SplitFlow: Flow Decomposition for Inversion-Free Text-to-Image Editing
Authors: Sung-Hoon Yoon
Minghan Li
Gaspard Beaudouin
Congcong Wen
Muhammad Rafay Azhar
Mengyu Wang
Venue: NeurIPS 2025 poster ()
Keywords: Image Editing
Flow Matching
Rectified Flow
Primary area: deep_learning

Abstract: Rectified flow models have become a $\textit{de facto}$ standard in image generation due to their stable sampling trajectories and high-fidelity outputs. Despite their strong generative capabilities, they face critical limitations in image editing tasks: inaccurate inversion processes for mapping real images back into the latent space, and gradient entanglement issues during editing often result in outputs that do not faithfully reflect the target prompt. Recent efforts have attempted to directly map source and target distributions via ODE-based approaches without inversion; however, these methods still yield suboptimal editing quality. In this work, we propose a flow decomposition-and-aggregation framework built upon an inversion-free formulation to address these limitations. Specifically, we semantically decompose the target prompt into multiple sub-prompts, compute an independent flow for each, and aggregate them to form a unified editing trajectory. While we empirically observe that decomposing the original flow enhances diversity in the target space, generating semantically aligned outputs still requires consistent guidance toward the full target prompt. To this end, we design a projection and soft-aggregation mechanism for flow, inspired by gradient conflict resolution in multi-task learning. This approach adaptively weights the sub-target velocity fields, suppressing semantic redundancy while emphasizing distinct directions, thereby preserving both diversity and consistency in the final edited output. Experimental results demonstrate that our method outperforms existing zero-shot editing approaches in terms of semantic fidelity and attribute disentanglement. The code is available at https://github.com/Harvard-AI-and-Robotics-Lab/SplitFlow.

SplitFlow: Flow Decomposition for Inversion-Free Text-to-Image Editing
Sung-Hoon Yoon', Minghan Li"", Gaspard Beaudouin', Congcong Wen!.), Muhammad Rafay Azhar', and Mengyu Wang
Harvard AI and Roboties Lab, Harvard University
École des Ponts, Institut Polytechnique de Paris NNew York University Abu Dhabi
Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University {syoon13, m1i4] @meei harvard. edu, gaspard. beaudouin@eleves enpc.fr, cwen2@meei harvard edu, rafayazhar@collegg harvard.edu. mengyu mengyu_wangdnee wang@meeignee www.aad.aa...................... edu
Abstract
Rectified flow models have become de facto standard in image generation due their stable sampling trajectories and high-fidelity outputs. Despite their strong gen- erative capabilities, they face critical limitations in image editing tasks: inaccurate inversion processes for mapping real images back into the latent space, and gradient entanglement issues during editing often result in outputs that do not faithfully reflect the target prompt. Recent efforts have attempted to directly map source and target distributions via ODE-based approaches without inversion; however, these methods still yield suboptimal editing quality. this work, we propose framework built upon inversion-free
flow wawpppositioonnddgggggeeeee formulation to address these limitations. Specifically, we semantically decompose the target prompt into multiple sub-prompts, compute independent flow for each, and aggregate them to form unified editing trajectory. While we empirically observe that decomposing the original flow enhances diversity in the target space, generating semantically aligned outputs still requires consistent guidance toward the full target prompt. To this end, we design projection and aggr-aggregttion mechanism for flow, inspired by gradient conflict resolution in multi-task learn- ing. This approach adaptively weights the sub-target velocity fields, suppressing semantic redundancy while emphasizing distinct directions, thereby preserving both diversity and consistency in the final edited output. Experimental results demonstrate that our method outperforms existing zero-shot editing approaches in terms semantic fidelity and attribute disentanglement. The code is available at https://githhb. com www.coom///aaeeeeee Haap//arraard-A-a -
Introduction
Flow-based generative models have demonstrated superiority in synthesizing images and also in text-to-image generation task. Building on these advances, recent research has actively explored image editing methods that modify a given image to align with target prompt. Due to the nature of diffusion 20
which generate samples from noise through iterative refinement, editing typically requires inversion step to estimate the corresponding noisy latent representation. However, this inversion process is often imperfect and fails to recover exact latent that reconstructs the original image. As result, the editing process may suffer from semantic drift, visual distortion, or inconsistent attribute manipulation. Even
7 23 and flow-based generative models [ B 15]
Equal contribution.
Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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when a precise inversion allows near pear-etrfect reconstruction, an empirical trade-off arises between reconstructability and editability. Latents that are highly optimized to match the input image tend to be rigid and entangled, meaning that adjusting a single attribute often leads to unintended changes in unrelated features [2].
Recently, some efforts have been made to improve the editing process based on rectified flow models 119 26 29 31 and also inversion-free method that directly maps the source and target distribution but these methods show limited semantic fidelity due to gradient entanglement and prompt-latent misalignmmnt. In particular, when a complex target prompt contains multiple semantic attributes (e.g., "a german shepard dog with black sunglasses jumping on the grass with mouth opened"). single editing trajectory guided by the full prompt often leads to entangled gradients and conflicting directions in the semantic space. This makes it difficult to isolate and control the influence of individual attributes, resulting either under-edited or overly distorted outputs.
Motivated by these issues, we propose SplitFlow, which decomposes the flow induced by the target prompt into independent sub-target flows derived from semantically decomposed sub-prompts. By computing independent editing flows for each sub-prompt and aggregating them into a unified trajectory while mitigating flow conflict, our method achieves improvements in both fidelity and editability. Performance on the PIE-Bench benchmark [TO] demonstrates that the proposed approach outperforms prior methods.
The contributions of this paper are threefold:
We demonstrate that decomposing the editing flow improves the fidelity of the edited image. especially in preserving background consistency.
We propose SplitFlow, which progressively approximates the target latent through flow decomposition followed by flow aggregation.
We introduce projection-and -adi--annn-aa-tttiioooo method that aligns sub-tar sub-target flows with the target direction, while preserving their semantic diversity and alleviating potential conflicts velocity field that arise during integration.
2
Related Work
Diffusion and Rectified Flow. Diffusion models 21 18 synthesize images by gradually reversing noising process that transforms data into Gaussian noise. Starting from random noise, they iteratively denoise through learned score functions, generating high-quality outputs over many timesteps. Rectified Flow (RF) [3 15 introduces more stable and efficient generation process via a straight trajectory in latent space.
Text-to-Image Inversion and Editing. Text-guided image editing aims to modify given image to match a target text prompt. Most diffusion-based editing pipelines rely on first mapping the image
back into the model's latent space, typically in the form of Gaussian noise. DDIM [1] enables approximate inversion via deterministic trajectory, but suffers from eumulative errors due to its linear approximation. To improve inversion accuracy, optimization-bels have been proposed, though at a high computational cost. On the editing side, early methods achieve localized edits by fine-tuning diffusion models 12] or manipulating cross-attention maps [6] 24 While effective, these methods still operate within the diffusion framework and depend heavily on either inversion fine-tuning: which are often inefficient for practical use.
approaches []6] 25 g
Recent efforts have explored RF models for editing, which offer more stable and efficient sampling trajectories than diffusion models. RF-Inversion 19 formulates editing as an optimal control problem to balance editability and fidelity, RF-Solver 26 incorporates attention injection to guide editing, and FireFlow (3] employs higher-order ODE solvers to improve inversion accuracy. Despite their advantages, these methods still depend on itcrative re-noising procedures, making them prone to cumulative error. FTEdit [29] reduces errors by performing iterative average at each inversion timestep, which is basically equivalent to increasing the number of sampling steps.
Inversion-Free Editing. Unlike traditional inversion- approaches, which require recovering the noise latent that originally generated the image, inversion-free methods bypass this step and directly optimize in the image or latent space. InfEdit [30] introduces early inversion-free

