Learning an Image Editing Model without Image Editing Pairs
Authors: Nupur Kumari
Sheng-Yu Wang
Nanxuan Zhao
Yotam Nitzan
Yuheng Li
Krishna Kumar Singh
Richard Zhang
Eli Shechtman
Jun-Yan Zhu
Xun Huang
Venue: ICLR 2026 Poster (2026)
Keywords: generative models
image editing
unsupervised learning
personalization
customization
Primary area: generative models

Abstract: Recent image editing models have achieved impressive results while following natural language editing instructions,  but they rely on supervised fine-tuning with large datasets of input-target pairs. This is a critical bottleneck, as such naturally occurring pairs are hard to curate at scale. Current workarounds use synthetic training pairs that leverage the zero-shot capabilities of existing models. However, this can propagate and magnify the artifacts of the pretrained model into the final trained model. In this work, we present a new training paradigm that eliminates the need for paired data entirely. Our approach directly optimizes a few-step diffusion model by unrolling it during training and leveraging feedback from vision-language models (VLMs).  For each input and editing instruction, the VLM evaluates if an edit follows the instruction and preserves unchanged content, providing direct gradients for end-to-end optimization. To ensure visual fidelity, we incorporate distribution matching loss (DMD), which constrains generated images to remain within the image manifold learned by pretrained models. We evaluate our method on standard benchmarks and include an extensive ablation study.  Without any paired data, our method performs on par with various image editing diffusion models trained on extensive supervised paired data, under the few-step setting. Given the same VLM as the reward model, we also outperform RL-based techniques like Flow-GRPO.

Published as a conference paper at ICLR 2026
LEARNING AN IMAGE EDITING MODEL WITHOUT IMAGE EDITING PAIRS
Nupur Kumari"
Sheng-Yo Yu Wang!
Nanxuan Zhao 2
Yotam 2
Yuheng Li
Krishna Kumar Singh²
Richard Zhang
Eli Shechtman
Jun-Yan Zhu
Xun Huang
1 "Cameggi Mellon University
Adobe Research
ABSTRACT
Recent image editing models have achieved impressive results while following natural language editing instructions, but they rely on supervised fine-tuning with large datasets of input-target pairs. This is critical bottleneck, such naturally occurring pairs are hard to curate at scale. Current workarounds use synthetic training pairs that leverage the zero-shot capabilities of existing models. However, this can propagate and magnify the artifacts of the pretrained model into the final trained model. In this work, we present new training paradigm that climinates the need for paired data entirely. Our approach directly optimizes a few-step diffusion model by unrolling it during training and leveraging feedback from vision- laiiuage models (VLMs). For each input and editing instruction, the VLM evaluates if an edit follows the instruction and preserves unchanged content, providing direct gradients for end-to-end optimization. To ensure visual fidelity, we incorporate distribution matching loss (DMD), which constrains generated images to remain within the image manifold learned by pretrained models. We evaluate our method on standard benchmarks and include an extensive ablation study. Without any paired data, our method performs on par with various image editing diffusion models trained on extensive supervised paired data, under the few-step setting. Given the same VLM the reward model, we also outperform RL-based techniques like Flow-GRPO.
INTRODUCTION
Large-scale text-to-image models have achieved remarkable success, generating images of high fidelity that closely align with textual descriptions Ramesh al. (2022); Peebles & Xie (2023); Kang ctt al. (2023). Despite these advances, text-only conditioning offers limited user control and falls short for many downstream applications (Meng al. 2022 Gal et al. 2023). In practice, users often wish to start with an existing image to perform tasks like adjusting local attributes, changing the style, or placing an object in a new context. These image editing operations require precise, image-guided control that text-only prompts cannot provide.
While collecting large-scale text-image pairs is relatively straightforward (Schuhmann et al. 202 constructing supervised datasets for editing tasks is far more challenging. As one requires a pair of images, the input and its edited counterpart, along with the text instruction, and such data is rarely available online. Early methods addressed this by synthetically generating editing pairs (Brooks et al. 2023) from a pretrained model, using zero-shot editing techniques (Hertz et al.) 2023). However, synthetic datasets can quickly become outdated with new and improved base models, and they risk amplifying and propagating artifacts of the synthetic editing process. More recent approaches extract frames from videos and annotate their differences (Chen et al.) 2025 Song al. 2023b Krojer et al. 2024). Although promising, the applicability of this strategy is constrained by the diversity of transformations present in natural video sequences, where obtaining pixel-aligned before-am and-after edited pairs is nearly impossible. A final alternative to manually create training pairs (Winter et al. 2024 Magar et al. 2025), but this can be quite laborious and does scale easily.

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In this work, we explore the possibility of training an image editing model without any training pairs. Our key idea is to leverage supervision from Vision Language Models (VLMs) (Liu et al. 2023a), relying their general image- ungggeunddrsttndnng capabilities to check whether the generated images satisfy the editing instructions. Prior works have studied the use of specialized models or general- purpose VLMs in improving generative models along dimensions such as text-alignment and aesthetic quality, primarily using reinforcement learning (Black et al. [2024;] Liu et al., (2025a). In contrast, our method is the first to explore using gradient feedback from VLMs for general instruction-follovii and we distill this feedback into a lightweight generative model that can generalize to arbitrary images and edit instructions. Our final method combines the VLM-ffedbell loss to ensure that generated outputs remain in the realistic image domain while following the edit instructions, summary, our contributions are threefold:
with a distribution matching
1. We propose NP-Edit (No-Pair Edit), a framework for training image editing models using gradient feedback from Vision-Language Model (VLM), requiring no paired supervision.
2. For efficient training and effective VLM feedback, our formulation combines it with dis- tribution matching loss to learn a few-step image editing model. The final model remains competitive with existing baselines trained on supervised data.
3. We conduct a comprehensive empirical study analyzing the impact of (i) different VLM backbones, (ii) dataset scale and diversity, and (ii) VLM loss formulation. Our findings show that performance improves directly with more powerful VLMs and larger datasets, demonstrating its strong potential and scalability.
2 RELATED WORKS
Diffusion- based image editing. Development of large-scale text-to-image models has enabled a wide range of downstream applications, including local image editing (Hertz et al., 2023 Meng
et al., 2022), stylization (Sohn et al. 2023 Hertz et al. 2024 Jones et al. 2024), personalization and
customization (Gal al. 2023 Ruiz et al.
2023). These can broadly be viewed as different forms of
image-editing capabilities. Early approaches often relied zero-shot inference-time methods (Hertz)
ctt al. 2023 Parmar et al. 2023 Cao et al.] 2023 Avrahami et al.] 2023 Kim et al.] 2023) or flexible
but slow optimization baaeed techniques (Gal et al.
2023 Ruiz et al. 2023 Kumari et al.
2023
To improve efficiency and robustness, subsequent works introduced training- based ap- proaches (Brooks et al. 2023 Xiao et al. 2025 Chen et al. 2023 Fu et al. [2024] Sun et al.) (2024).
However, obtaining large datasets of image pairs remains challenging: synthetic curation (Brooks
et al. 2023 Zhang al. 2023 Zhao et al. 2024 Hui al. [0224] Yang et al.) [202bb] Tan et al.
2025
Cai et al. [2025] Kumari et al. (025) risks becoming outdated as generative models improve,
while human annotation (Winter al. 2024| Magar al. 2025 Ge et al. 2024 SSusko al. 2025)
is costly and labor-intensive. Recent efforts have explored constructing paired data from videos (Chen
al. 2025 Song et al., 2023b) or simulation environments (Yu et al., 2025), although these remain limited in either annotation diversity or visual realism. We also target similar image-editing capabil- ities but remove the need for paired data (Zhu et al. (017)) by using differentiable feedback from Vasion-language models instead of ground-truth edits.
Post-training for image generation. Post-training methods typically align image generators with human preferences using either Direct Preference Optimization (DPO) (Wallace al. 2024 Yang
et al. 2024a) or Reinforcement Learning (RL) BBlack et al.
2024). While early RL-based works use
feedback from simple scalar reward model (Kirstain et al.
2023 Xu et all 2023), the paradigm has
recently been enhanced by employing sophisticated Vision-l Lnguuage Models (VLMs) as "judges" to
provide more generic and accurate reward signals (Ku et al. 2024).
Although post-training has been successfully applied to text-to-image generation, its use for image editing models has been less explored. Concurrently to our work, EARL (Ahmadi et al. 2025) begins to address this by using VLM-as-a-judge framework to post-train an image-editing model with RL. However, RL-based approaches often depend heavily on good initialization, typically requiring Supervised Fine- Tuning (SFT) phase with paired editing data. In contrast, differentiable feedback from the VLM model, thereby obviating the need for an initial SFT stage and enables the learning of image editing models without the use of synthetically generated data.
method leverages
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Related to work, Luo et al. (2025) recently introduced method that incorporates gradient feedback from VLMs to satisty various criteria, including the horizon line, style, and layout, in generated images. However, their framework operates in per-example optimization setting, requiring costly LoRA fine-tuning etal 2022) for each criterion and prompt pair, and also does not consider image editing tasks.
Few-step diffusion models. Standard diffusion (or flow-matching) models require many sampling steps to generate high-quality images. Many prior works reduce the number of denoising steps for faster sampling by predicting larger denoising steps, including consistency models (Kim et al. 2024
Geng et al., 2024; Song et al.) 2023a Yang et al. 2024c] SSng & Dhariwal 2024; Lu & Song
2025 Heek al.] 2024 Frans et al. 2024] /hoo al. 2025), shortcut models (Frans al. 2022))
meanflow (Ging et al. 2025). and inductive moment matching (Zhou et al.] a 2025). Another line distills
pre-trained multi-step teacher into a few-step student by matching ODE trajectories (Song et al.
2023a (Salimans & Ho 2022 Geng et al.
