Visual Autoregressive Modeling for Instruction-Guided Image Editing
Authors: Qingyang Mao
Qi Cai
Yehao Li
Yingwei Pan
Mingyue Cheng
Ting Yao
Qi Liu
Tao Mei
Venue: ICLR 2026 Poster (2026)
Keywords: Instruction-Guided Image Editing
Visual Autoregressive Modeling
Primary area: generative models
TL;DR: The first large-scale visual autoregressive model for instruction-based image editing, delivering precise results with remarkable speed.

Abstract: Recent advances in diffusion models have brought remarkable visual fidelity to instruction-guided image editing. However, their global denoising process inherently entangles the edited region with the entire image context, leading to unintended spurious modifications and compromised adherence to editing instructions. In contrast, autoregressive models offer a distinct paradigm by formulating image synthesis as a sequential process over discrete visual tokens. Their causal and compositional mechanism naturally circumvents the adherence challenges of diffusion-based methods. In this paper, we present VAREdit, a visual autoregressive (VAR) framework that reframes image editing as a next-scale prediction problem. Conditioned on source image features and text instructions, VAREdit generates multi-scale target features to achieve precise edits. A core challenge in this paradigm is how to effectively condition the source image tokens. We observe that finest-scale source features cannot effectively guide the prediction of coarser target features. To bridge this gap, we introduce a Scale-Aligned Reference (SAR) module, which injects scale-matched conditioning information into the first self-attention layer. VAREdit demonstrates significant advancements in both editing adherence and efficiency. On EMU-Edit and PIE-Bench benchmarks, VAREdit outperforms leading diffusion-based methods by a substantial margin in terms of both CLIP and GPT scores. Moreover, VAREdit completes a 512$\times$512 editing in 1.2 seconds, making it 2.2$\times$ faster than the similarly sized UltraEdit. 
Code is available at: https://github.com/HiDream-ai/VAREdit.

Published as a conference paper at ICLR 2026
VISUAL AUTOREGRESSIVE MODELING FOR INSTRUCTION-GUIDED IMAGE EDITING
Qingyang Mao'; Qi Cai", Ting Yao?, Li Tao Mei
Yehao Li?, Yingwei Pan?! Mingyue Cheng',
Univvrsiity of Science and Technology of China, HHiDream Inc. maoqy0503 @ mail.ustt.edu.cm (mycheng, qiliuql) @ustc.edu.cn, [qqcaiqi, liyehao, pandy, tiyao, tmei) } @ hidream.aai
ABSTRACT
Recent advances in diffusion models have brought remarkable visual fidelity to instruction-guidee image editing. However, their global denoising process inher- ently entangles the edited region with the entire image context, leading to un- intended spurious modifications and compromised adherence editing instruc- tions. In contrast, autoregressive models offer a distinct paradigm by formulating image synthesis as a sequential process over discrete visual tokens. Their causal and compositional mechanism naturally circumvents the adherence challenges of diffusion-based methods. In this paper, we present VAREdit, a visual autore- gressive (VAR) framework that reframes image editing next-scale prediction problem. Conditioned on source image features and text instructions, VAREdit generates multi-scale target features to achieve precise edits. A core challenge in this paradigm is how to effectively condition the source image tokens. We observe that finest-scale source features cannot effectively guide the prediction of coarser target features. To bridge this gap, we introduce a Scale-Aligned Reference (SAR) module, which injects scale-matched conditioning information into the first self- attention layer. VAREdit demonstrates significant advancements in both editing adherence and efficiency. On EMU-Edit and PIE-Bench benchmarks, VAREdit outperforms leading diffusion-based methods by substantial margin in terms of both CLIP and GPT scores. Moreover, VAREdit completes a 512x512 editing in 1.2 seconds, making it 2.2x faster than the similarly sized UdtraEdit, Code is available at: https: httptggtttttu.ueg/// thub. com/HiDream ai/VAREdit
INTRODUCTION
Instruction-guii guided image editing (Brooks et al. 2023 Zhang et al. 2023 Sheynin et al. 2024) rep- resents a significant advance in generative AI, shifting the paradigm from pure synthesis towards fine-grained interactive control. This progress has been propelled by two primary factors: the cu- ration of large-scale, high-quality editing datasets (Ge ct al.
2024
Ye et al., 2025) and the rise of
diffusion models (Ho et al. 2020 Peebles et al.) 2203 Labs
2024 the mainstream architecture.
In particular, the impressive visual fidelity largely derived from the iterative denoising process in diffusion models. However, this core mechanism also imposes critical limitation. The global nature of the denoising process often leads to unintended spurious edits, causing modifications to "bleed" into regions that should remain unchanged. Simultaneously, the multi-step denoising pro- cess incurs substantial computational cost and limits real-time practical use.
In contrast to diffusion models, autoregressive (AR) models (Esser et 2021: Sun et 2024 Pang et al. (025) offer fundamentally different paradigm. Instead of holistic iterative refinement, they synthesize an image causally sequential token-by-token manner. This compositional gen- eration process is a natural fit for image editing. Specifically, it provides a flexible mechanism for preserving unchanged regions while precisely modifying edited regions, addressing the entangle- ment problem of diffusion models. More encouragingly, the pioneering visual autoregressive (VAR)
"This work was performed at HiDream.ai.
Corresponding authors.

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Published as a conference paper at ICLR 2026
Add flowers to the woman's hair
Charge the baseball into crown
Remove the bunny from the cup
Happy Birthday
Change horse be wooden
Write 'Heppy Birthday' cream on the cake
Make the clown laugh
Add stained glass window style to two young children holding hands
Make the violinist wear a ned dress
Turn the into early dusk with fisher rewing a boat on the river
VAREdit achieves high-precision performance in instruction eestucttionguided image editing. It excels across diverse editing scenarios, including object-level modifications (addition, replacement, removal), attribute changes (material, text, posture, style, color) and complex compositional edits.
Figure 1:
modeling (Tian 2024) breaks the spatial structural degradation through the scale-by-scale pre- diction strategy, leading to high-quality image generation. While the VAR paradigm has shown
competitive performance in image synthesis (Kumbong al., 2025 Han et al.) 2025). its potential for cnttrrucctionguuided image editing remains largely unexplored. Early training-free attempts (Wang)
ct al. 2025) repurpose text-to-image models but lack the task-specific knowledge required for robust
editing, causing them to lag significantly behind iiffusiion-bssd methods (Hou et al. 2024 Zhao
(ct al. 2024a: Zhang et al.) 2025b).
We address this critical gap by introducing VAREdit, novel tuning-based VAR framework for instruction-guided image editing. VAREdit reframes image editing as a next-scale prediction prob- lem, which generates multi-scale target features to achieve precise image modification. A central challenge in this paradigm is how to effectively condition the model on the source image. A straight- forward approach is the lengthy input sequences. While the finest-scale-only conditional setting provides sufficient in- formation and eliminates the redundant input tokens, creates severe scale mismatch since high- frequency details may disrupt the prediction of coarse target features. To resolve this issue, we perform systematic analysis of inter-scale dependencies and uncover key insight: the model's sensitivity to scale-aware conditioning is concentrated in its first self- attentieno layer. Accordingly, we propose the Aligned Reference (SAR) module, a mechanism that injects the matee- source information only in the first self-attention layer, while other layers still use the finest-scale condition. As demonstrated Figure [ this design enables VAREdit to achieve exceptionally pre- cise editing across diverse challenging editing scenarios. Our contributions threc-fold:
full-scale source conditions, yet is computationally inefficient due to
We introduce VAREdit, novel tuning-based visual autoregressive model for instruction- guided image editing. To the best of our knowledge, are among the first to introduce next-scale visual prediction into instruction- -uidded image editing.