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framework based on the consistency models [22]. FlowEdit [1 by directly manipulating image-space velocity differences. However, lacks directional selectivity and often struggles with global edits. These works highlight that explicitly modeling semantic flow differences enhances editing control and efficiency.
extends this idea to RF models
Multi-Task and Flow Decomposition. Our method builds upon insights from gradient conflict resolution in multi- learning [32] [7]14 [4], where conflicting objectives are resolved via adaptive re-weighting, Similarly, we propose to semantically decompose the target prompt into multiple sub-flows and adaptively aggregate them for consistent and diverse edits. To our knowledge, our work is the first to incorporate flow decomposition and aggregation into text-based image editing framework.
Preliminaries
3.1 Flow Matching and Rectified Flow
Let IR denote the data space, and æo E d be an initial data point sampled from a source
distribution po- Flow Matching (FM) methods [I5 [3 [ aim to learn time-dependent vector field
(o( t) [0, 1] x Id + Rd, such that the solution to the following ODE transports 2o ~ Po to
I1 P1, where p1 denotes the target distribution:
dz; = (2 t)dt
(I)
The solution Ha describes continuous trajectory defined by the ODE, starting from the initial point zp and reaching the target point xT. The ground-truth vector field v" (xx,, () governs this trajectory and induces a distributional path v, that satisfies the boundary conditions æ=== zi and T==1 21 The objective of FM to Iearn a parameterized vector field ue (01, t) that approximates v (Zz, t) by minimizing the following regression loss:
[()) E.,, ( v² (z,, 1)1
(2)
where 11.12 denotes mean square error. Here, FM enables deterministic sampling via ODEs, avoiding the stochastic noise accumulation seen diffusion models. By leveraging known couplings between distributions, it ensures both interpretability and fidelity. This leads to faster, more stable sampling and a principled connection to optimal transport, making FM strong candidate for generative modeling. Rectified Flow (RF) 15), special case of FM, aims to learn an ODE whose solution closely follows straight- line trajectories between pairs of points sampled from Zg and æ1. The ground-truth vector field is defined as v² (I,, t) 21 To. where is the linear interpolation between zo and T1. The training objective can thus be formulated as:
((0)= = E [10, t) - (x1 20) where x = (1 t) + tæ
(3)
By eliminating the need for stochastic sampling and instead following deterministic linear paths, RF enables faster and more stable performance in downstream tasks such as ODE-based generative modeling and image editing.
3.2 Inversion-free Image Editing
Image editing aims to transform a source image x guided by source text prompt sp"he, into an edited image 1gt using a target text prompt gi In RF, the trajectory between noise and image is assumed to be lincar, meaning that the noisy latent at any timestep interpolation. Leveraging this property, inversion free image editing method [1]] bypass the need for latent inversion by estimating the vector field difference between the source and target images at each timestep. This enables the construction of transition path the clean image space, gradually mapping the source image toward the target. Specifically, the target-aligned latent timestep t, denoted as x. can be approximated by:
be obtained via linear
I 20 are + x
src
(4)
The evolution of this trajectory follows the ODE:
dx
FE v (I
tgt
S dt vd ( + are src xrC) dt,
FE
x0 1z
(5)
3

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Source caption:
(0.))
A german shephard dog stands on the grass with mouth [closed]
(Sec. 4.1) Decomposition
Target caption:
german shephard dog |with sunglasses [jumping] on the grass with mouth [opened]
Xi
FEO:
PEXCO
20
F812
FF 3ig
41
LLM
"Write semantic captions that split the larget cuption."
(M or eic)
(Soc. 4.2) Composition
black sunglasses dog jumps grass
(ofot(2) dog with open mouth jump grass
soots: german shepard dog with black sunglass
Ag
3g
Figure 1: Simplified visual illustration of the proposed SplitFlow. Purple, Blue, Orange line indicates independent sub-target flow. The aggregation done certain timestep. After the aggregation, use a single, unified flow. The notation this figure follows the paper.
where the difference in velocity fields guided by the source and target prompts is defined as: of (2 _²("²) Ug( (x ,t, t,, (2:"", (,, (t)o). Since the source image is known, zrre can be directly obtained by linearly interpolating between it and a randomly sampled noise. In contrast, the target image is unknown and cannot be directly interpolated. Therefore, x; is approximated using igt = xz + xt 2:0
the previous timestep FE and Eq. (4) as follows: x
Based on the estimated velocity field difference, the entire editing process can be implemented as an iterative trajectory the clean image space. Starting from the source image, we initialize the path with = _ At each timestep L, the edited latent is updated using the approximated velocity PE _x=E + A (x r.. This iterative procedure continues until the trajectory converges to the desired target image.
difference: #;-1
Method
Long target prompts often contain multiple attributes and complex semantics, resulting in large semantic gap between the source and target in the latent space. This gap makes direct editing challenging, entangled gradients can degrade edit quality. Providing simultaneous guidance for all attributes may lead to conflicting flows, which can cause semantic drift or even failure in the editing process. To address this challenge, we propose SplitFlow, approximates the target latent through flow decomposition (Sec. 4.1) followed by flow composition (Sec. 4.22p Specifically,
editing framework that progressively
first decompose the semantic complexity of the target prompt into a set of sub-target prompts and compute an independent flow for each, enabling latent directional components be isolated and manipulated separately. These sub-flows are then aggregated into unified flow that semantically aligns with the original target prompt. The editing process proceeds along this unified trajectory, resulting in more stable, diverse edits.
4.1 Progressive Target Approximation with Flow Decomposition
In image editing tasks, the semantic gap between a source prompt T and target prompt tat often complex and high-dimensional. Direct transformation from source target may lead to unstable or imprecise editing results. To address this, we propose to decompose the overall semantic transition into sequence of intermediate sub-target prompts (1)))(()) N where is the number of sub-target prompts. This decomposition simplifies the editing task into semantically controllable and progressively guided transformations.
To perform the decomposition, we leverage a Large Language Model (LLM) prompt reasoning engine, represented as function fILM- We construct composite input sequence by concatenating an instruction prompt th, the source prompt * and the target prompt eot, Then we feed the input

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to fLLM to generate sequence of sub-target prompts that represent incremental semantic transitions:
{ y = (+) T = concat (V), " .p
(6)
Each sub-target prompt splat(() captures a localized semantic component that contributes to the overall transformation from spere to so For example, shown in Fig. [ given source prompt yoere "A german shepherd dog stands on the grass with mouth closed" and a target prompt G = "A german shepherd dog with black sunglasses jumping on the grass with mouth opened", and instruction prompt ab = "Write semantic captions that split the target caption." ", the resulting sub-target prompts may be: {<*tat(0) i==1
"A black sunglasses dog jumps grass". "A dog with open mouth jump grass", "A german shepherd with black sunglasses" ). The number of sub-target prompts N is adaptively determined by fILM based on the degree and complexity of the semantic difference. In most cases,
N S 3, which yields compact yet effective semantic trajectory for guided editing.
Following the formulation of the baseline (Eq. sub-target prompt individually. For each sub-target prompt pran()) we define a corresponding flow FF(() governed by an independent velocity field. Specifically, the sub-target flow is expressed as It
5 we extend the framework to handle each
follows:
FE(() z. + zt It
(gg(() x="";
(7)
where =6 denotes the initial latent of the source image, x:fe is the interpolated latents at timestep between the source image and Gaussian noise. Accordingly, the ODE governing each decomposed sub-target flow given by the sub-targer relative velocity field uf (z, (2)21()) xx:r"):
dx. PE( A (x( kgt(()
"z, _xf")) dt, ( (gh(i) _0t1)) = uo(x (96()) , (l)(()) uo (x,""0 L, "o")).
(gt(() is not directly observable during inference, approximate it based on the previously Since st updated latent in Eq. 7 as: st
tg)(()
= x--1 + atre xgre. The decomposition phase starts from
PR()
Mmar and proceeds until Idec-
4.2 Flow Composition
While decomposing the flow, as described in Sec. 4.1 allows us to disentangle the gradients and achieve independent transformations, the ultimate objective in the image editing task is to perform editing aligned with the target prompt faithfully. Therefore, we devise a method to compose the previously generated sub-target flows into unified flow that adheres to the full semantics of the target prompt. Considering the formulation of the ODE as shown in Eq. [ the velocity field computed for each sub-target prompt can be interpreted the latent representation toward the target state. In multi-task learning (MTL), it is well known that gradients from different tasks can conflict, leading unstable optimization or degraded performance on each tasks. To address this issue, various strategies such as gradient projection and orthogonality constraints have been proposed. Inspired by gradient conflict resolution methods [4] in MTL, we propose a method that mitigates interference between sub-target velocity fields while effectively achieving the desired image editing objectives.
gradient-like vector in latent space that guides
4.2.1 Latent Trajectory Projection (LTP).
To enforce global semantic consistency with the target flow while maintaining the diversity of each sub- target flow, we project each sub-target latent (conditioned on (fa((i) onto the target latent (conditioned on gf o. To perform this projection, we normalize the target latent as 2 /1|== FE 1|2- FE(()
Here, the projection of each sub-target latent {x² (B(00) Ji=1 onto .F is computed as follows:
(+)
FF()) (1) 2t FFE) FFE
x
(8)
where the inner product ()) is computed along the channel dimension of the latent. By projecting the sub-target latent onto the target latent, we ensure that the overall editing process remains consistent with the intended semantic shift. We then aggregate the projected sub-target latent to form the projected latent:
proj
p1oj(s) 3
(9)
5