2023). ). using adversarial loss (Sauer et al. 2024b Kang
ct al. 2024]] Yin et al. 2024a] Sauer et al. 2024a Xu al. 2024), or applying score distillation (Luo et al. 2023 Yin et al. 2024b a Zhou et al. 2024). In our framework, we adopt DMD (Yin et al.
2024b) as a distribution matching objective. This ensures that our few-step editing model's output remains in the real-image manifold defined by the pre-trained text-to-image teacher, while using VLeM-feebback to ensure the model follows the editing instructions.
3 BACKGROUND
3.1 DIFFUSION MODELS
Diffusion or flow-based models are a class of generating models that learn the data distribution by
denoising samples corrupted by different levels of Gaussian Noise (Ho et al. 2020) Song et al.
2021 Lipman et al.] 2023). Given a real sample x, a forward diffusion process creates noisy samples x a'x x'x+a'' a'e over time E (0, 1]. where ~ (O,, I), and f a² define noise schedule such that x" ~ (O, I) and x x. The denoising model parameterized to reverse the forward diffusion process by either predicting the noise (Ho et al. (020)) added to the sample or velocity towards the clean sample (Liu et al. 2003bb Salimans & Ho] 2022). In our work, we follow the flow-based formulation, with the forward denoising process being a linear interpolation, i.c., a 1 t and o' = t. The training objective for a flow-based model, with parameters 6, can be simplified to the following:
Ex Bxttt..e- well vo(x', .t, c)ll,
where = c c x and is time dependent weighting factor. The denoising network can be conditioned on other inputs c, such as a text prompt, reference image, or both, as in our case.
3.2 VISION LANGUAGE MODELS (VLMs)
Vision Language Models (VLMs) trained from multimodal image-text data have shown exemplary visual understanding and reasoning capabilities and can serve as a general-purpose visual model. common strategy for training such large-scale VLMs is via visual instruction tuning (Liu et al.) 2023a), which aligns a pre-trained Vision Encoder output with the input word embedding space of a pretrained Large Language Model (LLM). More specifically, the image x is encoded into a set of tokens using the vision encoder, X, g(x). The input question regarding the image and its ground truth answer are tokenized in the LLM input embedding space as X,, and X., respectively. A projector module, fø. projects the vision-encode tokens into the LLM word embedding space and is trained via standard autoregressive loss to maximize the probability of predicting the correct answer:
L II ((lfff((())) ==1
p P(X.
where X. = [aaa-..-a (a....aa)) ar,] is of token length L, and X denotes all the tokens before the current prediction token index. The final loss simplifies to a cross-entropy over the total vocabulary length. In experiments, we use LLa La-VaOOne Vision-7B (Li et al.) 2024) the VLM that uses SigLIP (Zhai et al. 2023) vision encoder and Qwen-2 LLM (Qwen-Qeers Team 2024). and is among the state-of-the-ar VLMSs of this scale.
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4 METHOD
Given a pretrained lext-to-image diffusion model Ginit and a dataset X {(y; i; Ci c of reference image y, corresponding edit instruction c, and captions e² and c* that describe the reference and edited image respectively, we fine-tune Ginit into a few-step image editing model Ga without requiring ground truth edited image x according to the edit instruction. Our approach, No-Pair (NP)-Edit, introduces VI.M-based loss to evaluate edit success and combines with a distribution matching loss to ensure outputs remain within the natural image domain. Below, we first detail the construction the dataset, then our training objective, and finally other implementation details.
4.1 EDIT INSTRUCTION DATASET
Each dataset sample consists of real image y as reference and an associated edit instruction, c. Following prior works (Liu et al., 2025b Ye et al. 2025), we focus on several categories of local editing operations, such as Add, Replace, Remove, Adjust shape, Action, Stylization, Text editing. Color, Material, and Background change, well more free-form editing tasks such as
Customization or Personalization (Gal et al. 2003] Ruiz et al. 2023) Kumari et al. 2023). Candidate instructions for each type generated using Qwen2.5-32B VLM (Qwen-Teem, 2025). Given
image-instruction pair, we further query the VLM to assess its validity and to suggest the caption, cx for the edited image. For the customization task, we restrict reference images those showing prominent central object, either filtered from real image corpus or generated via the pretrained model (Tan et al., 2025; Kumari al., 2025), and prompt the VLM generate a caption that places the object in novel background or context. In total, for local and free-form editing instructions, our dataset consists of 3M and 600K reference images, respectively. The input prompt to the Qwen2.5-32B VLM for each setup is shown in the Appendix
4.2 TRAINING OBJECTIVE
Training diffusion flow-based model (Ho et al. 2020 Liu et al. 2023b) for image editing without pairs presents a unique challenge. Standard diffusion training takes as input noised versions of ground-trune -ruth image. In our setting, no such ground- -tuth edited image exists; thus,
these intermediate noisy inputs. On the other hand, directly mapping noise to the edited image in a single step is naturally challenging and yields poor fidelity (see Appendix BB. To address this, during training, we propose to unroll the backward diffusion trajectory starting from noise using a two-step sampling procedure (Song al.) 2023a). Specifically, given the reference mmage-instruction pair (y, c), the editing model Go first predicts provisional clean image x from noise e. Then, a second step refines this estimate by feeding an interpolated noisy input back into the model:
x E - Vo.
where v = Go(e, = l,),y), E e~N(0,
X/ - tvo. where Vo = Go( c,y.) X = (1 (1-t))s + te N(0,), (0,1), x6
With the second step, the model is now trained on noisy intermediate states at timesteps determined by while being more efficient than full backward unroll. In our method, we focus on few-step generation-speccficlllyy four Few-step generator provides better estimate of the denoised image, x at intermediate steps, which in turn enables effective VLMM-baed feedback. VLMs tend to give unreliable judgments when inputs noisy or blurry (see Appendix B This also enables faster inference and lowers training costs.
restrict E [t, 12, ta], a fixed schedule, in the second step.
VLM-based editing loss. To evaluate whether an edit is successfully applied in xg. we define set of template questions with corresponding ground truth answers, DOA = {(X,, Xa,)}, tailored to each edit category. The VLM is instructed answer with binary yes and no answer, i.e., Xa, E (Yes, No), and Xa, denotes the opposite response. The loss is then a binary cross-entropy over the predicted logit difference between the tokens corresponding to the correct and opposite responses, respectively.
LVLM Σ log p(az), where p(a,) a(C)) -(d))
(4)
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Edit inatuctiion "Ckeege fing barkgrsind to bright Grinng meadow"
t
to (2.
profeeeeeeennn eigla wnit expart time ..nolluae editing instucttion been arecured.
DMD Loes
Batoaarioation Editing loss
(P( P("w
VLN
(P(yee)) P(
Cenventtr
inter
(X()
Canerador
(xpp
Icaen by promerattio suard
-.faurere provided and
to
iepert imsage
exeee as the four vougs isactii ooe slangee in sesssed
Imrage decause of editt (ww.taattttttttttnooooooo
Incut on
Figure I: Method. We fine-tune a pretrained text-to-image model into few-step image-editing model using differentiable VLod-foo regarding edit success and distribution matching loss (DMD (Yin et al. (024a)) for ensuring output images remain in the natural image manifold. Given the cdit instruction and condition image, we predict the edited image starting from noise. During training. randomly sample the few-shol timestep (Line 3 of Algorithm D. and perform two-step diffusion unrolling predict the edited image for the intermediate timestep as shown here. The loss is backpropagated through the two diffusion sampling steps.
where (a)) is the logit corresponding to the token Xay. o is the sigmoid function, and p(a,) the probability of correct answer, while restricting normalization to only the Yes and No tokens, which we observe to more effective during training (Zhang et al. 2024). Computing this loss is relatively fast, as it requires single forward call to the VLM per question, as opposed to autoregressive prediction.
For each edit instruction, we use two complementary questions to compute the editing loss: (I) Edit-verification question to assess whether the intended edit is applied, and (2) question to ensure the image is not over-edited and is consistent with the reference image. Specifically, for the local image-editing instructions, we verify edit success with the following question "The objective is to evaluate if the editing instruction has been executed the second image. Editing instruction: fedit instruction). Answer with Yes or No." except removal edit-type, where directly evaluate if the intended object is removed by asking "Answer with a Yes or No if the image has {object name]". For the identity-rreeervatin question, we ask the following: "Answer with a Yes or No if the second image is exactly the same as the first image. IGNORE the changes in the second image because of the edit: (edit instruction]" )". We provide the list of all questions along with their system and user prompts for all editing types, including free-form editing in the Appendix E
Distribution matching with text-to-image teacher model. While VLM feedback evaluates the efficacy of instruction following, it does not enforce the generated outputs remain in the real image domain. To ensure this and keep the output distribution of the generator aligned with the
pre-trained model, we apply Distribution Matching Distillation (DMD) (Yin et al.) 2222bb ab between the fine-tuned model, Go. and the pre-trained text-to-image (teacher) model, Gini- DMD minimizes the Kullback- Leibler (KL) divergence between the real image distribution, as estimated by the teacher model, and the output distribution of the fine-tuned model. The gradient of this KL-divergence loss with respect to the generator parameters can simplified to:
dG
voDKL= = B.....(...)..r001)...))
(xh, Veen(xhi,, )²)) d0
where cx is the text caption describing the noisy edited image x and Vical, Vgen represents the predicted velocity from the teacher and a trainable auxiliary model, As respectively. The auxiliary model is trained along with Go to learn the current output distribution of Go using flow-based denoising objective. This loss ensures that the edited images not only satisfy the instruction but also remain faithful to the text-conditioned distribution of real images modeled by the pretrained teacher.
4.3 TRAINING DETAILS
The pretrained model Gş is originally designed to generate image, x. conditioned only on text c. To adapt it to our editing task, we extend its conditioning to include the reference image y. Following recent works (Xiao et al. 2025 Tan al.) 2025). we concatenate the VAE encoding of the reference image to the noisy target image encoding along the token sequence dimension, similar to text embedding, thereby enabling the model to attend to both text and visual conditions.