We identify a scale-mismatch issue the finest-scale conditioned VAREdit and propose the SAR module as an effective solution.
VAREdit attains a new record on standard editing benchmarks, outperforming leading dif- fusion models in both editing adherence and generation efficiency.
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Published as a conference paper at ICLR 2026
2
RELATED WORK
2.1 INSTRUCTION-GUIDE IMAGE EDITING
Instruction-guian -uided image editing (Brooks et al. 2023 Zhao et al. 2024a Yu et al. 2025 Cai al, 2025) seeks to modify an image according to user's textual instruction, which differs from caption- based approaches (Hertz et al. 2022, Feng et al., 2025) that often resynthesize an entire image from global description. This field is predominantly driven by diffusion models and seminal work In- structPix2Pix (Brooks et al., 2023) established a powerful paradigm by channel-wit concatenating source and target images. Subsequent research advanced this by curating larger-scale datasets with "expert" models (Zhang et al. 2023; Sheynin et al. 2024 Zhao et al. 2024a| Ge et al.) 2024; Ye eet al., 2025) exploring alternative conditioning, like spatial concatenation for large-motion edits (Zhang et al. 2025b; Cai et al., (025). However, diffusion-basee
models often produce unintended modifications and suffer from slow iterative sampling. To address these limitations, recent studies have widely explored autoregressive (AR) models for instruction-guided image editing. Some ap- proaches leverage the capabilities of Multimodal Large Language Models (MLLMs) in high-quality semantical alignment (Fu et al.) [024]] [Zhou et al., 2025) for further diffusion-basee visual gener- ation processes. Other methods employ AR-based frameworks directly for the image editing task, integrating knowledge distillation (Mu et al. 2025), mask-aware region conditioning (Wu et al.) 20255) or in-context reasoning for interactive manipulation (T.i et al.) 2025). While these methods have achieved promising results, they still follow the vanilla next-token prediction paradigm with potential risks of structural degradation. Our work builds on the pioneering visual autoregressive (VAR) (Tian al., (024) architecture that follows the next-scale prediction paradigm, tuning- based approach that surpasses recent approaches in both edit quality and efficiency.
2.2 AUTOREGRESSIVE IMAGE GENERATION
Autoregressive (AR) models, a mainstay in language tasks, have been extended to image synthesis. They operate by tokenizing image into discrete sequence and then training Transformer model to predict the next token. Early works use vector quantization (VQ) for tokenization (Van Den Oord
ct al. 2017 Esser et al. 2021 Chang et al., 2022: Yu et al. 2022). while recent advances such as lookup-free quantization (Yu et al. [023 Zhao et al. 2024b) have improved performance with large vocabularies. The AR modeling paradigm itself has also made significant progress. For instance, MAR eL al. 2024) uses masked multi-token prediction to improve quality and speed, while Ran- dAR (Pang et al.] 2025) enables flexible token ordering for new capabilities such resolution trapolation. To better capture spatial dependencies, models like VAR (Tian et al., 2024) and Infinity (Han et al. 2025) introduced coarse-to-line prediction schemes. As AR approaches are well-suited for language modeling, they demonstrate a significant advantage in cnatucctionfollowiiin
perfor- mance (Lai al.) [025 Zhang et al.) 2025a) for image generation. Meanwhile, recent advances like hybrid parallelization and KV-scale mitigate the efficiency bottleneck (Kumbong et al.) [222]] Li et al. 2025). These innovations are enhancing both generation quality and efficiency, enabling AR models to emerge as strong competitors to diffusion models, particularly in tasks requiring high fidelity to complex, compositional prompts.
3 METHODOLOGY
We first review the visual autoregressive (VAR) modeling paradigm. Then, we introduce VAREdit, novel framework that reframes instruction guidded image editing multi-scale conditional gen- eration task. Finally, we analyze the challenges of various source image conditioning strategies and present the inspiration and the design of our Scale-Aligned Reference (SAR) module, targeted solution to the scale-mismatch problem in naive conditioning.
3.1 PRELIMINARY
Visual autoregressive (VAR) model generally comprises a multi-scale visual tokenizer and a Transformer- based generation model. The process begins with encoder E that maps an image I to a continuous feature representation F = {II E xxxx A quantizer Q then decomposes F into hierarchy of K discrete residual maps R Q(F). These maps follow a coarse-to-fine
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Published as a conference paper at ICLR 2026
Source Image
Multi scale Resifuals
aeff-attention Calculation
Edited Image
Fastisale Festure
Multi-scale
VQ Decoder
For Crees
VAR Transformer
Instruction Roebuck
Pooling
R 2010
Text Encoder
Rg Par
Target Image
Multi scale Residusts
Mut scale Featurte
Unithed
Ppolin
Multi-scal VQ Encodor
R: 209
Only for veferssce
Trumh
The overall architecture of VAREdit for instraction-guided image editing. VAREdit first encodes the images into multi-scale residuals by a shared vector quantizer and maps the instructions into textual token embeddings. These features are organized as the finest-scale source feature F K ((rre) the pooled textual representation F(1gt)) the coarse-to-fine target features (((ttt) and then sent to the VAR Transformer. The source feature F(are) is further sent to the Scale-Aligned Reference (SAR) module in the first self-attention layer, while the textual token embeddings are also used for cross-attention calculations of key and value matrices. The ground truth residuals R 1 guide the
Figure 2:
(tgt)
training of the last K output residuals Bi:k
(tgt) During inference, the residuals I2 (tgt) 1: are predicted
((lgt) and then decoded to the edited image. autoregressively, which are then aggregated to K
structure, where the spatial resolution (/, wx) of each residual map R3 increases with its scale index Then, the Transformer model predicts the residuals in an autoregressive manner (where the scale features are flattened to obtain the sequential representation):
K
p (RI:K)
II
p( (R. RR:--I).
Concretely, to predict the residual map for the next scale Re+1, the model first computes an inter- mediate feature representation F; by aggregating previously generated residuals R1:::
F+ Σ Up(Lookup(R., C), (h,
(2)
where Lookup(-,C C) represents retrieving vector embeddings from the learned codebook C and -p( denotes upsampling operation (e.g., bilinear upsampling). This cumulative fea- ture Fx is then downsampled (e.g., average pooling) to match the spatial dimensions the next scale, (h441,4*+1), Wx+1), creating the input for the next scale's prediction step: Fş = Down(F (ha+1, Wk+1)). The process is initiated with Fu, start-of-sequence representation de- rived from conditional embedding (e.g., class labels). Once all К residual maps Ri.K have been generated, they are used to compute the final feature map F Fx through Equation 2 Finally, decoder D synthesizes the output image DFF) as the predicted generation.
3.2 VAREDIT
We introduce VAREdit, framework that reframes instruction-guide image editing as conditional, multi-scale prediction problem. Figure2pprseent presents the overall architecture of VAREdit, which lever- ages pre-trained VAR model its foundation and autoregressively generates the target residual maps R (tgt) conditioned
source image (arc) and textual instruction t:
K
K
p( R
(tgt) (frc) 1:K
t) = II
p (
(tgt)
(tgt) 1:k-
(arc) ()
p R
(tgt)
(tst) 1:k-1
I (arc) t), (3)
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Published as a conference paper at ICLR 2026
where the second equation comes from the fact that F( integrates all previous & scales' residual information R (tgt) according to Equation 2 and we take the spatial- mential-dimension aligned form F(tgt) of 1-
the aggregated features as input. All scale Teatures are flattened to obtain sequential representation.
3.2.1 CONDITIONING SOURCE IMAGES
As for the specific design of VAREdit, the key challenge is how to effectively and efficiently incor- porate the source image ((re) to guide the multi-scale generation process.
condition the generation on the complete multi-scale features of the source image, F(are) This is achieved by prepending the sequence of all source tokens to the target sequence, which allows the model reference any scale source residual when generating the target residual. The conditional likelihood becomes:
Full-scale Conditioning. straightforward approach
1:K
K
(arc) (tst) ( t) = p(RE I:K p P
(tat)
t) - II
R
(tgt)
((gt)
p(are) t).