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While both the target latent s and the projected sub-target latents {a. J the same semantic direction, their origins differ fundamentally: the former stems from unified trajectory conditioned on the full target prompt, whereas the latter are partial trajectories derived from sub-prompts and aligned through projection. This approach retains the global coherence of the target trajectory while preserving localized variations introduced by sub-target prompts, enhancing both semantic consistency and editing diversity.
J are aligned along
4.2.2 Velocity Field Aggregation (VFA).
After projection, to further enhance flow diversity, we introduce Velocity Field Aggregation (VFA), which combines the velocity fields of sub-target flows. To quantify the directional consistency among sub-target flows, compute the cosine similarity between their relative velocity fields with respect to the source latent. Specifically, we first compute the relative velocity vector between z; peou(() and
st as follows:
g: of (xppmi(ti : xx7"))
t, sotor(t)) vo(at"u(())
vo(a," ,4 (0
(10)
The cosine similarity S.y between the i-th and j-th sub-target prompt is defined as:
So (0 0z 0r )
u proj((t)) 1 (ap pmuj(t) 2
, 22)0))
(11)
2x rrr to compute cosine similarity. This metric cap- tures the angular agreement between projected velocity directions, thereby reflecting the consistency of semantic changes introduced by each sub-target prompt. By applying the softmax operation to the cosine similarity map IR NxN xxxxxxxxx we obtain a weight map w E R that determines the relative contribution of each velocity field at every spatial location (h, w) in the latent grid:
Here, of (A(0) is the normalized from vf² (x (i)
N
exp (Ejzi i Siy(h,
of (h, w) = wi (n w) rx (1, w), wi(h, w) = Ex
Σ
(12)
x exp (Ejze Sky (h, w))
Combining the projected latent from Eq. ( with the aggregated velocity field ug the latent after aggregation can be updated as follows:
FF
0 dt.
(13)
Since the proposed VFA adaptively weights the sub-target velocity fields based their semantic alignment, it not only suppresses the influence of redundant flows but also emphasizes those with distinct semantic directions. This enables the model preserve editing diversity while maintaining coherent alignment with the target prompt. Also, note that the LTP and VFA are applied at the end of the decomposition phase (1dec).
Mathematical Justification of VFA. Here, we mathematically verify why VFA improves both fidelity and editability over mere averaging by showing:
(g. gavg) 2 1|gavg11.
(14)
where g EK W/ gi and gavy : Ek gi- Here, Sky is denoted as (Éx. By) and as = E, Skj
We first reformulate Eq. 14 in terms of the scores a,, where the LHS is (ō. Gavg) * Σ i==1 wias and the RHS is || K= Σ a; Thus, the proposition is equivalent to proving Ee w,a, 2
R E. az. The proof combines two standard results. First, from Gibbs' inequality, the KL-divergence between the softmax distribution w = {w;} and the uniform distribution
{1/K} is non-negative, which implies: ΣK1 w.a, 2 (g(Z/KK (14 1). where Z = Eg ext Second, applying Jensen's inequality to the convex function f(x)=
gives log log(E(e")) > E [a], which in our context is:
K
log( (//Z = log
a
>
aj
K
1=1

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Table I: Quantitative comparison results PIE benchmark. For each model group (diffusion- baased and flow-bec how-based), the best and second-best values are indicated in bold and underlined, respectively. Ourst is the result with fidelity- fdelity-enhanced setting.
Method
Model
Structure
Background Preservation
CLIP Similarity
Editing
Distance x301 4
PSNR
LPIPS
x300
MSE x100 J 5SIMx:00
Whole
Edited
DDIM
21
Diffasion
PIP
69.4
17.67
208,
219.88
71.14
25.01
22.44
25.41
Null-text 16
Diffasion
P2P
13.44
27.03
60:61
35.86
84.11
24.75
21 86
PuP-Inv
Diffusion
PIP
11.65
27.22
54.55
32,86
84.76
25.02
22.10
25.41
RF-IIversson
19
Flux
40.6
20.82
184.8
129.1
71.92
25.20
22.
RF-Solver 26
Flux
RF-Solver
31.1
22.90
135.81
80.11
81
26.00
88
FireFlow
RF-Solver
28.3
23
120.8
70.39
82.82
Flow Edit
[
Flux
27.7
21.91
111.70
94.0
83
25.61
22. 70
PTEdit 29
SD3.5
AdaLN
18.17
26.62
80.55
40.24
91.50
25.74
27
IRFDS
SD3
62.72
19.61
179.76
FlowEdit [T]
SD3
27.24
22.13
105-46
87.31
83.48
26.83
23.67
FlowEdit [1]
SD3.5
11,80
26.97
$3.68
31.23
89.70
26.18
22.55
SplitFloo((Uus)
83.91
26.96
23.83
SplitFloo(OOrs
SD3
14.55
25.22
68.53
44.96
87.54
26.23
23.01
SplitFlow(Ours)
Chaining inequalities Eq. (14 1) and Eq. (1
directly yields the required result:
K
wsd; 2 log(Z/K) Z//K)
K
1=1
This proof formalises why VFA improves both fidelity and editability: It suppresses conflicts, steering the update toward high-consensus attributes. Empirically, this manifests as higher background preservation and better CLIP similarity in Table [
When the decomposition phase ended, the aggregated latent now follows the ODE formulation of the target prompt as described in Sec. 3.2 The decomposed flow stages facilitate fine-grained attribute manipulation without gradient entanglement, while the final unified flow phase ensures alignment with the holistic editing intent. This hybrid strategy improves editing stability and fidelity by integrating diversity-awe aware adjustments with prompt-level consistency. A detailed algorithmic description of SplitFlow is included in the supplementary material.
Experiments
5.1 Experimental Setup
Implementation Details. To show the effectiveness of the proposed method and for a fair com- parison with the prior works, we employed the commonly used Stable Diffusion (SD3, SD3.5) rectified flow model. By following the protocol of the baseline [1I], we use the same T = 50 steps with Mmar 33, which skips the first one-third steps. The CFG values for the source and target set to 3.5 and 13.5, respectively. The decomposition Tdde is set to 28, which lasts for 5 steps (Mmaa Tdee). To decompose the target prompt, we used Mistral-7B 9
Dataset. We evaluate our method on Prompt-based Image Editing (PIE) Benchmark 100 which contains 700 images of natural and artificial scenes. In PIE benchmark, ten categories span from random editing written by volunteers change image style. Each image includes source prompt, target prompt, editing directive, edit subjects, and editing mask.
Baselines and Evaluation Metrics. As this work focuses on image editing based on rectified flow, we compare with the State-of-The-e (SoTA) editing methods; RF-Inversion [19], RF.
Solver (26). FireFlow B . iRFDS [1I, FTEdit (29. and FlowEdit [II]. rfffusiinnbaseed models such
as DDIM 21
16
10
are also included for comparison. We evaluate our method on PIE-Bench
dataset using standard metrics. For assessing reconstruction quality and background preservation, we report image-level metries: I.PIPS (33), SSIM (27). MSE, PSNR, and Structure Distance [10] To
measure semantic alignment with the target prompt, use CLIP similarity (28).
7

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Table 2: Ablation study PIE-benchmark. Here AVG denotes the case where average is applied for latent trajectory aggregation.
Baseline
LIP
VEA
Structure
Background Preservation
CLIP Similarity
Distance
PSNR
LPIPS
MSE
SSIMx<1=
Whole
Edited
27
22.13
105.46
87.34
83.48
26.83
23.67
AVG
22.28
23.36
92.00
68.26
85.00
26.31
23.67
26.22
22.37
103.58
83.67
83.76
26.93
23.82
25.96
22.45
102.14
81.99
83.91
26.96
23.83
Input
Directinv-P2P
RFsolver
Flow
FlowFdit-Plux
FlowEdit-SDS
SplitFlow lDw
a) Change "plani" "flwwrr
b) Change "dumplings"" to "sushi"
c) Change "red and white" "bluc and green"
d) Add "with hat"
"a single pink flowe growing middle of
Figure Qualitative comparison prompt-based image editing methods. Each row corresponds to specific editing instruction, where the source prompt is modified into a target prompt. From top to bottom, the tasks are: (a) change "plant" to "flower". (b) change "dumpling" "red and white" to "blue and green", (d) add "with hat", (e) delete "a single pink lotus flower is growing in the middle of". The columns show the input image and the results generated by different models, including Directinv+P2P. RFsolver, FireFlow, FlowEdit-Flux, FlowEdit-SD3.. and SplitFlow.
"sushi", (c) change
5.2 Main Results
Comparison with State-of-the-art Methods. To demonstrate the effectiveness of the proposed SplitFlow, we conducted experiments as shown in Table Compared to the FlowEdit [I] and iRFDS [3I), within the same SD3 model, the proposed SplitFlow not only outperforms in preserving background but also in editing quality. To further demonstrate the effect of the trade-off between fidelity and editability, we omit the noise interpolation during the decomposition phase and directly use the source latent zire to better preserve structural details of the input image in the fidelity- enhanced setting". Compared to the state-of-the-art method FTEdit (29], our SplitFlow! fidelity- enhanced variant) achieves superior performance in both Structure Distance and LPIPS, despite FTEdit employing stronger backhone model. Moreover, significantly outperforms FTEdit both CLIP Similarity metrics, demonstrating better alignment with the target prompt. Also, compared