To stabilize training, in the initial few iterations, we train the model with the objective of simply reconstructing the concatenated reference image. This encourages the network propagate content
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from the reference input, aligning it toward producing realistic images under joint text- text-image conditioning. After this, we introduce our main training objective as explained in the previous section.
The final loss for the generator is weighted combination of the VLM- editing loss and DMD loss. The auxiliary network, Ag. is updated Na times for every generator, Gg. update (Yin et al. 2024a). Our pre-trained generative model is 2B parameter internal DiT-based (Peebles & Xie 2024) latent space diffusion model. The overall training pipeline is illustrated Figure [n bandis is detailed
more formally in Algorithm beloww Other training hyperparameters are detailed in Appendix
Algorithm 1: NP-Edit: training method
Input: Pretrained VLM and text-to-image model Gim. Dataset X'
C,,
Output: Few-step amage-editing model Gs
Gg t (opyWeights(Gimi)>
Aa f copy WeightstGGtii // Wa rmup with ident ity loss
for step to Nuormup do
(y. e²) X. NO, I), ~~ (0,
xt> y
x t +(1- -11-1x++6
vo ",,xx,l,y,,
La - 1|v vol where v= -
Og la 1I Vea Lid-
end for
11
12
Main training loop while traindo do
13
[(y,c,c*)] ~ X. N.. Ve + Go
14
15
16
x/ + c Vo
17
ift then
Ve t Fxlxx,t,y,co
(1- t)x tte; e~ e~ NO0.1)
x - X ivg
29
end if
21
Compute LVVM / Equ.
Compute DKL. / Equ. 0G + 0g - Aym V8 CVLM And V VgDKL for local step 1 to Naw do
5
23
c~ (), ((y,,,) c*) ~
25
26
x - Co(c,y.c) // edited image with backward unroll
x - (1-11fj)) tix te E~N(O,I)t (0,1)
27
Vo - Ax c*))
28
ΦA + ΦΛ nA Vollvo v/| where v = =c cxxf
29
30
end for
31
end while
EXPERIMENTS
In this section, we show the results of our method on local image editing well more free-form image editing tasks like customization, and compare them with the state-of-the-ar baseline methods.
5.1 LOCAL IMAGE-EDITING
For evaluation, following prior works, we use the English subset of GEdit- Benchmark Liu al. (2025b), which captures real-world user interactions across different edit types. We also show results on the ImgEdit (Ye et al. 2025) benchmark in Appendix A
Benchmark.
Evaluation metric. For quantitative evaluation, we follow prior works and use GPT4o-based VIEScore (Ku al., 2024) metric. It scores each edit Semantic Consistency (SC) score,

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Input reference image
Owenn-maage-Edit (50 step)
1eppixEEdi step)
FLUX. 1Kontext step)
Qwen-Image (4 step)
Ours step)
Edii the backgrouxdd be -hhngghiis bund
ca
VAL.FRANT
Mir
VAL FRANT
WuD PUNNT
Replace the text CS with VALO and réplacé GO with RANT
RemLvve the dog getting a hairzut:
Light the candle to enhance the sandlelight
Adjust the image style to a watersolor effect
Change the color of the sheep to purple
Make the person, the imagge wavve
Replace the computeer's easing bamboo filee composikz
Figure 2: Qualitative comparison on GEdit-Bench under the few-step sampling setting. For upper-bound comparison, in the 1" column we show results of the best multi-step sampling method (as measured by the quantitative metrics Table Our method performs on par or better than baseline methods across different edit types in the few-step setting. We show more samples the Appendix Figure [3

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Table Quantitative evaluation GEdit-Bench. Our method performs on par better than baselines under the few-step setting. For multi-step sampling, outperforms OmiGen and remains competitive with many of the larger-scale models like BAGEL and FLUX.1 1 Kontext. All numbers reported in x x10
Method
# Param
#Step
SC Scoret
PQ Score
Overall
Omni-Gen Xiao et al. 2025
4B
50
5.52
6.14
4.97
BAGEL, [Deng et al. 2025
7B
50
7.02
6.26
6.14
FI.UX.1 Konteet (1 Labs ct al.
2025
12B
28
6,29
6.65
5.65
Step-1X Edit (L.iu of al. Z1255
V
12B
28
7.30
7.37
6.79
Qwen-Image-Eall Wi et al 2025
20B
50
7.94
7.50
7.36
FL.UX.I-Konicx (Labs al. 2025
12B
5.80
5.74
5.04
Step-IX Edit (I.iu 21225
12B
6.61
6.43
6.01
Qwen-Image-Faim [Wu al. 2025
20B
6.82
6.21
6.06
Turbo-Edit (Deutch al. 20244
1B
3.84
6.67
3.84
NP-Edit (Ours)
2B
6.16
Table 2: Free-form editing task, Customization, evaluation on Dreambooth. We perform better than OminiCon- trol, DSD, and SynCD, which are trained for this task on synthetic datasets. When compared to FLUX. K-ontexxt and Owen-Imaae--Fee we still perform comparably the few-step setting, All numbers are reported x10
Method
Param
Step
SC Scoref
PQ Score
Overall
DSD Cai et al. 2025
12B
28
6.71
7.41
6.78
SynCI Kumar al.
2025
12B
30
7.66
7.83
7.54
FL.UX. Konteext Labs et
2025
12B
28
8.19
7.45
7.61
Qwen-Image- (u et al.
2025
20B
50
8.53
7.79
8.02
OminiControl TTan al. 2025
12B
8
6.33
7.82
6.22
DSD Cai et
2025
12B
6.37
6.78
6.29
SynCD [Kumari el al. 2025
12B
8
7.71
6.84
7.07
FLUX.1-Kontee (Lass el al.
2025
12B
7.99
7.18
7.39
Owen-Image-Edii (Wu at.
2025
20B
8
8.08
7.44
7.62
NP-Edit (Ours)
2B
8
7.68
7.56
7.33
NP-Edit (Ours)
2B
4
7.60
7.28
7.10
evaluating whether the edit instruction was followed, and (2) Perceptual Quality (PQ) score, assessing realism and absence of artifacts. Following VIEScore, for the Overall score, we take the geometric mean between SC and PQ for each image, and average across images in the evaluation benchmark.
Baselines. We compare our method with leading baselines, including FLUX.1- Konttext (Labs et al, 2025), Step1X- Edit (Liu al.] 2025b), BAGEL (Deng et al.) (025), OmniGen (Xiao al. (025). and Qwen-Image-Edit (Wu et al. 2025).. Since no prior work explicitly targets few-step editing, we simply evaluate the above baselines with few-step sampling well their original multi-step setting for an upper-bound comparison. We also include Turbo-Edit (Deutch et al., 2024), state-of-the zero-shot few-step method that requires no paired supervision (as zero-shot) and is thus closest to our setup. We use the open-source implementation of all baselines, with further details in the Appendix F
Results Table showws the quantitative result. the few-step setting, our method achieves the best Overall and Perceptual Quality (PQ) score compared to baseline methods. When compared their original multi-step sampling, our few-step model still outperforms OmniGin and remains competitive with BAGEL, FLUX. -Kontext, despite being x6 smaller parameter-wise While Step E-Edit and Meen-Image-Edin perform better, they are substantially larger models. Figure provides a qualitative comparison. As can see, our method can successfully follow different editing instructions while being consistent with the input reference image. For instance, in the 6th row (sheep color change), our approach produces a more natural edit compared to baselines. It also performs comparably to the multi-step variant for edits like lighting the candle in 4tth row or making the person wave in 7thh row.
5.2 FREE-FORM EDITING: CUSTOMIZATION
Benchmark. We use the widely adopted DreamBooth (Ruiz et al. (023) dataset for evaluation. It consists of 30 objects and 25 prompts per object category. The goal generate the same object as shown in the reference image, but in a different context, mentioned in the text prompt.
Baselines, We compare against state-of-the-an unified image-editing baselines such as FLUX.1 Kontext (Labs al. 2025) and Qman-Image-Fddit Wu et al. (2025) well as OminiControl Tan
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Input reference image
Qwen--mage--Eitt (50 step)
FLUX, 1-Kontex: step)
OmninCContral (8 step?
Owen-Image-Edit step)
Ours (8 step)
Shoe with A tree and Ruturan leaves iss the backeground
A tay on top of sidewalk crowsled street
A cat werring pinke sunglasses Figure 3: Qualitative comparison on Customization task. Our method can generate the object in new contexts while having better fidelity under few-step sampling. We show more samples in the Appendix Figure TS
ct al. 2025). DSD (Cai et al., 2025). and SynCD (Kumari et al., 2025) which are forwaforward models trained specifically for this task synthetic datasets.
Evaluation metric. Here well, we use the VIEScore evaluation with a similar Semantic Consis- tency (SC) score to evaluate identity and text alignment, a Perceptual Quality (PQ) score to measure realism, and the geometric mean of the two for the Overall score. We also report CLIPScore (Radford
ct al. 202 and DINO (Oquab et al. 2023) similarity- based metrics in the Appendix.
As shown Table our method performs comparably to tfte-offthhearr methods. In the few-shot sampling setting, all the baseline methods fail to generate realistic samples at 4 steps; therefore, we compare with them at 8 sampling steps. Our method still results higher fidelity samples as Figure Jshws, while maintaining object identity with the reference image. Note that our method performs better than OminiControl, which is also a few-step (8) model for this task.
Results.
5.3 ABLATION
In this section, we perform several ablations to analyze the role different components of our method, dataset scale, and stronger VLMs. All ablations are done on the local image-editing task.