(4)
1ik-1
While this method provides comprehensive, scale-by-scale reference for the editing task, it is computationally expensive. Doubling the sequence length results quadratic increase Onn²) in the cost of self-attention, rendering it impractical for high-resolution editing. Moreover, providing multiple source scale features may introduce redundant or conflicting information for target feature prediction, potentially degrading editing quality.
Finest-scale Conditioning. To address the prohibitive cost of full-scale conditioning, we propose a more efficient strategy to condition solely on the finest-scale source feature, F(are) This approach is motivated by the hierarchical nature of the visual tokenizer: the finest scale encapsulates the most detailed, high-frequency information from the source image, which is often the most critical for guiding an edit. This simplification slims down the likelihood to:
(( (ggt) (sre), t) p(R
(tgt)
(arc) K t)
II
p P
(tgt)
((gt) lik
(arc) tt.
(5)
In this way, only the tokens from (erc) are prepended to the target sequence. While this strategy dramatically reduces sequence length and thus alleviates the computational bottleneck of the vanilla full-scale conditional setting, it introduces critical scale mismatch challenge. The model is tasked with predicting coarse gaassegrained target image structure while having access to only the fine grainerdied, local details in the source condition, which is insufficient for precise editing.
3.2.2 SCALE DEPENDENCY ANALYSIS
The critical scale mismatch challenge introduced by the efficient finest-scale approach raises fun- damental question: which source scales are truly necessary for high-fidelity editing? To investigate such scale dependencies of target residuals and source residuals, we perform diagnostic analysis of the self-attention mechanisms in model trained on the full-scale source features. This full-scale setting allows the model to freely attend to all source scales.
The self-attention heatmaps in Figure averaged across all heads and samples from PIE-Bench (Ju et al. (023),, reveal that different Transformer layers develop distinct attention patterns. In the first self-attention layer, when predicting tokens for a given target scale, the attention mechanism dis- tributes broadly, focusing heavily on the corresponding and all coarser source scales. This pattern indicates that the initial layer is responsible for establishing the global layout and long-range de- pendencies. This behavior shifts in deeper layers, where attention patterns become highly localized. They exhibit strong diagonal structures, suggesting that attention is primarily confined to tokens in the spatial neighborhood. This functional transition suggests a shift from global structuring to local refinement, where the model copies and adjusts high-frequency details. For this latter task, the fine-
grained information provided by F (arc) is sufficient. This motivates to design a hybrid solution that provides the scale-aligned reference in the first layer while all subsequent layers attend only to the finest-scale source. See Appendix A.7 for self-attention heatmaps of all model layers.
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Layer
Layer 2
Layer 4
Layer
Layer 12
Laver 16
Layer 20
Layer 24
Layer 28
Layer 32
1.0
Figure 3: Self-attention heatmaps of PIE-Bench samples based on the full-scale transformer across different layers. The dependency patterns differ in the first self-attention layer versus others, inspir- ing scale-aligned reference module.
3.2.3 SCALE-ALIGNES REFERENCE
Based on our analysis, we introduce the Scale-Aligned Reference (SAR) module, designed specif- ically to resolve the scale mismatch problem within the first self-attention layer. The core idea is to dynamically generate coarse-scale reference features by downsampling the single, finest-scale source feature map ((are) This creates set of reference features, each aligned to the spatial di- mensions of target scale:
F(ref) Down(F( (()),
(6)
During generation, when predicting the tokens for target scale the first self-attention layer com- putes queries Q (tat) and attends to a combined set of keys and values, which are derived from two sources: the newly generated, scale-aligned reference feature (ref)) and (ii) the causal history of previously generated target tokens. Specifically, the self-attention output Ó
(tgt) is computed as:
k
(tot) KL
(ref)T
KK
(tat)T
... K (tgt)
(tyt) k
= Softmax
v((rf)) V((gOT
(lgt))
Vd
k
where (K(ref), v(ref) are projected from the scale-aligned source reference F(ref) and (K( (tat) (tgt)) ) are from previous target scales. This method reduces the spatial size of atten- i:k-1 l:k-1)
tion scores before the softmax operation without changing the output sequence length. Crucially, this SAR mechanism is applied only to the first self-attention layer. All subsequent layers operate
using only the finest-scale conditioning, attending to F(are) and the target's history residuals. This allows VAREdit to capture crucial scale dependencies in aligned manner while achieving the efficiency of the finest-scale approach.
4 EXPERIMENTS
4.1 EXPERIMENTAL SETUP
Datasets. VAREdit is trained on a large-scale dataset 3.92 million paired examples, aggregated from the SEED-Data-Edil (Ge et al. 2024) and ImgEdit (Ye et al. 20255) datasets. From the SEED- Data-Ede dataset, we extracted all single-turn samples and decomposed multi-turn conversations into single-turn editing pairs. These pairs jet al., (2025) to remove instances with poor instruction followiin quality. Finally, all single-turn samples from ImgEdit were incorporated. Additional details regarding this data processing pipeline
then filtered using vision-language model (Team)

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Published as a conference paper at ICLR 2026
available in Appendix A.1
For evaluation, we assess VAREdit on four established benchmarks: 2024), including 3,589 samples across eight distinct editing types, including 700 samples that cover ten different editing types, (3)
(1) EMU-Edit (Sheynin et al.
(2) (Ju et al. ImgEdit-Bench (Ye et al.
(023),
2025 including 737 samples in the basic-edit suite covering nine editing suite, and (4) GEdit-Bend 2025), including 606 samples covering eleven extensive editing types.
types and 47 complex samples in the understanding- srrraandiinngnnnnnngeeiiiii
(English subset) (Liu et al.
Evaluation Metries. Standard benchmarks like EMU-Edit and PIE-Bench rely on CLie-based scores (Radford et al., 202T). EMU-Edit employs caption-image similarity (CLIP-Out.) and text- image directional similarity (CLIP-Dir.). whereas PIE-Bench measures similarity for the whole im- age (CLIP- (LIPeWhole) and the edited region (CLIP-Edit). However, these metrics often fail to capture important editing quality aspects, such as spurious modifications or incomplete edits. To address these shortcomings, we also adopt the evaluation protocol from OmniFdit (Wei et al. 2025), which uses GPT-4o (Hurst et al. 2024) as an automated judge to provide two key scores on scale of 0-10: (I) GPT-Success (Suc.): Measures the adherence to the editing instruction (higher is better) and (2) GPT-Overedit (Over.: Assesses the preservation of unedited regions (higher is better). Since model could achieve perfect GPT-Over. score by simply disregarding the edit instruction and outputting the original image, we further introduce GPT-Balance (Bal.) as an evaluation metric for overall editing performance, defined the harmonic mean of the GPT-Suc. and GPT-Over. scores. Detailed prompts and computation methods, along with evaluation details about ImgEdit-Bent Bench and are provided in Appendix AA.2
Compared Approaches. To ensure a comprehensive and rigorous evaluation, we assess VAREdit against several state-of-the-art uuning-based methods. Our comparative analysis includes broad spectrum of basic open-source methods: InstructPix2Pix (Brooks et al. (023). UltraEdit (Zhao
al. 2024ap. OmniGen (Xiao et al.] (025), AnySD YYu et al.j 2025). EditAR (Mu et al. 2025).