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Table 3: Ablation study of aggregation step M/eec PIE-benchmark.
Structure
Background Preservation
CLIP Similarity
Distance x10
PSNR f
LPIPS x10
MSE x 4
SSIM X10
Whole
Edited f
Baseline
27.24
22.13
105.46
87.34
83.48
26.83
23.67
30
25.99
22.41
102.33
83.06
83.88
26.92
23.79
26.13
22.41
102.26
82.66
83.85
26.90
23.71
25.96
22.45
102.14
81.99
83.91
26.96
23.83
27
25.96
22.44
102.39
82.15
83.91
26.92
23.85
26
25.94
22.44
102.70
82.27
83.91
26.95
23.84
Input
FlowEdit
SplitFlow (Ours)
Editing prompts
Source:
Three giraffes walk line through lush, z0o- like forest path, while another animal near pond.
You
Target: :
through a lush, zoo- like forest path, while a tiger rests near pond.
Three elephants
Source A group of sheep stands on green meadow with lake and mountains in the background
Target: T Three dogs with sunglasses stands on green
meadow with lake and snowy mountains in the background.
The sky is covered with cloud.
Figure 3: Qualitative comparison results with more complex prompts.
to the methods based on Flux such as RF-Inversion 19], RF-Solver 26 FireFlow 3 SplitFlow?
outperforms the prior works. Within the SD3.5 model, SplitFlow demonstrates superior background preservation capability compared to prior works.
Qualitative Comparison. The qualitative comparison results are presented in Fig. 2 Across various scenarios-include change, add, and delete object prompts-our proposed SplitFlow demonstrates superior editability while effectively preserving the background. For instance, as shown in Fig. (d), which involves adding "with hat" portrait of Mozart, other methods fail to generate the hat or distort the original image, whereas our method successfully synthesizes the hat while maintaining the integrity of the source image. Although well known that enhancing editability often compromises fidelity, our approach achieves favorable balance by disentangling gradients within the flow during the editing process with decomposition. Fig. 3 to further demonstrate the effectiveness of our method, we provide qualitative comparison results with more complex scenario. In the first row, given the source prompt "Three giraffes walk line through a lush, zoo-like forest path, while another animal rests near pond," the editing prompt requires changing the three giraffes elephants and the other animal to a tiger. While Flow Edit fails to convert "three giraffes" to "three elephants," our method successfully performs the transformation and better preserves the semantic detail of "in a line." " Additional qualitative results under complex scenarios are provided in the supplementary material.
5.3 Detailed Analysis
Component Analysis. To validate the effect of the designed component, we conducted an ablation study on PlE-benchmark shown in Table The result of simply averaging the individual flows after decomposition is reported in the second column of Table 2 denoted as AVG. Interestingly, even naive averaging sub-flows maintains CLIP similarity on par with the baseline (II), while significantly improving background preservation, as reflected in metrics such as PSNR, LPIPS, MSE, and SSIM. We attribute this the semantic decoupling effect of sub-flows. which localize edits to specific attributes and reduce unintended changes in irrelevant regions. While semantic decomposition and flow separation are key contributions of our work, our overarching objective extends beyond fidelity enhancement. Our goal is to strike balance between fidelity and editability. ensuring that complex, multi-attribute prompts are both faithfully represented and accurately reflected in the edited outputs. To this end, our proposed components -Latent Trajectory Projection (LTP) and Velocity Field Aggregation (VFA)-g go beyond averaging by explicitly aligning sub-flows with the global editing direction and adaptively weighting their contributions. By applying LTP to align each