Training objective. We ablate our training objective across four settings: (1) using only distribution matching loss, (2) using only the VLM-based editing loss, (3) removing the identity-preservatie question from DQA, and (4) replacing the binary-cross entropy loss (Eqn. 4 with standard cross- entropy over the full vocabulary. Results are shown below in Table Training without the VLM- based loss and relying solely distribution matching significantly degrades the model's capabilities at following editing instructions. We observe that VLM-based loss is essential for maintaining consistency between input and edited images and for certain editing tasks like Removal (Figure and Appendix Figure 5). However, only training with VLM-based loss leads to unrealistic outputs (Appendix Figure 6). and the training eventually diverges, evidenced the low overall score in T underscoring the need for DMD loss. In addition, using binary cross-entropy loss and having question check consistency between input and edited images improves the overall performance.
Dataset and VLM scale. To study the role of dataset scale, we vary the number of unique reference images training, Our final dataset represents the maximum scale feasible under our computational resources. Table shows the performance across different dataset sizes and VLM backbones. We observe consistent gains with larger datasets, suggesting that further scaling data could yield additional improvements. Similarly, larger parameter VI.M-backbone leads better performance,

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Published as a conference paper at ICLR 2026
Table 3: Training objective ablation. We compare the GEdit-Bench using the VIEScore metric. Ablating
different components of our method leads drop in performance, indicating importance.
Method
SC Scoret
PQ Score
Overall 1
Ours w/ only DMD
6.16
7.69
6.10
4.93
7.51
4.93
w/ only VLM
2.03
3.48
1.93
w/o VLM identity
5.70
7.67
5.76
w/ standard loss
5.95
7.64
5.89
Ours
Only
SFT
SFT
Table 4: Dataset and VLM scale and compari- son with Reinforcement Learning on the GEdit- Bench. Increasing dataset scale and using stronger VLMs leads to increased performance. Our method also performs better than post-training an SFT
Replace the sohon. Eus with trucce
model with RL Liu al. 2025ap.
Method
SC Score?
PQ Score t
Overall
% Dataset
4.41
7.10
4.66
50 % Dataset
5.41
7.73
5.52
100 % Dataset
wosbrella
VL
5.36
7.67
5.45
5.88
7.74
5.89
Figure 4: Qualitative analysis of ablation experiments.
LLava-0.5B
4.57
7,50
4.59
Our method maintains better input and edited image align- ment compared to only training with DMD loss, which also
LLava-7B (Ours)
6.16
7.69
6.10
SFT
3.91
5,70
3.64
fails tasks like removal. Compared fine-tuning an SFT
SFT RL
4.55
5.47
4.19
model RL, our method results better fidelity while following the edit instruction. Please zoom in for details.
SFT + Ours
6.08
7,83
6.06
across different VLMS such as Intern VL. (Chen et al. 2024 and LLvvv- Owea-OneVee Vision (Li et al. 2024). underscoring the promise that our method can improve as more powerful VL.Ms are developed.
Our method vs. Reinforcement Learning (RL). RL is a common post-training strategy for improving pre-trained models without paired supervision and can also leverage VLMs the reward model, similar setup to ours. Thus, we benchmark our method against Flow-GRPO (Liu et al. 2025a)) widely used RL method for text-to-image diffusion. However, since RL relies on reasonable initialization, we need to first train an image-editing model via Supervised Fine-Tuniig (SFT) on a paired dataset (Lin et al., (025). We then fine-tune it with Flow-GRPP using the same Llava- One Vision reward model in our approach. As shown in Table SFT alone performs poorly, likely due to the limited quality of paired data. Our method surpasses both SFT and SFT+RL, despite requiring no paired supervision. Fine-tuning the model with some paired data before applying approach can slightly improve the pixel-level consistency between the input reference and output edited image (as shown in Figure 4). although the quantitative numbers are similar.
The Appendix provides additional results and a more detailed discussion of the method's limitations.
DISCUSSION AND LIMITATIONS
This paper introduces a new paradigm for enabling image editing capabilities given a pre-trained text- to-image diffusion model, without paired before-and-aften edit supervision. Our approach combines differentiable feedback from VLMs to ensure editing success with a distribution matching objective maintain visual realism. This method achieves competitive performance with recent state-of-the-art baselines trained on paired data while enabling efficient few-step generation.
Despite these promising results, our method has limitations. Without pixel-level supervision, edits may deviate from the input image in fine-graing details or fail to fully preserve subject identity. We show in Appendix C that adding perceptual similarity loss (e.g., LPIPS (Zhang et 2018)) between input and edited images alleviates this to some extent, though often at the cost of editing quality. Another concern for our method that it is directly tied the VLM's capabilities and its biases. Additionally, the requirement to keep it in GPU memory introduces VRAM overhead. We expect ongoing advances in stronger and more efficient VLMs can help address this issue. Overall, our framework scales effectively with large unpaired datasets and highlights path toward more flexible post-training of generative models for diverse downstream tasks.
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Acknowledgment. We thank Gaurav Parmar, Maxwell Jones, and Ruihan Gao for their feedback and helpful discussions. This work was partly done while Nupur Kumari was interning at Adobe Research. Sheng-Y Yu Wang is supported by the Google PhD Fellowship. The project was partly supported by Adohe Inc., the Packard Fellowship, the ITPP grant funded by the Korean Government (MSIT) (No. RS- RS-2024-00457882, National AI Research Lab Project), NSF IIS-2239076, and NSF ISS-2403303.
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Appendix
Additional Comparison with Baseline Methods
17
Ablation Study
18
Limitation
20
D Dataset Construction Details
22
Training Implementation Details
26
F,1 Local-image editing
26
Free-form editing (Customizationn
27
F Other Baseline Details
27
G Use of LLM in Paper Writing,
28
Reproducility Results
28
Ethics Statement
28
A ADDITIONAL COMPARISON WITH BASELINE METHODS
Human preference study. We perform pairwise human comparison study GEdit-Bende using Amazon Mechanical Turk. To measure editing success (similar to SC score in VIEScore), following Imagic (Kawar al. 2023), we show the reference image in the middle and the images edited by method and a baseline method, on the left and right randomly, and ask the user: "Which of the two images (Left or Right) matches with the editing instruction better, while preserving the details from the input reference image shown in the middle?". We collect 500 responses per pairwise comparison and show the results in Table 5 Under the few-step setting, our method outperforms Turbo- -dit, marginally preferred over I-Kntext, Step- IX Edit, and has random chance when compared with Swen--Imaee-a
This consistent with the VIEScore evaluation in Table Similar editing success, to measure perceptual quality (similar to PQ score in VIEScore), we show the images edited by our method and a baseline method on the left and right randomly, and ask the user: "Which image do you think has better overall quality and photorealism?". As Table 5 shows, here our method is preferred over all the baselines, also consistent with the VIEScore evaluation.
Local image-editing. Here, we compare on another commonly adopted image-editing benchmark. ImgEdit (Ye et al.] (025) (Basic), which covers nine local editing types across diverse semantic categories with total of 734 samples. Quantitative results under their proposed GPT4o-basee evaluation protocol are reported in Table 6 with VIEScore (Ku al. 2024 results in Table 7 Consistent with the trend observed on GEdit-Bench, our method has better or on-par performance in the few-step setting and remains comparable to many of the multi-step baselines well. We also include a comparison on TEDBench (Kawar et al. 2023), which focuses primarily on non-rigid and background edit-types. Table X shows the quantitative comparison with similar trends and Idgt (Basic). Qualitative comparisons are shown in Figure 14 and 16 for ImgEdit (Basic) and TEDBench, respectively. We also show additional qualitative samples GEdit-Benn in Figure [3]
Customization or free-form editing. Here, we report additional metrics commonly used for evaluation. Specifically, CLIPScore (Radford et al. 2021) and TIFA (Hu et al. 2023) to measure text
alignment; and similarity in DINOv2 (Oquab et al. 2023) feature space after background masking to measure identity alignment, denoted as MDINOv2-L We also report an overall Geometric score (Yan)
et al. 2023) by taking the geometric mean of TIFA and MDINOv2-I. The results are shown in
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Table 9 Consistent with the VIEScore evaluation reported in the main paper, method performs comparably with other baselines in the few-step sampling regime. We show more sample comparisons in Figure 15
fal
Table 5: Human evaluation on GEdit-Bence under few-step sampling setting. We report the percentage of outperform Turbo- Edit and remain
who prefer our method over each baseline. For editing competitive with other baselines, with slight preference over FLUX.1-Kontext and Step-13 1X Edit and random chance with Image-Edit. This trend is consistent with the VIEScore evaluation Table [ The standard error for all is within +4%.
Ours
Hurbo-Edit
FI.UX.I-Konter
Step- IX Edit Qwen-Image-Fdit
SC preference
63.19
53.35
56.46
48.44 %
PQ preference
73.92
79.91
80.39
71.98 %
Table 6: BigEdit-Bencll comparison using their proposed GPT-4o based evaluation protocal and few-step sampling setting. Our method outperforms baseline methads the Avg all edit types.
Method
# Param
Action?