ACE+ (Mao et al. 2025). ICEdit (Zhang et al. (025b)) We also include four frontier image editing
approaches for comparison: GPT-44-lmage (Hurst et al.) 2024), Step1 X-Edit (Liu al.) 2025),
FLUXX11 Kontext (dev) (Labs et al. (2225) and Qwen-Image-Fie Edit (Wu et al., 2025a). The detailed descriptions of these compared approaches are provided in Appendix A..3
Implementation Details. Our VAREdit models are initialized with weights from the pre-trained Infinity model (Han et al., 2025) along with its bitwise multi-scale residual quantizer (Zhao et al., 2024b). To differentiate source image tokens from target image tokens, we introduce a positional offset of A = (64, 64) to the 2D Rotary Position Embeddings (2D-RoPE) for all source tokens. To investigate scaling properties,
develop two distinct model sizes: VAREdit-2B and VAREdit-SB. VAREdit-2B undergoes a two-stage training procedure: an initial 8k iterations trained at 256 x 256 resolution with batch size 1,536 and learning rate 6e 5, followed by 7k fine-tuning iterations at 512 x 512 with batch size 960 and learning rate 1.875e 5. The larger VAREdit-&B is trained directly 512 512 resolution for 60k iterations with batch size 1,536 and learning rate 6e 5. During training, we optimize the bitwise classifier loss over target residual token indices at each scale, following Infinity (Han et al.) 2025). As for inference, we employ a classifier-fre guidance (CFG) strength of 1 = 4 and logits temperature of 0.5. Please refer to Appendix A.4 for more implementation details.
4.2 QUANTITATIVE RESULTS
Superior Editing Quality. The quantitative results in Table I demonstrate the superiority of VAREdit in editing performance. Among basic open-source methods, VAREdit consistently out- performs all diffusion-based and autoregressive baselines on our primary metric, GPT-Balance, Our VAREdit-BB model achieves a GPT-Bal. score of 7.892 on EMU-Edit and 8.105 on PIE-Bench, surpassing the strongest competitor (ICEdit on EMU-Edit, UltraEdit on PIE-Benchi by 64.9% and 45.3%, respectively. This underscores VAREdit's 's ability to perform precise edits while preserving unchanged regions. On conventional CLIP- based metrics, VAREdit also consistently outperforms these baselines and secures the top scores on both EMU-Edit and PIE-Bench. Compared to the four frontier approaches, GPT-4o- performs best on both the two benchmarks due to the large scale of training data and model size. Among the open- source approaches, VAREdit-S8 -BB performs better than SteplXEEde and FLUX.1 Kontext (dev) according to GPT-scores and slightly lower than Image-Edi Edit (20B). We have further evaluated the performance of VAREdit on Bedit-Bench. Please refer to Appendix A.6 access the detailed results and analysis.
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Table 1: Quantitative results of VAREdit, leading open-source methods and frontier image editing approaches on EMU-Edit and PIE-Bench benchmarks.
EMU-EEdit
PlE--Bench
Method
Size
CLIP
Score
GPT Score
CLIP
Score Edit
GPT Score
Time
Out.
Dir.
Suc.
Over.
Bal.
Whole
Suc.
Over.
Bal.
InstructPix2Pix
1.1B
0.268
0.083
3,358
6.299
2.923
0.236
0.217
4.794
6.534
4.034
3.5s
UltraEdit
7.7B
0.278
0.095
4.881
7.704
4.541
0.247
0.220
5.831
8.350
5,580
2.6s
OmniGen
3.88
0.274
0.082
4.738
8.709
4.666
0.240
0.212
3.459
8.939
3.498
16.58
AnySD
2.9B
0.268
0.059
3.098
8,590
3.129
0.227
0.206
3,456
7.806
3.326
3.4s
EditAR
0.8B
0.273
0.064
3.582
7.260
3.305
0.242
0.216
5.070
8.116
4.707
45.5s
ACE++
17B
0.258
0.058
2.375
5.979
2.076
0.234
0.205
2.743
8.093
2.574
5.7s
ICEdit
17B
0.271
0.095
5.027
7.591
4.785
0.234
0.215
5.321
7.593
4.933
8.4s
VAREdit (256px)
2.2B
0.268
0.091
6.072
7.058
5.565
0,256
0,224
7.233
7.804
6.684
0.5s
VAREdit
2.2B
0.271
0.099
6.210
7.055
5.662
0.260
0.225
7.530
8.083
6,996
0.7s
VAREdit
8.4B
0.282
0.125
8,284
8.461
7.892
0.266
0.233
8.621
8.536
8.105
1.2s
StepIX-Edit
21B
0,280
0.126
7.799
7,843
7.081
0.254
0.226
7.919
8.069
7.35
12.86
FLUX.1 Kontext
12B
0.284
0.125
7.419
8.800
7.210
0.256
0.225
7.236
8.923
6.998
28.5s
Owen-Image Image-Ediii Edit
20B
0.286
0.133
9.113
8.598
8.550
0.263
0.232
8.789
8.567
GPT-4o-Image
N/A
0.286
0.117
9.517
9.048
9.142
0.266
0.237
9.440
8.866
8.902
N/A
Avergge GPEOOe Sore
OmniCien
GPT:OEE: Sinore
OmniiGnn
VAREdit-8B BB *
AnySD
ICEdit
VAREdit-8B
UltroEdit
e KTrit
VAREdit-28
8
AnysD
UltroEdit
EdiAR
Avergge
EdRAR
0
InstructPuclPss
insrrccFii2Px
ACE
800
1600
2100
Number of Successful dditngg Samples
Number of Successful E Editing Samples PIE-Bench
EUU-Edit
Figure 4: Scatter plots of successful editing samples capacity versus the GPT-Over. scores over the successful editing subsets of basic approaches on EMU-Edit and WiE-Bench benchmarks.
Notably, GPT-Bal. serves as crucial metric since it penalizes common failure mode: models like OmniGen can achieve high GPT-Over. scores simply by being too conservative and failing to perform the requested edit. To distinguish true preservation from mere inaction, we conduct more rigorous analysis. We isolate only the most successful edits (GPT-Suc. 2 9) and re-evaluate GPT- Over. scores the subsets for all basic approaches. Figure 4 presents the comparisons with two observations. First, VAREdit produces a significantly larger volume of high-quality edits, demon- strating the superior instruction-followe the highest GPT-Over. score within this successful subset, surpassing even OmniGen. This demon- strates VAREdit's ability preserve unchanged regions.
capability. Second, and more critically, VÁREdit achieves
Robustness Across Categories. We present the radar charts in Figure 5 that break down perfor- mance by editing types in EMU-Edit and PIE-Bench. VAREdit achieves state-of-the-art perfor- mance across the vast majority of categories among all basic approaches. While VAREdit-2B shows some limitations in challenging global style and text editing tasks, VAREdit-8B substantially closes this performance gap and beats all competitors. This illustrates the excellent scaling properties of our framework, suggesting that performance can be further enhanced by scaling to larger models and datasets. Detailed numerical results presented in Appendix A.8
We also present the fine-grained results ImgEdit- involving all categories in the basic-edit suite and
(UGE) suites. According to the results in Table 2 GPT- 4o- Image performs perfectly across almost all categories. Among open-source models, VAREdit- 8B performs the best in background change, style transfer, removal, replacement, addition, motion change, hybrid edit and fine- aee-grained UGE. VAREdit achieves satisfactory performance close to or better than closed-source Imageo-Image, demonstrating its superiority across categories.
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VAREdINB =
VAREdR-25
IestracPacPP
Ultrabdit
Omuicenn
Amm50
EdIAR
-AG++
JCEdR
Ovenal
Local
Global
Locd
Global
Style
Bardom
Style
Bandom
Background
Modiy Backgrund
Modity
Bernv
Adi
Remova
Adl
Style
Toot
Style
Coler
Rezove
Coler
Recsvv
Color
Badagrunnd
Color
Sadggrundd
Post
Atmbere
Post
Attibbte
(3) EMU-Edit
PIE-Bench
Figure 5: Fine-gre tine-grained categorical GPT-Suc. and GPT-Bal. scores of VAREdit and basic approaches on EMU-Edit and PIE-Bench benchmarks.