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fub-flow with the target flow, we observe meaningful improvements in CLIP similarity, particularly in the Edited metric, which is computed over the foreground mask. Although LTP results in lower background preservation compared to simple averaging, it still outperforms the baseline in all metrics. Furthermore, when VFA applied, each sub-flow contributes more effectively to the final trajectory. This not only enhances background preservation- -imilar to the averaging strategy strategy-but also improves CLIP similarity by promoting global semantic alignment while preserving diversity across sub-prompts.
Ablation Study on Aggregation Timestep. In Table we conduct an ablation study on the aggregation timestep Ndee to evaluate the effectiveness and robustness of SplitFlow. Across all tested configurations, SplitFlow consistently outperforms the baseline. As T]dee decreases- corpessseecrrrsoodiinng to longer decomposition period- we observe improved editability at the expense of background preservation. Considering this trade-off, we set Tldee = 28 for our final configuration. Compared to the baseline, the total number of steps required by SplitFlow can be calculated as N x (1maa -]dee) Mmar. Since N < most cases, the inference steps can be approximated as 3 x (33 -28)+833+ +-33 = 48. In practice, FlowEdit requires 57 minutes for inference. whereas SplitFlow takes 83 minutes to process 700 images on the PIE Benchmark. Additionally, prompt decomposition using an LLM takes approximately 20 minutes. Additional detailed ablation studies on LLM, cost analysis are provided in the supplementary material.
Limitations potential limitation of our method lies in its dependence on the decomposition of the target prompt. Since the editing flows are derived from sub- tob-prompts, the quality and characteristics of the final output can vary depending on the choice of LLMs. Although SplitFlow incurs higher inference time than the baseline, we emphasize that the LLM serves only as proxy to facilitate our main cnntribution-ddmmonsraan
that decomposing the editing process into sub-target flows can significantly improve image-editing performance-ane and proposing a principled method to aggregate these flows effectively. By showing that flow decomposition substantially enhances both fidelity and editability in image editing, this work also opens up new directions for future research. These include developing more effective prompt decomposition techniques using LLMs or vision-language models, as well as exploring optimization-benn based approaches to mitigate gradient conflicts during flow composition. Additional discussions, including analyses of extreme cases, are provided in the supplementary materials.
Conclusion
In this paper, we proposed SplitFlow, flow decomposition and composition framework designed to address gradient entanglement and semantic conflict that arise in image editing with complex and multi-attribute target prompts. SplitFlow computes independent editing flows for each sub-target prompt and forms a unified trajectory through projection and aggregation, thereby maintaining semantic alignment while mitigating interference between attributes. To this end, we introduced two aggregation strategies: Latent Trajectory Projection (LTP), which aligns the directional components of the latent trajectory to ensure coherence with the global target semantics, and Velocity Field Aggregation (VFA), which adaptively integrates sub-target flows while preserving their semantic diversity. These components enable SplitFlow to effectively balance fidelity and editability- often confficting objectives in image editing. Extensive experiments on the PIE-Bench benchmark demonstrate that our method consistently outperforms existing approaches in both visual quality and prompt alignment. Our results confirm that decomposing the editing process into semantically meaningful flows and carefully reassembling them provides a promising direction for accurate, and high-quality text-guided image editing.
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Reviews:
[{"review_id": "cxQ1A8771A", "reviewer": "Reviewer_jnDU", "summary": "This paper focuses on the limitations of rectified flow models in image editing tasks, namely inaccurate inversion processes and gradient entanglement issues. To address these, the authors propose a flow decomposition-and-aggregation framework. The approach semantically decomposes the target prompt into sub-prompts, computes independent flows for each, and aggregates them. A projection and soft-aggregation mechanism is designed to adaptively weight sub-target velocity fields, suppressing redundancy and emphasizing distinct directions. Experiments show the method outperforms existing zero-shot editing approaches in semantic fidelity and attribute disentanglement.", "questions": "- What is the latency of the proposed method, and how does it perform compared to other methods?\n\n- What is the fidelity-enhanced setting?\n\n- What is the performance of applying the proposed method to other base models, such as SD 3.5, Flux, etc?", "limitations": "- The major issue of this paper is the lack of theoretical analysis for the proposed flow decomposition.\n\n- Through multiple sub-target flow generations, the inference time would be largely increased.", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths ###\n\n- This paper focuses on the limitations of rectified flow models in image editing tasks, and proposes a valid solution to address inaccurate inversion processes and gradient entanglement issues\n\n- Experiments show the method outperforms existing zero-shot editing approaches in semantic fidelity and attribute disentanglement.\n\n### Weaknesses ###\n\n- The major issue of this paper is the lack of theoretical analysis for the proposed flow decomposition and why it can handle gradient conflict.\n\n- Through multiple sub-target flow generations, the inference time would be largely increased.\n\n- In Table 2, it seems that only averaging the sub-flows can obtain much better results than the proposed method in terms of the structure and background preservation.\n\n- The paper's writing requires refinement. For instance, while the notation $x_t$ is familiar to experts in the field, the authors should explicitly define its meaning, particularly for $x^{tgt}_t$ and $x^{src}_t$ in Equation 4, instead of deferring the explanation to Equation 7.", "was_revised": true, "final_justification": "The reliance on the LLM for prompt decomposition significantly constrains the method’s upper performance bound; besides, incorporating the theoretical proof would render the paper substantially different.", "rebuttal": "We sincerely thank the reviewer for the constructive comments and suggestions. We hope that our responses below adequately address the concerns raised.\n***\n`Weakness 1: The major issue of this paper is the lack of theoretical analysis for the proposed flow decomposition and why it can handle gradient conflict.`\n    \nHere, we mathematically verify **why Velocity Field Aggregation (VFA) improves both fidelity and editability**. Since the proposed Latent Trajectory Projection (LTP) is inspired by gradient conflict resolution techniques in multi-task learning, a theoretical justification for this approach can be found in Appendix A of [32] Gradient surgery for multi-task learning.\n\n**Recap.** For each sub–prompt $k \\in\\{1,\\dots,K\\}$, we denote the *relative velocity field* at time $t$ by\n$$g_k := v^{\\Delta(k)}(x_t^{tgt(k)},x_t^{src}) = v_{\\theta} (x_t^{tgt(k)},\\varphi^{{tgt}(k)}) -v_{\\theta}(x_t^{src},\\varphi^{{src}}). $$ \n\nFollowing Eq.~(12) in the main paper, we set $\\bar{{g}} = \\sum_{k=1}^{K} w_k\\ {g}_k$, where the weights are given by $w_k= a/b$, here\n\n$a = \\exp (\\sum_{j=1}^{K} \\langle\\hat{g}_k, \\hat{g}_j \\rangle )$,  \n\n$b = \\sum_{i=1}^K \\exp(\\sum_{j=1}^K \\langle \\hat{g}_i, \\hat{g}_j \\rangle) $.\n\n**Goal of proof.** To quantify the effectiveness of our softmax-weighted aggregation strategy in VFA, we compare the resulting velocity field $\\bar{g}$ against the naive average $g_{avg} = \\frac{1}{K} \\sum_{i=1}^{K} {g}_i$. Specifically, we compute the directional alignment with respect to each sub-target velocity field $g_k$ via the inner product\n\n***\n`Eq. (1):`\n$$\n\\langle \\bar{g}, g_k \\rangle \\ge \\langle g_{\\text{avg}}, g_k \\rangle\n$$     \n\nThis inequality suggests that $\\bar{g}$ better preserves the semantic directionality of each sub-flow, by adaptively weighting mutually consistent components and downweighting conflicting or redundant ones.\n\n**Lemma 1: Affinity–Weighted Jensen Inequality**\n\nLet $S_{kj}= \\langle \\hat{g}_k,\\hat{g}_j \\rangle$ \n\nand $a_k=\\sum_j S_{kj}$.  For the softmax weights $w_k\\propto\\exp(a_k)$, the following holds\n$$\n\\sum_{j=1}^{K} w_j\\ S_{kj} \\ge \n\\frac{1}{K} \\sum_{j=1}^{K} S_{kj},\n\\quad \\forall\\ k.\n$$\n\n**Proof of Lemma 1**  \n*Step 1. Re–express the equation.*  Let $ Z = \\sum_i e^{a_i} $ and  $ \\bar{e} = \\tfrac{1}{K} \\sum_i e^{a_i} $. Then,\n\n***\n`Eq. (2):`\n$$\n\\sum_j w_j S_{kj} - \\frac{1}{K} \\sum_j S_{kj}\n= \\frac{1}{Z} \\sum_j \\left( e^{a_j} - \\bar{e} \\right) S_{kj}.\n$$\n\n*Step 2. Order of two sequences.*  Because $ e^x $ is strictly increasing, the ordering of  $ \\{e^{a_j}\\} $ is the same as that of $ \\{a_j\\} $.  For fixed \\( k \\), the Gram matrix monotonicity (positive–semidefinite $ \\Rightarrow $ Chebyshev order) implies that $ \\{S_{kj}\\} $ is *also* non-decreasing in $ a_j $. Thus, the two centered sequences  $ b_j := e^{a_j} - \\bar{e} $ and $ c_j := S_{kj} - \\bar{S}_k $  (with $\\bar{S}_k=\\frac{1}{K} \\sum_j S_{kj}$) have the *same sign for every index*:  $(b_j - b_i)(c_j - c_i) \\ge 0 \\quad \\text{for all } i, j.$\n\n*Step 3. Apply the Chebyshev sum inequality.*  For any two equally long sequences with the same order, Chebyshev's inequality gives $ \\sum_j b_j c_j \\ge 0 $. Hence,\n\n***\n`Eq. (3):`\n$$\n\\sum_j \\left( e^{a_j} - \\bar{e} \\right) S_{kj}\n= \\sum_j b_j (c_j + \\bar{S}_k)\n= \\sum_j b_j c_j\n\\ge 0.\n$$\n\n*Step 4. Completion of the Lemma 1 proof.*  Substitute the result from Equation (3) into Equation (1), and divide by \\( Z > 0 \\) to conclude the proof of **Lemma 1**.\n\n**Proof of Eq. (1)**  $\\langle \\bar{g}, g_k \\rangle \\ge \\langle g_{avg}, g_k \\rangle$.\n\nSince $ \\exp(\\cdot) $ is convex and monotonic, applying Jensen’s inequality to the sequence $ \\{a_j\\} $ implies that the softmax weights $ w_j $ place greater mass on higher $ a_j $ values.  Moreover, since $ S $ is a positive semidefinite Gram matrix, we have $ S_{kj} \\ge 0 $, which supports the inequality. Then\n\n$$\n\\langle \\bar{g}, g_k \\rangle = \\sum_{j=1}^{K} w_j\\ \\langle g_j, g_k \\rangle = ||g_k\\|| \\sum_{j=1}^{K} w_j \\ \\||g_j\\|| \\ S_{kj}.\n$$\n\t \nLikewise, the inner product of the baseline can be formulated as follows:\n$$\n\\langle g_{avg}, g_k \\rangle = \\frac{1}{K}\\||g_k\\|| \\sum_{j=1}^{K} \\||g_j\\|| \\ S_{kj}.