Bg
Style
Adjust
Replace
Add 1
Extract
Remove
Compone
Arg
Qwen- Image-Fdir
20B
3.14
2.83
3.70
3,25
3.00
3.52
1.96
3,06
3.02
Flux, 1-Kontett
12B
2.97
3.89
3,04
3.15
3.31
1.82
2.37
2.46
2.95
Stsp1X Edit
12B
1.66
2.60
3.46
3.44
2.50
3.25
1.77
2.41
2.38
2.83
NP-Edit (Ours)
2B
4.44
4.13
4.14
3.94
3.57
4,52
201
2.71
3.18
3,63
Table VIEScore evaluation on ImgEdit- Bench. Our method performs on par or better than baselines under the few-step setting. For multi-step sampling. it still outperforms OmniGen and remains competitive with many of the larger-scale models like BAGEL and FLUX.1 Kontext. All numbers reported in x x10
Method
# -aaram
# Step
SC Scoret
PQ Score f
Overall
BAGEL
7B
50
7.55
6.22
6.47
12B
28
6.94
6.73
6.19
Step-IX IX Edit v1.1
12B
28
7.26
7.30
6.72
Qwenlmage Edit
20B
8.30
7.77
7.85
Qwenlmage Edit
20B
4
6.23
5.14
5,46
FLUX.1 K-ontext
12B
6.08
5.22
5.14
Step-IX Edit v1.1
12B
6.00
5.37
5.14
NP-Edit (Ours)
2B
4
6.72
7.78
6.62
Table 8: VIEScore evaluation TEDBench. Our method performs on par better than baselines under the
few-step setting and remains competitive to baselines multi-step sampling. All numbers reported in x
Method
Param
#Step
SC Scoret
PQ Score
Overall
FLUX.I-Komee
12B
28
6.54
6.77
5.97
Step-1X 1 Edit v1.1
12B
28
6.86
7.62
6.67
QwenImage Edit
20B
7.90
7.88
7.58
QwenImage Edit
20B
4
6.20
5.32
5.44
FLUX.I-Konter
12B
5.84
4.96
Step-IX Edit v1.1
12B
6.24
5.90
5.39
NP-Edit (Ours)
2B
4
6.96
8.28
6.98
B ABLATION STUDY
Training objective. Here, we provide a more detailed analysis by examining performance across different editing sub-types. As reminder, we ablated training objective under four settings: (1) using only the distribution matching loss, (2) using only the VLM-based editing loss, (3) removing the identity-presee preservation question from DQA, and (4) replacing the binary-cross entropy loss as explained in Eqn. g with standard cross-entropy over full vocabulary length. As shown in Figure
training with only the DMD loss yields comparable performance certain sub-edit types such as
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Table 9: Quantitative evaluation of free-form editing task, Customization, DreamBooth dataset. All
numbers reported x10
Method
#Param
#Step
MDINO Scoref
CLIP Score
TIEA
Geometric Score
DSD
12B
28
6.55
3.08
8.71
7.32
SynCD
12B
30
7.34
3.09
8.53
7.71
B.UX.I-Kontex.
12B
28
7.72
3.07
8.88
8.14
Qwen- Been-Image-Edi
20B
50
7.47
3.14
9.37
8.22
OminiControl
12B
8
6.16
3.02
8.12
6.64
DSD
12B
8
5.88
3.15
8.93
6,99
SynCD
7.11
3.16
9.11
7.79
FLUX.IIKomter
7.50
3.08
7.98
Qwen-Image-Edit
20B
7.29
3.08
8.96
NP-Edit (Ours)
2B
6.82
2.97
8.73
7.54
NP-Edit (Ours)
2B
7.03
3.04
8.89
7.72
Replace
Style
Add
Remove
Human
Replace the blue sky with starry night sky
Text
Color
Motion
Bac kground
Material
Tone
Ours
w/ CE Loss
Replace the purple flowers with orange flowers
Only DMD Loss
w/o VLM Identity
Figure 6: Training with only VLM-editing loss leads to lower fidelity samples with the model only maxi- mizing the edit success probability. Current general- purpose VLMs are often not good at subjective tasks like evaluating image fidelity, highlighting the require- ment of distribution matching loss our framework.
Figure 5: Performance each sub edit-type. Train- ing with only DMD loss fails to achieve certain tasks like removal and style changes. In addition, using bi- nary cross-entropy loss and VLM identity-basedd ques- tions helps improve the overall performance.
leC
bel
Curs (4-step)
Singls step
Suestion: Art Shere
sunngifatts in the
Edit the bacleground be Sha Shanghai's bund
YLM
Yos
there cloce
imsager
WLN resporeu 3
No YES YES
Figure 7: Unreliable VLM response on intermedi- ate outputs of multi-step diffusion model. Here show 28-step diffusion process, denoising predic- tions from early steps (e.g.. 4), which correspond to high noise levels, are blurry and semantically am- biguous. This can lead to unreliable responses from the VLM, shown here. Therefore, adopt few-step diffusion model that always generates sharp images.
Add sat sitting in the basket
Figure 8: Our (4-step) vs single-step editing model. We compare our final 4-step model with single-step model, both trained via our approach. Editing im- age in single step is still challenging and leads lower-fidelity outputs.
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Color change, since DMD matches the text-conditioned score between the fine-tuned and pre- pre-trained models, thus improving overall text alignment. However, fails on tasks like Removal, underscoring the importance of yLMdd editing loss and its generalizability across diverse editing instructions. In addition, VLM--assdd loss also helps in maintaining better consistency between input and edited images (first row of Figure in the main paper). However, when training with only the VL.M-base editing loss, there's ' a gradual degradation in image quality, as Figure 6 shows, highlighting the complementary role of distribution matching losses such DMD.
Sampling steps. For our method, we chose train few-step image-editing model instead of multi-step diffusion model, as multi-step diffusion has a noisy or blurry estimate of the final output in the early stages of diffusion. This can make it difficult get reliable response from the VLM, as shown in Figure On the other hand, predicting an edited image in one step is still challenging. as mentioned in the main paper, and shown here in Figure 8 Thus few-step provides a nice balance between the two extremes of single and multi-step sampling for our purposes.
Sensitivity to prompt wording in VLM-based editing loss. It has been observed that VLMs can be sensitive to the specific prompt wording (?). While did not need extensive prompt engineering, we find that removal edit-type benefits from explicitly asking about an object's 'ss presence in the Edit-verification question template, as mentioned in Section 4.2 To reiterate, the templates are: (1) "Answer with a Yes or No if the image has {object name } and (2) "The objective is to evaluate if the editing instruction has been executed in the second image. Editing instruction: {edit instruction]: }. Answer with a Yes or No.". Here, we show a qualitative comparison of two models trained for the removal edit-task while varying the Edit- dditt-verficattin question to the above two templates. As Figure 9 shows, the model trained using more specific prompt wording has better performance. Nonetheless, we expect such prompt-level sensitivity to diminish as VL.Ms continue to improve in robustness and reliability.
Temrove Spec Dueeton
Gerwic Edit Sapooeex O Guandion
Add
Background
Material
Remove
Motion
fraght
Colors
Human
Tone
Style
Figure 9: Sensitivity to prompt wording, We com- pare models trained for removal edit-type using two different template questions. A more specific question, such as, "Answer with Yes No if the image has (object name]". leads to better performance compared to a more general question, such as, "The objective is to evaluate if the editing instruction has been exe- cuted in the second image. Editing instruction: (edit instruction). ). Answer with a Yes No.".
Text
Replace
Ours
UPPS
Cydle 1855
Figure 10: Performance for each sub edit-type for LPIPS and cycle-loss on GEdit-Bench Additional regularization losses like LPIPS between input and edited image cyele loss help increase the alignment between input and output edited image as shown in Figure I1
but lower the overall editing success.
C LIMITATION
Alignment between input and edited image. A limitation of our framework is the lack of pixel-wise supervision to preserve regions that should remain unchanged given an edit instruction. Consequently, edited images can deviate from the imput image in details or spatial alignment, as shown in Figure 1s column. While our VLM-based editing loss includes a question to check consistency between the input and edited images (Identity-peseerattin question in Section 4.2). it does not enforce pixel-level alignment, since current VLMs struggle to detect subtle changes and minor details.
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w/o VLM Identity
Ours
w/ Cycle Loss
w/ LPIPS
Turn the rice into a hambburer and draw an avatar eating a burger
Remove the bangs
Replace the doctor's coat with a Merino wool sweater
question, Section 4.2 to maintain the consistency between input and edited image (1" 2nd column). However, given the lack of pixel-level supervision, it still struggle in many scenarios to maintain exact identity. Adding cycle image helps mitigate this issue to an extent (3th column). though with a drop in performance (Overall VIEscore of 5.10 vs 6.10 for Ours). We also experimented with LPIPS (Zhang et al. 2018) loss between the input and edited image, which helps (1" row) but the cost of completely failing tasks like removal (2nd row).
Figure 11: Limitation. We use
between input and cycle-edited
Here, experiment with regularization losses that improve the alignment between input and edited images in regions that should not be edited: (1) LPIPS (Zhang et al. 2018) loss between input and edited image, and (2) Cycle loss (Zhu et al. 2017) between input and cycle-edited image.
In the cycle loss, for an edited image (e.g., remove the flower pot), we apply reverse edit to it (e.g., add flower pot) and encourage this reversal reconstruct the original input image. We compute the reconstruction loss between the input and srerss-edited image in the SigLIP (Zhai et al. (023) feature space. We select SigLIP's global embedding over fine-grained feature spaces like LPIPS to provide flexibility when reverse instructions are ambiguous (e.g., restoring the exact original object). For training with cycle loss, we add to our training objective after 4K iterations. The reverse edit instruction for each (image, instruction) pair is generated using Qwen-32B VLM.
While LPIPS loss improves consistency, it degrades editing quality, particularly for edit-types like Removal, as shown in Figure Cycle loss works better in comparison but still has lower edit success than ours, shown Figure 10 The overall VIEScore on GEdit Bench for LPIPS and cycle loss is 4.52 and 5.21 respectively, compared to 6.10 for Ours. However, anticipate that as VLMs improve, with the capability of nuanced, pixel-level assessment, will also help improve the input-edit alignment in method without requiring additional regularization.
VLM supervision for difficult editing tasks. While NP-Edit can successfully edit images for various challenging edit-types like actions (2nd last row in Figure 2 and shape changes (Figure 12 (a)), our method is fundamentally upper bounded by the VLM's is ability understand and reason about image editing. Consequently, currently fails at tasks requiring complex spatial reasoning (c.g.. moving objects), as VLMs struggle to provide meaningful feedback in these scenarios. We show sample
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basiggeuund appear
the plate
survesl instead of straight
Malez the rouundel spherieal
wuug right side of frame
(a) Edit type: shape change V
(b) Edit type: move an object X
Figure 12: Challenging and difficult editing tasks. Our method can perform various challenging edits as well, like shape change shown in (a). But we fail other difficult tasks requiring spatial reasoning, such as moving objects, where VLM-based gradient feedback is less reliable.