Table Quantitative results of VAREdit and representative approaches on IngEdit-Bence
Method
Background
Extract
Attribute
Style
Remove
Replace
Add
Motion
Hybrid
UGE
UltrEdit
3.31
2.02
3.01
3.69
1.71
3.13
3.63
3.57
2.33
2.36
SteplX-Edit
3.19
1.87
3.13
4.44
2.61
3.45
3.90
3.43
2.52
3.11
GPma-4--mmaee
2.96
4.26
4.54
VAREdi--BB
2.95
1.94
3.33
4.05
3.51
3.97
3,57
2.88
2.57
2.81
VAREdit-88
3.93
1.52
3.98
4.60
4.17
4.54
4.06
4.31
3.30
4.09
Inference Efficiency. Table also demonstrates that VAREdit offers substantial efficiency im- provements. VAREdit-8B completes a 512 x 512 edit within 1.2 seconds, making it 2.2x faster than the similarly-sized UltraEdit (7.7B, 2.6s) and 7x faster than the larger ICEdit model (17.0B, 8.4s). This efficiency stems from the single-pass, multi-scale generative process. Moreover, VAREdit-2B achieves 0.7s inference time while surpassing all baselines in editing quality. While frontier editing approaches achieve impressive results, they incur substantial computational burden, with inference times increasing by over order of magnitude (more than 10x) on local hardware. This trade-off suggests that significant performance gains are attainable for VAREdit by strategically scaling its training dataset and model size, all while maintaining a moderate inference overhead.
4.3 QUALITATIVE RESULTS
Figure provides visual comparison that reveals the underlying reasons for VAREdit's quantitative success. In the first example, diffusion- based methods tend to overedit the image and achieve lower GPT-Over. scores. For instance, InstructPix2Pix alters the color of the entire ground and ICEdit erroneously removes the pole. The vanilla AR-based EditAR fails to execute the instruction at all, with a high GPT-Over. score yet very low GPT-Suc. score. VAREdit successfully completes the task while precisely preserving the unchanged regions, thus achieving the highest GPT-Bal. score. Similar observations hold for the subsequent examples, confirming the effectiveness VAREdit. To access further qualitative results and analysis, please refer to Appendix A.5
4.4 ABLATIONS AND ANALYSIS
To isolate the contribution of the SAR module, we con- duct an ablation study com- paring the three condition- ing strategies for the VAR Transformer: (1) Full: Con- ditions on features from source scales, (2) Finest: Conditions on the finest-scale source features, and (3) SAR: Our proposed SAR-enhanced conditions (on the finest- scale source features).
Table 3: Ablation quantitative results of VAREdit-2B in full-scale, finest-scale and SAR enhenneddeed settings.
GPT Score
Input Length
Dataset
Setting
Time
Suc. f
Over.
Bal. f
Full
5.775
5.892
4,967
1.042
0.8s
EMU-Edit
Finest
5.819
6.480
5.248
w/ SAR
6.072
7.058
5.565
777
0.5s
Full
7.184
7.170
6.423
1,042
0.8s
PIE-Bench
Finest
7.291
7.501
6,588
777
0.5s
w/ SAR
7.234
7,804
6.684
777
0.5s

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Published as a conference paper at ICLR 2026
Input
VAREdit-8.4B InstructPa2Pix
Ultraddit
OmniGen
AnySD
EdIARR
ICEdit
Remove clock
the
pola
GPT
Ball 10/ 10/30
8/7/7.47
0//0/0
10/9/947
8/9/8.77
0/20/0
0/9/0
10/ 9/947
stroight instead
GPT Score Suc. / OOver. Ball) 10/30/30
9/9/9
10/9/947
0/20/0
0/0/0
10/9/947
0/0/0
0/20/0
GPT Score
(Suc. Overr Ball) 9//47 10/5/6.7
3/8/4.3
8/7/7.47
5/8/
5/3/3.75
0/9/0
10/9/947
Figure 6: Qualitative results among the compared methods and VAREdit of three editing samples in EMU-Edit and PIE-Bench datasets. GPT scores are also reported to evaluate the editing quality.
The results in Table 3 and Fig- confiimm our hypothesis on 256 x 256 resolution VAREdit- 2B. The Full setting yields the lowest GPT-Bal. score, which can be attributed to signifi- cantly lower GPT-Over. scores. Introducing all source scales the conditioning creates dis- tractions for predicting target features and leads over- editing. Moreover, this setting 60% slower than the other two variants due to longer to- ken sequences. contrast, the Finest setting requires fewer to- kens as input than the Full set- ting, yet suffers from scale mismatch analyzed in Section 3.222 and results in poor performance. Compared with the Finest setting, the SAR-enhanced variant achieves higher GPT-Over. scores, demonstrating the effectiveness of scale-matched information injection. According the visual sults, our SAR-based variant further reduces unintended textual detail changes and incomplete style references, achieving more precise editing results.
Input
Full
Finest
W SAR
Change the color the roses from red to purple
GPT Score (Suc./ Over. / Bal.) 10/9/9.47
10/ 90/9/00 9.47
10 10/ 10
Change stained gloss window
GPT Score (Suc./ Over. Bal.) 9/ 8.47
9/9/9
10/10/10
Figure 7: Ablation qualitative results of VAREdit in full-scale, finest-scale and SAR-enhanced settings.
CONCLUSION
We have proposed VAREdit, an instruction-guidee image editing framework following the next- scale prediction paradigm. VAREdit takes the instruction and quantized visual token features into VAR Transformer model to predict multi-scale residuals of the desired target image. We have analyzed the efficacy of different conditioning strategies and proposed novel SAR module to ef- fectively inject seale-matched conditions into the first self-attention layer. Extensive experiments have demonstrated that VAREdit achieves significantly higher editing precision scores and faster generation speed compared to state-of- the-art methods. We hope this initial exploratory study pro- vides valuable insights into the future design of more effective and efficient AR- AR-based visual models.
Acknowledgement. This work was supported by the Key Science & Technology Project of An- hui Province No. 202523000050002, the National Natural Science Foundation of China (No. 62337001), the Key Technologies & D Program of Anhui Province (No. 202423k09020039) and the Fundamental Research Funds for the Central Universities.
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A APPENDIX
A.1 TRAINING DATASET PROCESSING DETAILS
Our training dataset for VAREdit-2B is constructed from the SEEDD-Data-Bdi (Ge et al. 2024) and ImgEdit (Ye et al. ) datasets, yielding a final collection of 3.92 million paired samples. We first extract all single-turn samples of the two datasets and decompose multi-turn conversations in SEED-Data-Edit into single-turn editing pairs for the collection. We identify significant number of noisy samples within the initial SEED-Data-Edirs characterized by poor instruction alignment, visual blurring or abnormal distortion. To address this, we filter these low-quality samples using qualified open-source visual language model (VLM), Kimi-VL-A3BB-hhinknng (Team et al., 2025). The filtering prompt is shown in Figure 8 We discard approximately million samples for which the VLM provided a negative judgment, resulting our final refined dataset. For VAREdit-8B, the training dataset includes large-scale proprietary editing samples.
Please check whether the second image is edited based on the instruction 'INSTRUCTION>> over the first image, in good quality. Specifically:
1. Summarize the difference of the second image over the first image, then compare the difference and the given instruction. Do they match precisely without additional elements?
2. Are the two images are both clear without blurrigg and abnormal distortion?
If the above aspects are both satisfied, generate "yes", otherwise generate "no", and no other content.
Figure 8: Prompt for filtering SEED-Edit-Data samples.
