR 25)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "do7TvwKJrg", "reviewer": "Reviewer_7YEx", "summary": "The paper proposes Uni-Inv and Uni-Edit. Uni-Inv is a predictor-corrector based inversion method that achieves accurate image reconstruction from latent noise, mitigating the large reconstruction errors seen in vanilla flow inversion. Building on this, Uni-Edit is a region-aware image editing strategy that re-enables the concept of delayed injection for flow models. Both methods obtain SoTA results on inversion and editing respectively.", "questions": "see weakness section.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The logic is reasonable and the writing is good.\n2. Uni-inv and uni-edit applies the design of \"take a step back\" and \"take a step forward\", which has emperically demonstrated strong performance.\n3.  The paper provides a theoretical analysis of Uni-Inv, bounding its local error to $\\mathcal{O}(\\Delta t_{i}^{3})$, which justifies its high reconstruction quality.", "weaknesses": "1. The reliability of the method seems to be dependent on the choice of hyper-parameter, different images might have different choices of hyper-parameter to achieven the best results. I'm pointing it out since this is a general problem for inversion based editing methods.\n2. On the application side, could Uni-Inv's accurate latent noise capture be used in conjunction with a framework like IP-Adapter, applied to the flow latent, to enable this image-conditioned editing?\n3. The region-adaptive mask m_{i} is calculated based on the difference between target and source velocities. Did the authors experiment with incorporating attention maps, as commonly done in diffusion editing, to see if an even more semantically precise mask could be generated?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)