(b). As unified generative models and Vision-Langue Models advance, the calructtionfollowiinn capabilities learned through our NP-Edit training framework are expected to improve correspondingly.
failure case examples in Figure (b))..
D DATASET CONSTRUCTION DETAILS
Each tuple our dataset = ((yi, Ci, ,, ) consists of real reference-image, y. correspond- ing edit instruction, and text prompt corresponding to the reference and edited image, ex and cx respectively. We use text-image dataset corpus to select reference images. Given a reference image, prompt Qwen-2.5 2w5n-22.-32BB VLM to suggest different possible editing instructions. The system and user prompt for it are as follows:
Role: system, Content: You helpful assistant and expert in image editing.
Role: user, Content: Task: As a researcher in image editing, your task is to generate simple editing instructions based the given image.
The edit types you can use include: local color change, local texture, 3) adjust (shape change), 4) add, 5) remove, 6) replace, 7) bg, 8) style, 9) action, and 10) text manipulation
**Important** Ensure that you create a balanced distribution of these edit types when generating the instructions. Each example should utilize a different edit type, and the edit types should be evenly distributed across all examples.
When using the "add" edit type, DO NOT USE vague placements like 'near'. 'under', or 'beside', instead, specify the exact location where the object should placed. For example, instead of "add castle near the trees" use "add castle in the clearing between the trees".
Ensure that each instruction straightforward and points single, clear edit change. Avoid complex or multi-step instructions.
Avoid Redundancy **; Make sure to introduce diversity in the edit instructions.
Given the input image, could you generate simple edit instructions for different possible edit types by following the "format" of examples below and based on what you have seen the image?
Here examples showing the of various edit types: Good example 1: (color change example) Good example 2: (texture change example)
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Good example (adjust shape example)
Good example 4: add example)
Good example 5: (remove example]
Good example 6: rreplace example]
Good example 7: bg example}
Good example 8: (style example
Good example 9: [action example}
Good example 10: (text manipulation example)
Bad Examples: the edit instructions hard/impossible to perform well, or mention vague terms that make the editing model struggle to perform well, and you should not follow. Bad example
Instruction: make this dog look like it's ready for formal evening out?
Type: add
Reasoning: This instruction is bad because it does not mention the exact changes that are needed to make the dog look like it's ready for a formal evening out.
Bad example 2:
Instruction: remove the balloon [given image of only balloons white background)
Type: remove
Reasoning: This instruction is bad as it removes the only object the image.
* *Important Considerations
1. Avoid repetition of specific phrases: Do not reuse examples or themes from the above examples. Create entirely new and diverse themes and scenarios.
2. Logical Flow: Ensure that each instruction is logical and makes sense given the image.
3. Specificity in Insertions: When adding objects, use precise placement (c.g.. "in the sky" or "on the lake"). Avoid vague terms like "next to",, "around", or "near".
4. Balanced of edit types: Use variety of edit types such as [insertion], [replace], [local texture], [shape changel. [style]. [remove], [local color change], and [bg]. Ensure an even distribution of these edit types across your examples.
Diverse scenarios: Introduce variety in the scenarios, such as futuristic, historical, magical, surreal, or natural settings. Avoid overusing common tropes. 6. DO NOT suggest instructions that change a very small/mintte part of the image.
Could you now generate 4 examples of **new, creative, and contextually relevant edit instructions by following the format above? Avoid using the specific phrases, themes, or scenarios from the examples provided above.
**Each example must use different edit type** from the ones listed above. Also, make sure to use each edit type equally across all generated examples.
Finally, you should make the edit instructions as simple as possible so that the downstream editing model is able work well.
In the above prompt. for the good examples, randomly select edit instruction for each editing type out of a fixed set of manually defined edit instructions. Given edit instructions for each image, again prompt the VLM check the validity of the edit instruction and, if valid, to suggest a possible caption for the edited image. The system and user prompt for this is:
Role: system, Content: You are a helpful assistant and an expert in image editing.
Role: user, Content: Task: As researcher in image editing, given the input image, edit type, and the edit instruction. your task is to check if given edit instruction is valid and can be applied to the image. If is valid, generate a descriptive caption for what the image would look like after applying the edit instruction. If it is not valid, return "invalid" and explain why is not valid, and output "NA" for the edited image caption.
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An edit instruction is invalid if it:
1. mentions to modifyteemvvc/rppacc an object that is NOT PRESENT in the image. is TOO HARD make editing model to understand and perform well, e.g., "remove any visible accessories."
DOES NOT change the image in any meaningful way, e.g., given the image of a forest, "change the background to dense forest."
For the "remove" edit type:
DO NOT mention the object that is removed during the edit in the edited image caption. For example, given image of cat in living room sofa with the edit type "remove" and edit instruction: "remove the cat"
Bad Example: A cat is removed from the sofa in living room. Good Example: A living room with a sofa.
Given the edit instruction and the original caption:
Edit type: fedit type)
Edit instruction: (simple edit instruction]
Output format:
Validity: ...
Reasoning:
Edited image Caption:
Please provide a concise but complete caption describing the edited image. Focus on the changes that would be made according the edit instruction.
Here are some more examples:
Example 1:
Edit type: bg
Edit instruction: change the background to sunset view
Validity: valid
Reasoning: The edit instruction is valid because it adjusts the current blue sky to sunset view, which meaningful change.
Edited image caption: A park with sunset view. People are walking around in the park.
Example 2:
Edit type: remove
Edit instruction: remove the wine glass
Validity: invalid
Reasoning: The edit instruction is invalid because it mentions removing a wine glass that is not present in the image.
Edited image caption: NA
* Important *
1. DO NOT use instruction words like replaced, added, removed, modified, etc. in the caption.
Keep the caption general to explain any possible images resulting from the edit instruction.
Only output the validity, reasoning, and edited image caption. Do not include any other text explanations.
After filtering the list of generated editing instructions using the above procedure. final dataset consists approximately 3M unique reference images with corresponding editing instructions spanning the 10 edit sub-types. Within the constraints of our available computational resources, this represents the largest dataset we were able to construct.
For the customization task, first instruct the VLM, to identify if the image has prominent object in the center. We provide an in-context sample image as well to the model. The exact system and user prompt for this is:
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Role: system, Content: You are a helpful assistant and an expert in image personaliza- tion/eustomizttonn.
Role: user, Content: Task: You are assisting in research project on image personalization. Your goal is to evaluate whether the SECOND image contains a **single, uniquely identifiable object** prominently positioned near the center of the frame.
The FIRST image (image' path1) is an example of valid case. The specific object category in the second image can be different focus only on **object uniqueness*# ** and *imagee composition**. **
Good examples include object categories that can be personalized, have unique texture, and not general objects: Backpack, purse, toy, cat, dog, cup, bowl, water bottle, wearables, plushies, bike, car, clocks, etc.
Bad examples include object categories that are general objects, and different instances of the category can not be distinguished: Tree, building, door, flowers, food, vegetables, fruits, natural scenes, roads, etc.
** *Important Considerations:#*
1. The object should be clearly recognizable and **visually distinct** from the background.
The object should be **near the center* of the image.
3. The **entire object** should be visible it should NOT be tight zoomed-in erop.
The background can be natural but should not be overly cluttered or visually distracting.
5. The image should feature **single primary object** multiple equally prominent objects.
Could you now judge the SECOND image and only provide the output, reasoning, and object name, in the following format: Output: True/False
Reasoning: Brief explanation
Object Name: The name of the object (e.g.. "backpack", "cat", "toy").
If the VLM response predicts a valid image, then query it again suggest a new background context for the object category as follows:
Role: system, Content: You are helpful assistant and an expert in image personaliza-
tion/customization.
Role: user, Content: Given image of object category, you have suggest three DIVERSE background captions for the object. Provide detailed description of the background scene. Only suggest plausible backgrounds. DO NOT add the object name in the caption. DO NOT use emotional words in the caption. Be concise and factual but not too short. DO NOT mention the object name in the output captions. the object is not thing, but scene, then output None.
Example background captions for "White plastic bottle" are:
near the edge of a marbled kitchen counter, surrounded by a cutting board with chopped vegetables, salt shaker, and a stainless steel sink in the background. 2. rests on a tiled bathroom shelf, accompanied by a toothbrush holder, a mirror with foggy edges, and shower curtain partially drawn open.
Example background captions for "a blue truck" are:
1. parked beside graffiti-cover brick wall under cloudy sky, with city skyscrapers rising in the background.
2. resting in grassy field surrounded by wildflowers, with distant mountains and a golden sunset the background.
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Object:
{object category name}
Output:
2.
E TRAINING IMPLEMENTATION DETAILS
E.1 LOCAL-IMAGE EDITING
Training byperparameters. We train a batch-size of 32 using Adam (Adam al. 2014) optimizer with learning rate of 2 x 10-6, B1 as O, and Bg as 0.9, on 32 A100-80GB GPUs. The few-step sampling time schedule, [1,, t3, 22, {i] is to 11.00.90, 0.70, 0.47]. shifted towards more noisy region (Esser et al., 2024). We train for total of 10K iterations with Naux 10, i.e., auxiliary network, Ag. being updated 10 times for every generator, Gg. update, following similar strategy adopted in DMD2 (Yin et al.) 2024a). We train with the identity loss (Section 4.3 for 250 iterations. For faster convergence, the first 4K training iterations are trained with a single step prediction (I = in Line 3 of Algorithm D. and then we start the 2-step unrolling of the diffusion trajectory. The final loss is a weighted combination of VLM--aseed editing loss and distribution matching loss with AVLM 0.01 and λDMD = 0.5. During training, we also add "do nothing" editing task with L2 loss between the input and edited image as regularization with a 1% probability. This helps the sample
model learn to maintain consistency between input and edited images. During training, the editing instruction corresponding to each subtype uniformly, except removal, which is sampled with 25% probability. This because, empirically, we observe that removal is more difficult than other edit-types like Color change.