\n$$\n\t \nThe subtraction of both terms gives:\n$$\n\\langle \\bar{g}, g_k \\rangle - \\langle g_{avg}, g_k \\rangle = \\|| g_k\\|| \\sum_{j=1}^{K} \\|| g_j\\|| (w_j - \\frac{1}{K}) S_{kj}.\n$$ \n\nBecause $S$ is a Gram matrix, $S_{kj} \\ge 0$, and $\\|| g_j \\|| > 0$, we apply Lemma1 to the equation (subtraction). This proves that the softmax aggregation $\\bar{g}$ has an equal or better alignment with every $g_k$ than the naive average. (Q.E.D)\n\n**Interpretation.** This proof formalises why Velocity Field Aggregation improves both fidelity and editability: It suppresses angular conflicts (negative inner products), steering the update toward high-consensus attributes. Empirically, this manifests as higher PSNR and better CLIP similarity in Table 1-2 of the main paper.\n\n***\n`Weakness 2: Through multiple sub-target flow generations, the inference time would be largely increased.`\n\nWe acknowledge this trade-off in our limitations section. The increased inference time stems from our decomposition strategy, which we believe is justified by the **consistent improvements in editability and background preservation**. More importantly, the core contribution of our work is conceptual: by introducing flow decomposition and adapting gradient conflict mitigation techniques from multi-task learning (e.g., LTP and VFA), we provide a **novel perspective for addressing entangled semantics in text-to-image editing**. We hope this direction will inspire future research, particularly in long-form and complex editing scenarios.\n***\n`Weakness 3: In Table 2, it seems that only averaging the sub-flows can obtain much better results than the proposed method in terms of the structure and background preservation.`\n\nWe thank the reviewer for the thoughtful comments. As shown in Table 2, even a naive averaging of sub-flows leads to notable improvements in background preservation and structural fidelity. We attribute this to implicit disentanglement by editing along semantically localized flows, simple averaging can already reduce interference between distinct attributes.\n\nWhile semantic decomposition and flow separation are key contributions of our work, our overarching objective extends beyond fidelity enhancement. **Our goal is to strike a balance between fidelity and editability**, ensuring that complex, multi-attribute prompts are both faithfully represented and accurately reflected in the edited outputs.\n       \nTo this end, our proposed components—Latent Trajectory Projection (LTP) and Velocity Field Aggregation (VFA)—go beyond averaging by explicitly aligning sub-flows with the global editing direction and adaptively weighting their contributions. As a result, SplitFlow not only maintains high fidelity but also significantly improves semantic editability, especially in foreground regions, as demonstrated by the improved CLIP scores.\n***\n`Weakness 4: The paper's writing requires refinement. For instance, while the notation is familiar to experts in the field, the authors should explicitly define its meaning, particularly for and in Equation 4, instead of deferring the explanation to Equation 7.`\n\nWe appreciate the reviewer’s feedback and will carefully revise the paper to improve clarity and readability. Specifically, we will ensure that all notation—especially in Equation 4—is clearly defined at the point of introduction, rather than delayed until later sections.\n\n***\n`Question 1: What is the latency of the proposed method, and how does it perform compared to other methods?`\n\nWe report the latency of our model in Table S2 of the supplementary material, including GPU memory usage and total inference time on the PIE-Benchmark. \n    \n***\n`Question 2: What is the fidelity-enhanced setting?`\n\nThank you for pointing out the lack of clarity regarding the fidelity-enhanced setting. In the baseline method FlowEdit (as described in Lines 125-126 of the main paper), $x_t^{src}$ is obtained by linearly interpolating between the source latent $x_0^{src}$ and a randomly sampled noise. This approximation improves editability in early diffusion steps but may compromise image fidelity to some extent.\n\nTherefore, as mentioned in Line 254 of the main paper, in our fidelity-enhanced setting, we omit this noise interpolation **during the decomposition phase** and directly use the source latent $x_0^{src}$  to better preserve structural details of the input image. For the subsequent unified flow phase, we retain the same settings as the baseline to ensure fairness and comparability of the overall pipeline.\n***\n`Question 3: What is the performance of applying the proposed method to other base models, such as SD 3.5, Flux, etc.`   \n\nWe conducted experiments with a stronger base model—Stable Diffusion 3.5. Our method not only outperforms the state-of-the-art method in terms of PSNR, LPIPS, and MSE, but also achieves significantly higher image editability. Also, compared to the baseline (FlowEdit–SD 3.5), SplitFlow delivers meaningful improvements in both fidelity and editability.\n\n| **Method**    | **Model** | **Distance ×10³ ↓** | **PSNR ↑** | **LPIPS ×10³ ↓** | **MSE ×10⁴ ↓** | **SSIM ×10² ↑** | **CLIP Whole ↑** | **CLIP Edited ↑** |\n|---------------|-----------|----------------------|------------|------------------|----------------|------------------|-------------------|--------------------|\n| **FTEdit**    | SD3.5     | 18.17                | 26.62      | 80.55            | 40.24          | 91.50            | 25.74             | 22.27              |\n| **FlowEdit**  | SD3.5     | 9.43                 | 28.31      | 43.59            | 23.40          | 90.84            | 25.88             | 22.50              |\n| **SplitFlow** | SD3.5     | 9.15                 | 28.44      | 43.10            | 22.93          | 90.92            | 25.95             | 22.51              |"}, {"review_id": "ZzEzKMnEAd", "reviewer": "Reviewer_wb3F", "summary": "SplitFlow presents an inversion-free framework for text-guided image editing in rectified flow models that addresses \"gradient entanglement\" issues in the setting of complex, multi-attribute target-edit prompts. The method decomposes the target edit prompts into sub-prompts using an LLM, computes independent editing flows for each sub-prompt, and then aggregates them through two mechanisms: Latent Trajectory Projection to ensure global semantic alignment, and Velocity Field Aggregation to adaptively weight sub-flows based on their directional consistency. The approach shows qualitative improvement over existing baselines in multi concept editing settings. On the PIE benchmark it shows a some improvement in some of the background preservation and structure preservation metrics, and in text alignment.", "questions": "(1) Gradient entanglement assumption: The paper assumes that \"entangled gradients can degrade edit quality\" when handling multi-attribute prompts, but why couldn't stronger text alignment in future models handle this directly? The assumption may not hold as text-image alignment capabilities improve.\n(2) Cost-benefit analysis: What is the actual computational overhead in terms of wall-clock time and memory usage? Is the performance gain worth the increased computational cost, especially for practical applications?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n(1) Well-motivated problem: The paper tackles multi-concept editing in rectified flow models, addressing gradient entanglement issues that arise when target prompts contain multiple semantic attributes - a practically relevant and challenging problem.\n(2) The decomposition-aggregation approach makes conceptual sense, breaking down complex editing tasks into manageable sub-flows that can be independently computed and then combined.\n(3) Empirical results demonstrate improvements over existing methods in terms of text alignment. \n(4) Comprehensive evaluation --  appropriate baselines and relevant evaluation metrics that indeed suggest SplitFlow offers the best background-preservation and editing alignment trade-off. \n\nWeaknesses:\n\n(1) Computational overhead: The method requires computing N independent flows plus aggregation steps, significantly increasing inference time but lacks detailed analysis of the time-quality trade-off compared to baselines.", "was_revised": true, "final_justification": "After considering the other reviews and the this rebuttal I will maintain my original score of borderline accept.", "rebuttal": "We sincerely thank the reviewer for the constructive comments and suggestions. We hope that our responses below adequately address the concerns raised.   \n\n`Question 1: Gradient entanglement assumption: The paper assumes that \"entangled gradients can degrade edit quality\" when handling multi-attribute prompts, but why couldn't stronger text alignment in future models handle this directly? The assumption may not hold as text-image alignment capabilities improve.`  \n\nWe agree that as flow-based models improve—particularly in their ability to align complex text-image semantics—the severity of gradient entanglement may diminish. However, our assumption is grounded in empirical observations with current state-of-the-art models (e.g., SD3, FLUX), where editing with long or compound prompts **still frequently leads to under-editing** (i.e., missing certain attributes) or **over-editing** (i.e., introducing unintended changes to unrelated regions). We attribute these effects to gradient interference among semantically entangled prompt components, which motivates our decomposition-and-aggregation strategy.  Moreover, as shown in the experimental results on SD 3.5 (Table in the review response of jnDU), which is a more image–text well-aligned model than SD 3, the proposed method still yields **consistent benefits**.\n\n***\n`Question 2: Cost-benefit analysis: What is the actual computational overhead in terms of wall-clock time and memory usage? Is the performance gain worth the increased computational cost, especially for practical applications?`  \n\nAs shown in Table S2 of the supplementary material, GPU memory usage and total inference time were measured on the PIE-Benchmark, which includes 700 images. While the proposed SplitFlow requires 15 additional inference steps compared to the baseline (33 steps), we also measured the actual computational cost. The total inference time is 57 minutes for FlowEdit and 83 minutes for SplitFlow. Additionally, prompt decomposition using an LLM takes approximately 20 minutes. Although our method incurs higher computational overhead than the baseline, it remains efficient overall as it is built upon an inversion-free framework, unlike inversion-based editing methods, which are more expensive. Furthermore, our method achieves substantial improvements in both fidelity and editability."