A.2 ADDITIONAL DETAILS OF EVALUATION METRICS
A.2.1 CLIP-BASED EVALUATION
For conventional CLIP-based metrics, we adhere to the established configurations of previous ap- proaches. On the EMU-Edit (Sheynin et al. 2024) benchmark, we follow UltraEdit (Zhao al. as the base model. On the PIE-Bench (Ju al., 2023) benchmark, we use /ttl/clppppe which is explicitly specified in the benchmark.
2024a) to use
as the base model,
A.2.2 GPT-BASED EVALUATION
Conventional metries often fail to capture important editing quality aspects, such as spurious mod- ifications incomplete edits. To address this, adopt the evaluation protocol from OmniEdit (Wei et al. 2025) to use GPT-4o (Hurst et (024) as an automated judge to provide GPT-Success (Suc.) and GPT-Overedit (Over.) scores. The evaluation prompt is shown in Figure 9 We also in- troduce GPT-Balance (Bal.) as a balanced evaluation metric for overall editing performance, which stands for the harmonic mean of GPT-Suc. and GPT-Over, metrics. The GPT-Bal. score of an editing sample is calculated as: (PT-BBa.(e) = EXGPI-SSu.(ee) (.F..ee)XGPI-Ove.... and it takes zero (e) value if either the edit is absolutely unsuccessful (GPT-Suc..e curs (GPT-Over.(ee = 0). The final reported score for each GPT-based metric on a dataset is the arithmetic mean of the scores across all samples.
0) or severe overediting oc-
A.2.3 ADDITIONAL EVALUATION DETAILS
For ImgFdit-Benche we follow the official protocol that uses GPT-4o to assign 1-5 ratings, averaged across the dimensions on instruction adherence, image-editing quality, and detail preservation. For GEdit-Bench, we also the official protocol that utilizes GPT-4. to assign 0 10 ratings seman- tie consistency (SC), perceptual quality (PQ) and finally calculate the overall score (O) accordingly.
A.3 DETAILS FOR COMPARED APPROACHES
We assess VAREdit against several basic tuning- methods:
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Human:
You are a professional digital artist. You will have to evaluate the effectiveness of the A 1-generrated image(s) based on the given rules. You will have to give your output in this way (K your reasoning concise and short.):{( ("score": [...]] "reasoning" "..."] and don't output any anything else. Two images will be provided:
The first being the original A Al-generated image and the second being an edited version of the first. The objective is evaluate how sucoessfully the editing instruction has been executed the second image.
Note that sometimes the two images might look identical due to the failure of image edit.
From scale to 10:
A score from 0 to 10 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.
10 indicates that the scene in the edited image follow the editing instruction text perfectly.
the object in the instruction is not present in the original image at all, the score will be 0.
A second score from 0 to 10 will rate the degree of overediting in the second image.
0 indicates that the scene the edited image is completely different from the original.
10 indicates that the edited image can be recognized as minimal edited yet effective version of original. Put the score in list such that output score [soore] scoree], where 'scorel' evaluates the editing success and' and'scome2' evaluates the degree overediting
Editing instruction: <EDITING INSTRUCTION>
<Image> Source Image </Image>
<Image> Edited Image </Image>
A ssistant:
Figure 9: Prompt for evaluating the GPT-Suc. and GPT-Over, scores of the edits.
InstructPix2Pix (Brooks et al., 2023) is the seminal study that introduces the natural, hu- manized instruction-guided image editing task. It constructs considerable paired editing samples train an end-to-end editing model based on SD v1.5, which establishes a power- ful channel-wise conditional concatenation paradigm in instruction-guiiee image editing.
UltraEdit (Zhao et al. 2024a) introduces large-scale dataset covering broader instruction types, real images and region editing. Canonical diffusion models trained on the UltraEdit set new benchmarking records. We adopt the published SD v3 variant for comparison.
OmniGen (Xiao al. 2025) is diffusion-based architecture that tokenizes texts and images into token embeddings and denoises the noise tokens to reconstruct latent represen- tations through Transformer-bast based rectified flow optimization. supports unified image generation, including image editing, subject-driven and condutionn generation.
AnySD (Yu et al. 2025) is another diffusion-basee architecture integrating a task-aware routing strategy with mixture-of-expert (MoE) blocks to meet task-specific editing quirements. This model trained based on SD v1.5 with newly proposed comprehensive image editing dataset, AnyEdit.
EditAR Mu et al.) 2025) is an AR-based architecture including -Q-autoeneoder and a joken-by-token autoregressive model to predict target image tokens in latent space. To inject general visual knowledge, EditAR introduces a distillation loss from a vision foun- dation model as a regularization term, thereby improving the text-to-image alignment.
ACE++ MMo al. 2025) is instruction- based diffusion framework, which adapts the
long-contexs condition unit module to scenarios of multiple conditions. This model first experiences zero-reference training stage and then N-reference training stage to enable support for general instructions across various tasks.
ICEdit (Zhang et al. 2025b) leverages large-scale Diffusion Transformers to construct an in-context editing framework. It integrates a L-RA-MoE hybrid tuning strategy to enhance flexible adaptation for specialized condition feature processing, while a VLM-based early filtering module is utilized to identify good samples at the very first denoising steps.
In addition, we also include four frontier image editing approaches:
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Step1X-Edit (Liu et al., 2025) is unified image editing model that combines the robust semantic reasoming of MLLM with a DiT-style diffusion architecture. maintains a good
balance between conditioned image reconstruction and editing instruction following.
2025 is generative flow matching approach that handles FLUX.1 Kontext (Labs et al.
both local editing and generative in-context tasks within a DiT-style architecture.
Qwen-Image- (Wu al. 2025a) is built upon Qwen-Image, simultaneously feeding the input image into Qwen2.5- VL and the VAE Encoder to obtain the representation, serv- ing as conditions of double-stream MMDiT architecture for effective editing.
GPT-44-Image (Hurst et al. 2024) is closed-sourced visual generation model that sup- ports generating highly realistic images that accurately follow instructions through simple text descriptions image uploads.
All the compared approaches (except for GPT-4o-Image) are implemented using the officially re- leased open-source codes with their published default parameter settings.
A.4 ADDITIONAL IMPLEMENTATION DETAILS
Our VAREdit models are built on foundational VAR-based model, Infinity (Han et al. 2025, We initialize our models with the released pre-trained 2B and 8B checkpoints for full fine-tuning. For visual tokenization, quantization (Zhao et al. 2024b), a lookup-free vector quantization method following:
the pre-trained multi-scale tokenizers integrating binary spherical
@BSQ (z) =
((x))
Va [s.200]
(8)
We the codebook dimensions the training stages of 256 x 256 and 512 x 512 resolution, all the source and target images will be tokenized into scales and 10 scales with the following lists of grid sizes (h,, wx), respectively:
32 and 56 for the 2B and 8B models, respectively. During
* 256px: (1, (2. 2), (4, (), (6. 6). (8, 8). (12, 12), (16, 16).
512px: (1, (2, 2), (4, 4), (6, (8, 8), (12, 12), (16, 16), (20, 20). (24, 24), (32, 32).
In particular, we only extract the finest-scale tokens of the source images in the finest-scale condi- tioning strategy. We employ 2D-RoPE (Heo et al., 2024) to encode the spatial information of all input tokens before each self-attention module. For a target token position (i.j) in scale k; its 2D-RoPE coordinate (æ, y) is calculated as:
(a, v) = (i [h/bll.j [w/ww]),
while the source token is treated as spatially separate one with an offset A = (64, 64), indicating that 2D-RoPE coordinate starts from (64, 64):
(x, y) = (i [h/ha] + 64, [w/w]] + 64).
(10)
Additionally, we also use the pre-trained scale positional embeddings the Infinity model to miti- gate inter-scale confusion.