Template questions for VLM-based editing loss. As mentioned in the main paper, we evaluate VLM-based loss questions per edit type. Specifically for any edit type except removal, the following template:
Role: user, Content: You are professional digital artist and an expert image editor. You will be provided with two images.
The first being the original real image, and the second being an edited version of the first. The objective to evaluate if the editing instruction has been executed in the second image.
Editing instruction: (edit instruction] }
Answer with a Yes or No.
Note that sometimes the two images might look identical due to the failure of image editing. Answer No in that case.
Role: user, Content: You are professional digital artist and an expert image editor. You will be provided with two images.
Answer with Yes No if the second image is exactly the same the first image. IGNORE the changes in the second image because of the edit: {edit instruction). Everything else should be the same.
For the removal edit-type, we change the first question to explicitly ask about the presence of the target object to be removed, with the ground truth answer in this case being No. We find it to be more effective than generic template.
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Role: user, Content: You are professional digital artist and an expert image captioner. You will be provided with an image. Answer with Yes or No if the image has {object name).
E.2 FREE-FORM EDITING (CUSTOMIZATION)
Training hyperparameters. We reduce the warmup iterations for which we train with the identity loss to 100 in this case, since customization often requires more drastic changes in the output image compared to the input reference image. Further, we increase \DMY = instead of 0.5 as in the case of local image-editing. The rest of the hyperparameters remain the same. Both during training and inference, the input text prompt to the few-step generator, Gg. is in the following template: Generate the main object shown the first image in a different setting and pose: background scene description). We train the 4-step model for 10K iterations. For the 8-step model, we fine-tune for 5K additional training steps starting from the 4-step model.
Template questions for VLM-based editing loss. Here, we modify the questions instead evaluate if the background context and pose are different in the generated image, i.e., editing success, and if the object identity similar, i.e., image alignment and consistency between the input reference and edited image. The exact questions are as follows:
Role: user, Content: You are a professional digital artist and an expert in image editing. You will be provided with two images.
Answer with Yes No if the fobject name) in the second image is in different pose and location than in the first image. Note that sometimes the second image might not have the same object because of the failure of image editing. Answer No in that case.
Role: user, Content:
You are a professional digital artist and an expert in image editing. You will be provided with two images.
Answer with Yes No if the {object' name) in the second image is the exact identity, with similar color, shape, and texture as in the first image. Note that sometimes the second image might have the same object because of the failure of image editing. Answer No in that case.
F OTHER BASELINE DETAILS
Flow-GRPO (Liu et al. 2025a). We follow the open-source implementation of Flow-GRPO and train with the same computational budget as our method, i.e., across A100 GPU nodes (32 GPUs) and 2.5 days of training. The final model is fine-tuned from pre-trained image-editing model for 5K iterations. During training, we collect 16 images per-prompt with 12 denoising steps (28 during inference) for computing the mean and standard deviation in GRPO (Shao et al. 2024). Following their official implementation, we train with LoRA (Hu al. 2022) of rank 32, a 64, learning rate 1 x 10 and use the VLM to edits scale of 0 to 9, which is normalized between 0-1 to get the final reward. The exact prompt used to query the VLM is derived from VIEScore (Ku et al. 2024 and is shown below.
Role: system, Content: You are a helpful assistant and an expert in image editing.
Role: user, Content: You are a professional digital artist. You will have to evaluate the effectiveness of Al-generated edited image(s) based on given rules.
You will have to give your output in this way (Keep your reasoning VERY CONCISE and SHORT):
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score :
reasoning
RULES:
Two images will be provided: The first being the original real image and the second being an edited version of the first.
The objective is to evaluate how successfully the editing instruction has been executed in the second image.
Note that sometimes the two images might look identical due the failure of image edit.
From scale to
A score from 0 to 9 will be given based on the success of the editing. (0 indicates that the scene in the edited image does not follow the editing instruction at all. indicates that the scene in the edited image follows the editing instruction text perfectly.)
Editing instruction: fedit instruction]
Supervised Fine- Tuning We train with the standard velocity prediction flow-objective for 30K iterations with batch-size of 32 and learning rate 2 10-6 with linear warmup of 2K iterations. To enable classifier-fre guidance, we drop the image and text conditions 10% of the time.
Sampling parameters for local image-editing baselines. We follow the open-source implemen- tation sample images from all the baseline models for the benchmark evaluations. The turbo- edit (Deutch et al. 2024) baseline requires a caption corresponding to the edited image as well, and use 2-5en-2.5-22B VLM generate these captions for GEdit-Bench images.
Sampling parameters for customization baselines. We follow the open-source implementation to sample images from all the baseline models for the benchmark evaluations. In the case DSD (Cai al. 2025), it employs Gemini-1.5 convert the input user-prompt into detailed prompt. However, we skipped this step for a fair evaluation with other methods, which do not use any prompt rewriting tools. In the case of SynCD (Kumari et al., 2025), though it supports multiple reference images as input, we evaluated with single reference image, to keep the setup similar to other baseline methods and Ours. For sampling images with OminiControl (TTn al. (025) and DSD (Cai et al.) 2025), we follow their recommended prompt setting and replace the category name with "this item"
G USE OF LLM IN PAPER WRITING.
We primarily use ChatGPT as a grammatical aid and for polishing paragraphs, instructing it to "refine the text without adding or omitting any relevant information."
H REPRODUCILITY OF RESULTS
We provide full implementation details of our method, including dataset construction, training algorithm, and hyperparameters in Section and Appendix E A pseudo-code description is also included in Algorithm [ Together, these are intended to facilitate the reproducibility of all results reported in our paper.
I ETHICS STATEMENT
Our work introduces a training framework for fine-tuning text-to-image models into few-step image-editing model without using paired supervision. By enabling few-step sampling, our method improves inference efficiency and reduces computational cost. Nonetheless, the broader risks of generative models, such as creating deepfakes and misleading content, also apply to our approach. Possible ways mitigate this watermarking generative content Fermandez al. (2023) and reliable detection of generated images Wang et al. (2020): Corvi et al. (2023); Cazenavette et al. (2024)
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Input reference image
Owen--mage-Edit (50 step)
Step 1x-Edit (4 step)
FLUX. 1Kontext (4 step)
Owen--mage-Edd (4 step)
Ours (4 step)
Adjust the baokeground to beRch
HO LLY WOOD
LAND
HOLLY LAND
IIAI HOLLYLAND
Replace with
REMOVE the red dum.belll
Add pair of sunglasses style
Convert this image iutto Av. aviine style
Change the color of the goat yellow
Make the skild pout
Replace the benok's waterrial with warble
Figure 13: Qualitative comparison GEdit-Bench We show results of and baseline image-editing methods under the few-step sampling setting. For comparison, we also show results the best method with multi-step sampling, as measured by the quantitative metrics (Table I in the *st column. Our method performs on par or better than bascline methods across different edit types in the few-step setting.
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Input image
Qwen Image-areee (50 step)
Owen-Image Eddgee-Fitt
(4 step)
(4 step)
(4 step)
(4 step)
Change the tackground from a clear blue sky with bare branches to sunset sky over lush forest
Add coffee oup the table in the foreground
change the woodew. surface backggruund to marblee opunntertop
REMoOEE the horse in the foreground
Replace the sheep the imagge with dieer
Transfer the image into dranaatic cherooal drawing style
reaise person's right hand
Figure 14: Qualitative comparison ImgEdit-Bench, We show results of our and baseline image-editing methods under the few-step sampling setting. For comparison, also show the results of the best method with multi-step sampling, as measured by the quantitative metrics (Table the 1 column. Our method performs on par or better than baseline methods across different edit types in the few-step setting.
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Input reference image
Owen-Inage-Edit (50 step)
FLUX: 1Kontext step)
OminnConntrl (8 step)
Owen-Image-Edit step)
Ours (8 step)
A backpack on top of purple nug in a forest
stuffed mounmtai.
baokground
A toy with city backegroood
dog with biuce house backeground
bowl the beack
A purple wizard outfit
A backpack with Siffel tower the baokgroundd
Figure 15: Qualitative comparison on DrcamBooth. We show results of our and baseline methods under the few-step sampling setting. For comparison, we also show the results of the best method with multi-step sampling. measured by the quantitative metries in the first column. Our method performs comparably with baseline methods on identity alignment while having better image fidelity across different concepts in the few-step setting.