}, {"review_id": "7LUT6dAsnI", "reviewer": "Reviewer_srfy", "summary": "This paper proposes the SplitFlow framework to address two critical issues in flow-based text-to-image editing: imperfect image inversion and gradient entanglement. SplitFlow employs an innovative \"flow decomposition-and-aggregation\" strategy: it first utilizes LLMs to decompose complex target prompts into multiple simpler sub-prompts, then computes independent editing flows for each sub-prompt, and finally intelligently fuses these sub-flows into a unified editing trajectory through projection and weighting mechanisms inspired by multi-task learning (LTP and VFA modules). This inversion-free approach demonstrates superior performance compared to existing state-of-the-art methods on the PIE-Bench benchmark in terms of semantic fidelity, attribute disentanglement, and background preservation.", "questions": "Please refer to the Weaknesses part.", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n\n• **Novel semantic flow decomposition approach**: This is the first work to introduce semantic flow decomposition for text-based image editing, directly addressing the gradient entanglement problem by decomposing complex editing tasks into independent sub-flows.\n\n• **Creative integration of LLMs for image editing**: The use of large language models to guide image editing through prompt decomposition represents an innovative cross-modal approach that bridges language understanding and image generation.\n\n• **Comprehensive experimental validation**: The paper provides thorough evaluation on PIE-Bench benchmark with multiple metrics, demonstrating clear improvements in reconstruction error, prompt alignment, and background preservation compared to state-of-the-art baselines including FlowEdit, FTEdit, and FireFlow.\n\n• **Effective gradient conflict resolution mechanism**: The adaptation of multi-task learning's gradient projection technique (PCGrad/Grad-Surgery) to the generative model domain through LTP and VFA modules is technically sound and well-motivated.\n\n**Weaknesses:**\n\n• **Heavy reliance on LLM decomposition quality without robustness analysis**: The method's performance critically depends on the quality of prompt decomposition by the LLM, yet the paper lacks analysis of failure cases when the LLM produces imperfect splits (e.g., merging attributes or generating unnecessary sub-prompts). No quantitative analysis is provided regarding sensitivity to decomposition quality.\n\n• **Limited computational efficiency analysis**: While the paper mentions computing flows for multiple sub-prompts adds overhead (approximately 48 vs 33 steps), it lacks detailed runtime comparisons or memory usage analysis compared to simpler baselines like FlowEdit.\n\n• **Inadequate explanation of the \"fidelity-enhanced\" variant**: Table 1 shows dramatic improvements for the fidelity-enhanced version with only brief explanation (\"noise interpolation is not applied when forming x_t^src\"), but this significant performance gain deserves more detailed technical exposition in the method section.\n\n• **Lack of statistical significance testing**: All experiments are conducted with fixed seeds without error bars or statistical significance analysis, making it difficult to assess the reliability of relatively modest improvements in some metrics (e.g., CLIP Similarity).", "was_revised": true, "final_justification": "The rebuttal addresses my main concerns. Additional experiments show robustness to different LLM decompositions, detailed runtime/memory measurements clarify efficiency trade-offs, and the fidelity-enhanced variant is now clearly explained. Statistical runs with multiple seeds confirm stability. With key weaknesses largely resolved, I raise my score.", "rebuttal": "We sincerely thank the reviewer for the constructive comments and suggestions. We hope that our responses below adequately address the concerns raised.   \n\n`Weakness 1:Heavy reliance on LLM decomposition quality without robustness analysis: The method's performance critically depends on the quality of prompt decomposition by the LLM, yet the paper lacks analysis of failure cases when the LLM produces imperfect splits (e.g., merging attributes or generating unnecessary sub-prompts). No quantitative analysis is provided regarding sensitivity to decomposition quality.`  \n\t\nWe apologize for the confusion. The impact of LLM decomposition quality is analyzed in the supplementary material (Table S1), where we report experiments using different LLMs (e.g., Mixstral, Qwen2, LLaMA) and two prompting strategies. While some imperfect decompositions were observed, SplitFlow still maintains robust performance across these settings, demonstrating its tolerance to decomposition variations.   \n\nDue to this year's restriction on uploading images during the rebuttal phase, we are unable to present qualitative failure cases resulting from poor decomposition. However, we will include detailed examples and further analysis in the camera-ready version.\n\n***\n`Weakness 2: Limited computational efficiency analysis: While the paper mentions computing flows for multiple sub-prompts adds overhead (approximately 48 vs 33 steps), it lacks detailed runtime comparisons or memory usage analysis compared to simpler baselines like FlowEdit.`  \n\nAs shown in Table S2 of the supplementary material, GPU memory usage and total inference time were measured on the PIE-Benchmark, which includes 700 images. While the proposed SplitFlow requires 15 additional inference steps compared to the baseline (33 steps), we also measured the actual computational cost. The total inference time is 57 minutes for FlowEdit and 83 minutes for SplitFlow. Additionally, prompt decomposition using an LLM takes approximately 20 minutes. Although our method incurs higher computational overhead than the baseline, it remains efficient overall as it is built upon an inversion-free framework—unlike inversion-based editing methods, which are significantly more expensive. Furthermore, our method achieves substantial improvements in both fidelity and editability.  \n\n***\n`Weakness 3: Inadequate explanation of the \"fidelity-enhanced\" variant: Table 1 shows dramatic improvements for the fidelity-enhanced version with only brief explanation, but this significant performance gain deserves more detailed technical exposition in the method section.`.  \nThank you for pointing out the lack of clarity regarding the fidelity-enhanced setting. In the baseline method, FlowEdit (as described in Lines 125–126 of the main paper), $x_t^{\\text{src}}$ is obtained by linearly interpolating between the source latent $x_0^{\\text{src}}$ and a randomly sampled noise. This approximation improves editability in early diffusion steps but may compromise image fidelity to some extent.  \n\nTherefore, as mentioned in Line 254 of the main paper, in our fidelity-enhanced setting, we omit this noise interpolation during the decomposition phase and directly use the source latent $x_0^{\\text{src}}$ to better preserve structural details of the input image. For the subsequent unified flow phase, we retain the same settings as the baseline to ensure fairness and comparability of the overall pipeline.   \n***\n`Weakness 4: Lack of statistical significance testing: All experiments are conducted with fixed seeds without error bars or statistical significance analysis, making it difficult to assess the reliability of relatively modest improvements in some metrics (e.g., CLIP Similarity)`.    \n\nTo ensure a fair comparison, we followed the same random seed as the baseline. Additionally, we conducted three more runs with different seeds to analyze statistical variation. The table below reports the mean and standard deviation across these runs, demonstrating the consistency and robustness of our method.   \n\n**Table: Quantitative comparison results on PIE benchmark with SD3 (mean ± std)**\n\n| **Method**         | **Model** | **Distance ↓** | **PSNR ↑**        | **LPIPS  ↓**   | **MSE  ↓**     | **SSIM ↑**    | **CLIP Wh. ↑**   | **CLIP Edt. ↑**  |\n|--------------------|-----------|----------------------|--------------------|---------------------|---------------------|---------------------|---------------------|---------------------|\n| **FlowEdit**       | SD3       | 27.11 ± 0.15         | 22.18 ± 0.05       | 104.94 ± 0.48       | 86.54 ± 0.72        | 83.54 ± 0.05        | 26.88 ± 0.04        | 23.72 ± 0.06        |\n| **SplitFlow (Ours)** | SD3     | 25.90 ± 0.09         | 22.42 ± 0.02       | 102.48 ± 0.23       | 82.60 ± 0.41        | 83.83 ± 0.05        | 26.93 ± 0.04        | 23.79 ± 0.05        |"}, {"review_id": "2XFBhGXKiS", "reviewer": "Reviewer_JXwP", "summary": "The authors identified that text-based image editing methods struggle with long complicated text prompts. To improve this aspect, they propose SplitFlow. Specifically, they propose to use an LLM to decompose the edit prompt into several edit prompts, performing inversion-free editing path for each of them (based on FlowEdit) following by composing these paths together. The result is a single edited image that is loyal to the text prompt and preserves the background.", "questions": "I didn’t find any reference to the fidelity enhanced version mentioned in Tab. 1 and how it is different from the regular version. This is especially important as this version achieves the best or second-best results in structure preservation.  \n\nThis paper subject is text-based image editing, yet it provides only a few visual examples (only 7 examples in the main text, only 15 overall with the SM). The authors should provide more visual results, especially for long and detailed text prompts, where their method is supposed to excel. In addition, I encourage the authors to add to the SM visualization of the ablation process and hyperparameters effect. These images lead to intuition regarding the method's strengths and weaknesses.   \n\nRuntime should be mentioned in the main text as it is a major weakness of the proposed method. \n\nThe authors didn’t explore how their method works in extreme cases. For example, very long and detailed prompts that can be naturally decomposed into 5 or more sub-prompts. In addition, when the editing prompt is very simple (change the cat into a dog) will the LLM successfully recognize this simplicity and will not decompose the prompt (and then SplitFlow will behave as FlowEdit)? If the LLM will still decompose the prompt, will SplitFlow outperform FlowEdit? (Again, specifically for simple edits such as changing the cat into a dog and nothing else).  \n\nMinor: \n\nIn line 26 the authors cite many editing works as diffusion/flow models instead of citing diffusion/flow models works such as [A1, A2, A3] and Refs [1, 13, 16].  \n\nThe claim made in lines 30-34 about “precise inversion allows near-perfect reconstruction” may be reduced from “fundamental” to “empirical”, because as far as I know this is not something that has been proved theoretically. If the authors know of such proof, they should cite it (I didn’t find it in Ref [2]). In addition, in FlowEdit [11] and previously in EF [A4] the authors also discussed the shortcomings of perfect inversion (for generated images) compared to other editing methods.   \n\n“To our knowledge, our work is the first to incorporate flow decomposition and aggregation into the inversion-free editing framework.” [89-91].  I am not familiar with works regarding flow decomposition and aggregation in text-based image editing (with or without inversion). If the authors know such works, they should cite them, if they don’t know they should write something like: “To the best of our knowledge, our work is the first to incorporate flow decomposition and aggregation into text-based image editing framework.”  \n\nIn line 232 the authors should remove the word “training” as FlowEdit  (Ref [11]) and the proposed SplitFlow are training free methods.  \n\nSome of the information in Sec. 5 is unnecessarily repetitive (lines 247, 241).  \n\nComparison with diffusion models is not necessary (especially as the authors don’t mention it in the text) and could be moved to SM.  \n\nRefs [13, 14] and [16, 17] are the same.  \n\nFigure 2 is not clear. \n\nThe authors should add a graph that illustrates text adherence versus structure preservation (like EF [A4], PnP [24], FlowEdit [11]) to illustrate the empirical tradeoff between these two requirements.  \n\nAn interesting baseline the authors may want to consider is comparing their method to methods that perform chain of edits (such as [A5]).  These methods try to achieve different goal, however there are some similarities as both methods are supposed to excel with long and complicated text prompts (chain of edits methods do it multiple steps, where SplitFlow do it in one step).  \n\n[A1] Sohl-Dickstein, Jascha, et al. \"Deep unsupervised learning using nonequilibrium thermodynamics.\" International conference on machine learning. pmlr, 2015. \n\n[A2] Ho, Jonathan, Ajay Jain, and Pieter Abbeel. \"Denoising diffusion probabilistic models.\" Advances in neural information processing systems 33 (2020): 6840-6851. \n\n[A3] Song, Yang, et al. \"Score-Based Generative Modeling through Stochastic Differential Equations.\" International Conference on Learning Representations. \n\n[A4] Huberman-Spiegelglas, Inbar, Vladimir Kulikov, and Tomer Michaeli. \"An edit friendly ddpm noise space: Inversion and manipulations.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024. \n\n[A5] Zhou, Zijun, et al. \"Multi-turn Consistent Image Editing.\" arXiv preprint arXiv:2505.04320 (2025).", "limitations": "How does the method work with large deviation from the source image? FlowEdit struggles with such edits, does SplitFlow inherit this limitation?", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths: \n\nA new method for text-based image editing, suited for long editing prompts.  \n\nAchieves good results compared to baselines.  \n\nAblation study that justifies the design choices.  \n\nWeaknesses:  \n\nNot enough visual results, especially for long, detailed text prompts (see also questions).  \n\nLong run time - requires multiple editing paths and using an LLM.  \n\nAdditional hyperparameters that affect the results.", "was_revised": true, "final_justification": "The authors answered most of my concerns during the rebuttal and discussion period. \nI don't raise my score to accept as the final manuscript will be substantially different from the current one, and I cannot truly accept a version I didn't see.", "rebuttal": "We sincerely thank the reviewer for the constructive comments and suggestions. We hope that our responses below adequately address the concerns raised.   \n\n`Question 1: I didn’t find any reference to the fidelity enhanced version mentioned in Tab. 1 and how it is different from the regular version. This is especially important as this version achieves the best or second-best results in structure preservation.`\n\nThank you for pointing out the lack of clarity regarding the fidelity-enhanced setting. In the baseline method, FlowEdit (as described in Lines 125-126 of the main paper), $x_t^{\\text{src}}$ is obtained by linearly interpolating between the source latent $x_0^{\\text{src}}$ and a randomly sampled noise. This approximation improves editability in early diffusion steps but may compromise image fidelity to some extent. Therefore, as mentioned in Line 254 of the main paper, in our fidelity-enhanced setting, we omit this noise interpolation **during the decomposition phase** and  directly use the source latent $x_0^{src}$ to better preserve structural details of the input image. For the subsequent unified flow phase, we retain the same settings as the baseline to ensure fairness and comparability of the overall pipeline.  \n***\n`Question 2: This paper subject is text-based image editing, yet it provides only a few visual examples (only 7 examples in the main text, only 15 overall with the SM). The authors should provide more visual results, especially for long and detailed text prompts, where their method is supposed to excel. In addition, I encourage the authors to add to the SM visualization of the ablation process and hyperparameters effect. These images lead to intuition regarding the method's strengths and weaknesses.`. \n\nWe fully agree that visual examples are crucial for evaluating text-based image editing, particularly for long and complex prompts where our method is designed to excel. In the final version, **we will include additional qualitative results** to highlight these strengths. We also plan to incorporate visualizations of the ablation studies and the effects of key hyperparameters to provide intuitive insights into the method’s behavior and trade-offs.  \nHowever, due to NeurIPS policy restricting rich media content (e.g., images, external links) during the rebuttal phase, we are unable to share these results at this stage. We appreciate your understanding and will ensure that these additions are included in the camera-ready version.   \n***\n`Question 3: Runtime should be mentioned in the main text as it is a major weakness of the proposed method.`  \nThank you for the suggestion. We will explicitly mention the runtime as a limitation in the main paper and discuss the trade-off between computational cost and editing performance to provide a more balanced view.\n***\n`Question 4:  The authors didn’t explore how their method works in extreme cases. For example, very long and detailed prompts that can be naturally decomposed into 5 or more sub-prompts. In addition, when the editing prompt is very simple (change the cat into a dog) will the LLM successfully recognize this simplicity and will not decompose the prompt (and then SplitFlow will behave as FlowEdit)? If the LLM will still decompose the prompt, will SplitFlow outperform FlowEdit? (Again, specifically for simple edits such as changing the cat into a dog and nothing else).`\n\nAs you mentioned, in extreme cases such as a very long and detailed prompt, the decomposition can indeed result in five or more components. In fact, as shown in Supplementary Material Fig. S2, we have empirically observed that utilizing more decomposition paths leads to better performance in cases where multiple \"editing\" operations are involved.  \n\nFurthermore, even in a very simple case like the one the reviewer mentioned—e.g., \"*cat* → *dog*\"—our prompting framework (which guides the LLM to produce three sub-prompts) results in the following decomposition: \n\n > **Source prompt**: *a cat sitting on a wooden chair*,  **Target prompt**: *a dog sitting on a wooden chair*.    \n\nThen the Sub-target prompts are as follows: \n>1. *Dog on chair*, 2. *Wooden chair with dog*, 3. *Dog seated on furniture*.\n\n\nTo verify whether SplitFlow still improves upon FlowEdit in such a simple case, we evaluated performance specifically on this sample (cat→dog). As shown in the Table below, **even when the editing prompt is simple, the proposed SplitFlow not only shows higher fidelity but also demonstrates improved editability (in the edited region)**.\nFinally, as you correctly pointed out, if no prompt decomposition is applied, the outputs of FlowEdit and SplitFlow are indeed identical.\n\n**Table: Quantitative comparison results on one sample from PIE benchmark (cat → dog).**\n\n| **Method**         | **Model** | Structure Dist ↓ | PSNR ↑ | LPIPS ↓ | MSE ↓ | SSIM ↑ | CLIP Whole ↑ | CLIP Edit ↑ |\n|--------------------|-----------|-------------|--------|---------|--------|--------|----------------|---------------|\n| **FlowEdit**       | SD3       | 40.18       | 20.51  | 115.80  | 89.00  | 86.08  | **27.18**      | 19.90         |\n| **SplitFlow (Ours)** | SD3     | **34.47**   | 20.51  | **102.09** | **88.92** | **86.95** | 26.81           | **20.81**     |\n| Δ (Diff)           | -         | -5.69       | -0.00  | -13.71  | -0.08  | +0.87  | -0.37           | +0.91         |\n\n\n`Minor weaknesses`\n    \n1) We appreciate your feedback and will revise the paper accordingly, including proper citation formatting, removal of duplicate references, and clarification of redundant explanations.\n\n2) As correctly noted, our work is the first to incorporate flow decomposition and aggregation into a text-based image editing framework. We will revise the statement for clarity as follows:\n\"To the best of our knowledge, this is the first work to introduce flow decomposition and aggregation into a text-driven image editing pipeline.\"\n\n3) Figure 2 will be improved by adding descriptive annotations and enhancing the visual design for better clarity. Additionally, in the camera-ready version, we will include a graph illustrating the trade-off between text adherence (measured by CLIP similarity) and structure preservation (measured by LPIPS).\nDue to rebuttal-stage limitations, we are currently unable to include visual figures.\n\n4) We acknowledge that [A5] was released shortly before the NeurIPS submission deadline, and we unfortunately missed citing it. As the reviewer pointed out, both [A5] and our work address challenges in image editing with complex text prompts. However, [A5] targets error accumulation in multi-turn editing, whereas our approach—SplitFlow—focuses on improving editability in a single-turn setting. We will properly cite [A5] and include a comparative discussion in the final version."}]

Author rebuttal:

Meta-review: The paper considers a problem of image editing in rectified-flow diffusion models. To do the edit, authors propose to compute a separate flow for each subprompt and aggregate them into the single editing trajectory via Latent Trajectory Projection (LTP) and Velocity Field Aggregation (VFA). They demonstrate that the approach yields better image editing fidelity than other commonly-used techniques for image editing, such as inversion.

The reviewers raised concerns about the trade-off of the computational overhead and latency to run multiple flows, as well as reliance on LLM-based prompt decomposition.

The authors showed that the model remains efficient in comparison to other editing methods, as well as the need for VFA aggregation over naive averaging. The authors demonstrated that  SplitFlow beats FlowEdit on all metrics on the editing tasks.

The paper is recommended for acceptance.
Decision: Accept (poster)