A.5
QUALITATIVE STUDY AND ANALYSIS
A.5.1 COMMON-TYPE IMAGE EDITING
To further present the superiority of VAREdit, we conduct additional qualitative analysis across diverse editing scenarios, including object-level modifications (addition, replacement, removal), tribute changes (material, text, posture, style, color) and complex compositional edits. As illustrated in Figure IO, VAREdit-8B exhibits satisfactory editing adherence instructions and high image fidelity on these samples, which serve as commonly seen editing types.
A.5.2 IMAGINATIVE IMAGE EDITING
As VAREdit exhibits promising performance in common-type editing, we further explore some sophisticated editing types, including some unseen imaginative instructions. We have presented
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Published as a conference paper at ICLR 2026
(GO)
Add strawberries
Change light burning
girl hand
Change the texture of rose into plastic
Change the apple into laptop
Make the two birds face To face
Cheers!
Change chess player robot for the mirrar only
Change apple into red curve Cheers' on it
Replace bullets shimmering, multi- colto-cooreed butterflies
Switch the girl's clath into school uniform shirt with black bow tie
man smile change letter 'A' into white stor
tropical fish sea and change the scone into cartoon style
Figure 10: Qualitative editing samples of VAREdit-88 SB across numerous common editing types.
Turn this house into
'steampunk' style
Change the waterfall into flowing ice
Make the arange designed architect Zaha Hadid
Visualize singing sound as colorful weves
Add cincuit style Gogh 'ss 'Starry Night' on the notebook page
Add shapes king real queen dancing
Figure 11: Qualitative editing samples of VAREdit-8B in imaginative image editing.
the results in Figure [I Our model achieves relatively successful results these challenging edits of artistic style transfer (cyberpunk, Zaha's design style, and Vango's s starry night) and semantic conceptual synthesis (ice flowing waterfall, artistic sound waves, and chess shadows). These results strongly suggest that our model is not merely memorizing training examples but has developed generalizable editing capabilities.
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Published as a conference paper at ICLR 2026
What hoppen has washed for years?
What happen if the coffee for a month?
What happen the tempercture under 20 degrees fohrerheit?
Figure 12: Qualitative editing samples of VAREdit-8B in reasoning cassoning-based image editing.
Original
Step1
Step
Step3
Add of decorative garnishes the plate
yellow
bread
Original
Step 1
Step 2
Step 3
Change material of the deor to iron
Turn the house black
Delete the house window
Original
Step 1
Step 2
Step3
Change:
Change clething color green
clothes to 'Hello"
hat
MIAMI
Hello
Hello
Hello
Figure 13: Qualitative samples of VAREdit-8B in multi-turn image editing.
A.5.3 DEASONING-BASED IMAGE EDITING
To evaluate the capability of VAREdit in reasoning- based image editing He et al. 2025
present editing results of three representative image samples ImgEdit- -Bnch with implicit hypothetical instructions that demand reasoning. As illustrated in Figure 12] by presenting the instruction with implicit "dirty car", "evaporation" and "cold weather". VAREdit-SB catches these points and in- cludes the semanties in the edited samples. While some details can be further improved ("blank cup". "icy lake"), more balanced dataset curation will better address this gap.
A.5.4 MULTI-TURN IMAGE EDITING
more challenging scenario is multi-ture image editing, which demands the model preserve criti- cal details in multiple editing steps. To address this, we present editing results of three representative image samples in ImgEdit-Bench each with three continual editing instructions. As illustrated in VAREdit-8B performs well and maintains the overall quality after three editing steps.
Figure 13
A.5.5 HIGHLY-UUCEETTAN IMAGE EDITING
In some scenarios, users may present unclear instructions with ambiguity or even conflicts. To in- vestigate this, conducted qualitative study several representative samples. As shown in Figure [4] unclear concepts of "look better" and "add some style" trigger no editing effect, while the underspecified "make the sky louder" is executed by removing the cloud unexpectedly. For con-
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Published as a conference paper at ICLR 2026
Original
Edit.
Fdit
Original
Fdit:
Falit
some mushroom over the pizz0
sky louder
ships them prime number
and some style
Original
Edit 1
Edit
Original
Edit
Exttt 2
Make the car red blue
Make the can blue and red
Make the man happy and sod
Make the man's hair straight
Figure 14: Additional editing samples of VAREdit-BB highly-uncertain image editing.
Original
VAREdIt22
VAREd1--8
Original
VAREd1-2B
VAREdI--8F
skate
Megp 22
Add the word 'skate' to the bottom of the image
Change the woman from calm to laughing
Original
VAREdİL-2B
VARE&I-
Original
VAREdiL-2B
VAREdiL-E
Make this market selling hand-crafted pottery
Make the
Figure 15: Failure editing case results of VAREdit-2B and VAREdit-SB.
flicting instructions, VAREdit exhibits different patterns. By switching "red" and "blue", VAREdit tends to ignore "blue" rather than considering both, illustrating biased focus in some elements. In contrast, the "happy-and-sad" and "curve-and-straigh"" have been executed at somewhat balanced level rather than ignoring of them. In summary, VAREdit demonstrates degree of robustness in the face less-than-ideal instructions, and its behavior patterns (converging to priors, ignoring noise, or compromising in conflict) predictable.
A.5.6 FAILURE CASE ANALYSIS
To comprehensively explore the limitations of VAREdit, we conduct failure case analysis on both 2B and 8B sizes and present some representative editing results. As illustrated in Figure [5] our investigation reveals that while VAREdit-2B struggles with specific categories (e.g. text generation, motion change), VAREdit-8B largely mitigates these issues through scaling. However, even the larger model encounters difficulties in complex compositional tasks that require global modification and preservation of multiple distinct local elements. For example, VAREdit-SB modifies the market into pottery successfully, yet the shapes some pottery remain abnormal. Moreover, the cross-arm edit poses difficulty to the 8B model by maintaining the general hand fingers. We attribute this limitation to the current density of processing scales and hypothesize that expanding both the image resolution and training data will resolve these fine- grained failures.
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Published as a conference paper at ICLR 2026
A.5.7 USER CROSS- VALIDATE STUDY
While we use CLIP and GPT-based scores as the main evaluation metrics, it is unknown how well these values reflect the user preferences. To mitigate this gap, we conducted a blind, randomized human preference study on the PIE-Bench dataset. For each sample in participants with the source image, the text instruction, and the three edited images generated by our model (VAREdit-8B) and two strong baselines (Step1 X-Eiit, FLUX.1 Kontext (dev)). The three generated images were displayed in randomized order avoid positional bias. Participants were asked to select the single best image that satisfies two criteria: (1) Instruction following: how accurately the image reflects the textual instruction, and (2) Image quality: the realism of the edit and the absence of artifacts or over-editing. We collected responses from 5 participants (e.g., graduate students in our field with experience in evaluating generative models). The consistency in the results is expected to provide strong and valuable signal regarding user preference.
we presented
We report both the average number of wins and the overall preference rate for each model. As illustrated in Table 4 the three methods received a globally balanced number of wins, while VAREdit-8B achieved the high- est preference rate (36.0%). This study confirms that VAREdit's quantitative strengths translate directly into superior perceptual quality for users.
Table 4: Cross-validate user study results on PIE-Bench. The best group results marked in bold.
Method
Avg. Count
Avg. Rate (%)
GPT-Bal.
StepIX-Edit
210.4
30.1
7,351
FLUX.1 Kontext (dev)
237.4
33,9
6.998
VAREdit-8B (Ours)
252.2
36,0
8.106
A.6 QUANTITATIVE RESULTS ON GEDIT-BENCH
We present quantitative results on GEdit-Bench, reporting the SC score, PQ score and the overall score eval- uated by GPT-4.1 on VAREdit and some representative approaches. As illustrated in Table [5] GPT-4o-Image scores the highest, while Step1 X-Bdit and VAREdit-8B are the leading open- source methods. While the absence of training data with Chinese instructions may influence the editing performance of VAREdit, an improved scaling and balance of the training hours and data volume can mitigate this gap.