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Input image
Qwen Image-Frit (50 step)
Stee1x-Fdit (4 step)
FLUX (4 step)
Owen-Image (4 step)
(4 step)
Make the bird spread its wings
Makes the giraffe eat the grass below with its head pointing downwwards
Make the dag jump
Havve the coople holstnng their hands together
change the bAnanA be rotten
11
Chanae the into
Make the teddy bear pushups
Figure 16: Qualitative comparison TED-Bench. We show results of our and baseline image-editing methods
under the few-step sampling setting. For comparison. also show the results of the best method with sampling, as measured by the quantitative metrics (Table in the 1 column. better than baseline methods across different edit types in the few-step setting.
multi-step
method performs on par or
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Reviews:
[{"review_id": "Dn5flfrPTT", "reviewer": "Reviewer_anZe", "summary": "The paper proposes NP-Edit, a novel framework for training image editing diffusion models without any paired image data. Instead of relying on costly or synthetic edit supervision, NP-Edit trains a few-step diffusion model using direct gradient feedback from a Vision-Language Model (VLM), which evaluates whether an edit follows a natural language instruction while preserving unchanged content. In addition, the method incorporates Distribution Matching Distillation (DMD) to align edited outputs in the manifold of the pretrained T2I model. Experimental results show that NP-Edit achieves editing quality competitive with state-of-the-art methods..", "questions": "Please check the weakness section. It would also strengthen the paper to include a comparison with the recently released I2I model *Nanobanana*.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "S1. In the Abstract and Introduction section (Sec. 1.), the motivation for training an image editing model using the set of unpaired images are clearly explained. The paper is built on solid and strong motivation and requiredness of the task.\n\nS2. The proposed components (loss functions) are plausible and details of VLM usage is clearly released in the Appendix. The idea of implementing unpaired training setup using the prior knowledge of VLM and manifold constraint is a novel approach.\n\nS3. Related to S2, each loss function plays a critical role for performance improvement, as explained in the Ablation study section (Table 3). The paper clearly addresses the requiredness of each proposed loss term.\n\nS4. The proposed method shows strong qualitative results against baselines, including image customization tasks. Quantitative results also reported, which shows the outstanding performance on GEdit-Bench dataset and on-part evaluation result on customization task.", "weaknesses": "W1. The method relies on the Vision Language Model (VLM) to judge whether the edited results of the model are correct. If the VLM misinterprets the image or instruction, the training signal becomes noisy, which can negatively impact convergence and editing accuracy. A more detailed analysis on VLM feedback dependency is required. How does the bias of VLM affect the model training procedure and overall performance? Is the Distribution Matching Distillation (DMD) enough to alleviate the bias of VLM?\n\nW2. I was wondering if the method is applicable to more difficult tasks, such as 1) object addition or duplication, 2) object moving, and 3) enlarging or shrinking the object size. Extensive experiments with additional tasks is required.\n\nW3. The method does not rely on supervision with paired before-to-after images. However, due to the absence of paired supervision, the edited output may drift from the reference object's identity, or introduce unwanted structural changes. Is there any analysis of the corresponding phenomenon? If the phenomenon that I mentioned is not crucial, please logically explain which part of the method alleviates this.\n\nW4. Quantitative analysis on computation overhead is required. How much time is required to train the model and edit a single image?", "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": "YmcRUMYpWc", "reviewer": "Reviewer_jdS1", "summary": "This paper aims at eliminating the image pairs which are requested by contemporary in-context DiT model, which are expensive to curate and scale. Given only a reference image and an instruction, the NPEdit model is supervised by the VLM's feedback signals. The authors further designed VLM-based editing loss and DMD loss. Rich experiments are conducted and NPEdit achieves state-of-the-art results under few-step setting.", "questions": "I  notice that there is  only one  non-rigid editing instruction ( which is referred as 'action') in the paper, \" a person waves a hand\". How about other non-rigid instructions? In TEDBench[1] editing benchmark,  Imagic[1] and Forgedit[2] could conduct various non-rigid editing, for example, let the dog sit/jump, or  let a bird spread its wings, let a bird look backward, close an open book etc. \n\nI am willing to adjust my rating if  the authors could  address my concerns.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The topic of eliminating the requirements of image pairs  is very important for In-context DiTs in image editing task. Because image-text pair are easy and cheap to collect and scale up. Instead, image pairs are expensive and rare. \n\n2. The model proposes VLM-editing loss and DMD loss, which seems novel. \n\n3. The results surprise me because according to my personal experience, training in-context DiTs with  image-text pairs usually leads to copy-paste problem. Yet the results on image customization task demonstrate that NPEdit could alter the spatial orientation and location quite well, which is interesting.", "weaknesses": "1. Inadequate experiments on non-rigid editing, which is refered to as 'action' in the paper. NPEdit only demonstrates one non-rigid editing, 'a person waves a hand', which is not persuasive. In a previous benchmark TEdBench[1], Imagic + Imagen[1]  and Forgedit + SD 1.4 [2] could conduct some hard non-rigid instructions on this benchmark, for example,  let the dog sit, jump, or  let a bird spread its wings, let a bird looking backward, close an open book etc.\n\n2. Facial identity preservation does not seem good. VLMs are generally not good at measuring facial similarity, far worse than specific face recognition models like arcface. Using VLMs' judgements as supervision signals may not be accurate. The facial identities in the editing results of NPEdit further confirm this doubt. \n\n3. The paper did not explain very clearly how to obtain the editing result fed to VLM during training. It seems like an inversion method. This step is very important in the training process and should be elaborated further. \n\n\nReference:\n\n[1] Imagic: Text-Based Real Image Editing with Diffusion Models\n\n[2] Forgedit: Text Guided Image Editing via Learning and Forgetting", "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": "WJQWpwz0vm", "reviewer": "Reviewer_kKVu", "summary": "This paper introduces NP-Edit, a new framework for training image editing diffusion models without any paired supervision. Instead of relying on input–output image pairs or synthetic datasets, NP-Edit leverages direct gradient feedback from Vision–Language Models (VLMs) to guide model optimization. The VLM evaluates whether an edit instruction is followed and whether unchanged regions are preserved, providing differentiable supervision. To maintain realism, the method also employs a Distribution Matching Distillation (DMD) loss to align generated images with the manifold of a pretrained diffusion model. The proposed framework achieves performance on par with supervised image editing diffusion models, despite requiring no paired data. The paper includes extensive experiments and ablation studies examining the influence of VLM backbone choice, dataset diversity, and loss formulation.", "questions": "Overall, this submission makes a conceptual and practical contribution to the field of text-guided image editing by eliminating dependence on paired training data through the use of VLM-based differentiable supervision. The approach is both innovative and impactful, offering a scalable alternative to traditional supervised paradigms. The authors are suggested to address the above weaknesses to further strengthen the work.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper addresses a well-recognized bottleneck in image editing, dependence on expensive, hard-to-scale paired datasets. The motivation is timely and relevant, given the increasing reliance on generative models in open-world applications.\n\n2. The idea of replacing supervised pairs with gradient-based supervision from a VLM is conceptually elegant. This formulation could generalize beyond image editing to other multimodal generation tasks.\n\n3. Competitive empirical performance under the few-step diffusion setting has been demonstrated.\n\n4. The author also includes solid ablation studies and well-motivated analyses.", "weaknesses": "1. Since supervision is entirely derived from VLM feedback, the model’s performance is directly tied to the accuracy, bias, and robustness of the chosen VLM. Any systematic bias (e.g., cultural or aesthetic preferences) or failure to interpret nuanced edit instructions could propagate into the trained model.\n\n2. Unrolling the few-step diffusion model during training and backpropagating through the VLM introduces potentially high computational and memory costs. The paper lacks a detailed analysis of training efficiency or scalability to high-resolution datasets.", "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": "fBqMXyS3s6", "reviewer": "Reviewer_JFwR", "summary": "This paper proposes NP-Edit, a training paradigm for instruction-following image editing that removes the need for paired before/after supervision. The core idea is to unroll a few-step diffusion generator during training and optimize it end-to-end with differentiable feedback from a vision–language model (VLM) that answers templated Yes/No questions about (i) whether an edit was executed and (ii) whether identity/context was preserved; a distribution-matching distillation (DMD) loss to a text-to-image teacher constrains realism. The method trains a lightweight 2B-parameter few-step editor (4 steps by default), with results reported on GEdit-Bench and DreamBooth customization showing competitive performance with larger supervised baselines under few-step sampling, plus ablations showing the complementary roles of VLM feedback and DMD.", "questions": "* **Judge diversity.** Beyond LLaVA-OneVision-7B, what happens if you ensemble multiple VLMs (or mix families) as teachers during training? The scaling table shows benefits from stronger VLMs; an ensemble could mitigate single-judge biases—did you attempt this?  \n* **Human evaluation.** Can you provide a small-scale user study (e.g., 100 instructions × 3 systems, pairwise preferences for edit correctness and fidelity) to corroborate VIEScore-based gains and address potential metric bias? \n* **Failure modes by edit type.** The ablations suggest that Removal and certain style edits are harder. What specific misbehaviors occur (e.g., partial removal, texture spillover), and can targeted question templates or auxiliary losses mitigate them?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* **Originality.** The paper is, to my knowledge, the first to train a general instruction-following image editor using **gradient** feedback from a VLM (rather than scalar RL rewards or hand-crafted objectives), coupled with DMD to keep edits on the teacher manifold; this is a non-trivial and timely contribution in the post-training space.  \n* **Quality.** The technical formulation is clear: a two-step unroll to produce cleaner intermediate states for reliable VLM judgments; a binary-logit loss over Yes/No tokens; and a KL-motivated DMD term implemented with an auxiliary velocity predictor. Ablations convincingly show that removing either VLM loss or DMD substantially degrades performance.    \n* **Clarity.** The paper supplies an end-to-end algorithm box, precise templates for VLM prompts/questions, and concrete training hyperparameters (optimizer, learning rate schedule, warm-up, weighting, sampling times), all of which aid reproducibility.  \n* **Significance.** Competitive results with a 2B few-step editor against much larger systems under few-step sampling, and solid customization performance, suggest a practical path toward data-efficient editing without paired supervision. The explicit focus on few steps is also valuable for latency-sensitive applications.", "weaknesses": "* **Evaluation dependence on automated judges.** Most quantitative evaluation relies on VIEScore (GPT-4o-based) and related automated metrics; no human perceptual study is presented. This raises concerns about metric bias and circularity (a VLM both trains and evaluates), especially for nuanced fidelity judgments. Adding a user study or double-blind human pairwise comparisons would strengthen claims. \n* **VLM supervision reliability and calibration.** The paper shows VLM judgments are unreliable on noisy/blurry intermediates—motivating few-step training—but broader issues remain: sensitivity to prompt wording and model-specific biases. More analysis of calibration or robustness (e.g., multiple VLM judges, temperature/decoding settings) would be helpful.  \n* **Resource/engineering overhead.** Keeping a VLM in GPU memory is noted as a limitation; concrete throughput/memory numbers for typical hardware (and at training vs inference) are not reported, which makes it harder to assess deployability.", "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)