Table 5: Quantitative results of VAREdit and representa- tive approaches on GEdit BedithBenchheee (English).
Intersection
Method
G.SC
G_PQ
G.O
G.SC
G.PQ
G.O
InstruciPix2Pix
3.335
6,210
3.234
3.296
6.189
3.219
AnySD
3.122
5.865
2.919
3.053
5.882
2.854
OmniGen
6.037
5.856
5.154
5.879
5,871
5.005
Step1
7.289
6.962
6.618
7.131
6.998
6.444
GPT--o-Imuge
7.867
8.097
7.590
7.743
8.133
7.494
VAREdit-2B
5.121
5.301
4,460
4,992
5,220
4.334
VAREdit-8B
7.292
6.581
6.383
7.092
6.626
6.236
A.7 SELF-ATTENTION HEATMAPS ANALYSIS
We provide the full-layer self-attention heatmaps in Figure 16 as a more comprehensive scale de- pendency analysis. It can be seen that in the first attention layer, the attention scores distribute broadly on almost all source scales, especially the coarser source scales. While in other layers, the attention patterns become highly localized. Specifically, attention scores between source scales and target scales are in strong diagonal structures in deeper Transformer layers, suggesting that atten- tion is primarily confined tokens in the spatial neighborhood. In this task, the finest accumulated
source scale F(ere) is sufficient to provide reference information for high-quality image editing. Such observations inspire us design the SAR module to inject scale-matched conditions into the first self-attention layer, while maintaining the finest-scale conditional setting in deeper layers.
To investigate the attention pattern of the SAR variant, visualize self-attention heatmaps from some intermediate layers. As illustrated in Figure 17 the first layer exhibits a clear scale-aligned
(39)) behavior. The attention scores (Q) K fref) /vd)) assigned by each target scale k to the corre- sponding reference features are predominantly concentrated, without focus on the finest-scale source features. In subsequent layers, the attention pattern becomes highly localized as the same in the full-
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Published as a conference paper at ICLR 2026
Layazz
Layer
Lover
16
Loyer
1pper
Layer
Layer 12
Liber
Layer
Layer 15
Liver
Liven 27
1e
Lene 15
Lapo 28
Lover
Lose
Lase 23
Live
Lover
Laye:11
32
Figure 16: Self-attention heatmaps based on the full-scale setting among all transformer layers.
x10
Layer
Layer
Layer
'3T
15T
20
Layer
2.
127
1pe
7F
121
Figure 17: Self-attention heatmaps of PIE-Bench samples based on the SAR-variane across different layers. Note that in the first layer, the focused zone is organized not strictly aligned with the corre- sponding positions, since (ref) has spatial size of (hg. wi.) instead the largest scale (hx wx)
scale setting. This behavior mirrors the attention patterns observed in the standard full-scale setting, suggesting a shift from global scale-matching to local refinement.
A.8 NUMERICAL RESULTS FOR CATEGORICAL EVALUATION
Table 7 g present the detailed numerical results of the fine- fine-grained categorical scores shown in the radar charts of the main context. It can be observed that VAREdit-2B outperforms the compared approaches across the vast majority of editing types, such as addition, removal, coloring and mate- rial transfer. VAREdit-8B enjoys enhanced performance by substantial margin that outperforms all competitors. According to these tables, the 2B model shows some limitations in challenging scenarios, including global editing and text editing. Through scaling up the model size and training iterations, the 8B model substantially mitigates these gaps, especially text editing that achieves 150%+ improvements not only effective but also highly scalable. As a result, our VAREdit models exhibit high robustness across various editing types to achieve precise editing results.
the 2B variant. These findings strongly demonstrate that our VAREdit is
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Published as a conference paper at ICLR 2026
Table Numerical fine-grained categorical GPT-Sue. scores all methods on EMU-Edit. Among column names, "Back." stands for "Background". The best group results are marked in bold, while the second-best group results are underlined.
Method
Overall
Global
Add
Text
Back.
Color
Style
Remove
Local
InstructPix2PIx
3.358
3,950
4.034
0.330
4.043
5.270
4.634
1.637
3.897
UltraEdit
4,881
6.178
6.084
1.985
5.625
5.728
6.389
2.328
5.753
OnniGen
4.738
2.703
4.274
4.268
2.509
6.324
5,558
6,516
3.960
AnySD
3,098
1.379
3.752
1.899
5.501
4.289
0.744
3,684
2.789
EditAR
3,582
4.717
3.797
0.472
3.488
5.678
6.571
1,501
3.697
ACE+
2.375
3.717
2.694
2.288
2.871
2.832
0.535
1.650
ICEdit
5.027
3,548
5,931
4.751
2.635
6.935
5.138
4.793
4,955
VAREdI-2BB
6,210
5,475
6.865
2.708
6.724
7,854
6.740
6,889
6,314
VAREdit-BB
8,284
7.827
8.857
7.375
8,150
9.285
7.641
8,657
8,040
Table 7: Numerical fine lie-grained categorical GPT-Bal. scores of all methods on EMU-Edit. Among column names, "Back." stands for "Background". The best group results are marked bold, while the second-best group results are underlined.
Method
Overall
Global
Add
Text
Back.
Color
Style
Remove
Local
InstruciPix2Pix
2.923
3.934
3.477
0.326
2.607
4.740
4.514
1.469
3.204
UltraEdit
4.541
6.020
5.695
2.071
5.256
5.221
6.346
2.159
5.079
OmniGen
4,666
2.877
4.293
4.366
2.390
6.036
5.539
6,206
3,979
AnySD
3.129
1.630
3.893
2.124
5.072
4.276
1.012
3.497
2.818
EdiíAR
3,305
4.753
3.622
0.472
3.321
4,392
6.648
1,381
3.364
ACE+
2.076
3.689
2.070
2.021
1.660
2.422
3,899
0,510
1.392
ICEdit
4.785
3.603
5.673
4.732
2.651
6.301
4,850
4.427
4.756
VAREdit-21
5.662
4.279
6.775
2.887
4.882
7,303
5.661
6.716
5.821
VAREdit-BB
7.892
7.473
8,460
7.375
6.573
8,870
7.788
8.274
7.652
Table 8: Numerical fine- fininnnained categorical GPT-Suc. scores of all methods PIE-Bench. Among column names, "Attr., "Mater.", "Back." stands for "Attribute". "Material", "Background" respec- tively. The best group results marked in bold, while the second-best group results underlined.
Method
Overall
Random
Modify
Add
Remove
Attr.
Post
Color
Mater.
Back.
Style
InstruciPix2Pix
4.794
4.707
5.913
5.163
2.313
3.425
2.950
7.075
6.175
5.275
5,238
UltraEdit
5.831
6.336
6.588
7,200
2.275
5,200
3,300
6.000
6.750
6,200
7.050
OmniGen
3.459
3,921
3.563
3.138
2.938
2.825
2.625
5.325
3.050
3,200
3.650
AnySD
3.456
4.114
5.438
4.388
2.988
1.725
1.850
3.525
3.725
3.113
1.700
EdítAR
5.070
5,836
5.263
4.000
0.900
4.300
3.225
7.600
6.525
6,363
6,800
ACE++
2.743
2,514
0.888
3.875
0.150
1.650
1.400
4.750
3.600
4,225
4,763
ICEdit
5.321
6.613
2.650
8.150
4,325
5.988
REdii--BB
7,530
7.743
8.725
7.463
7,300
6.225
4,450
8.425
7.375
7,950
7,663
VAREdit-SB
8.621
8.643
8.775
8.975
9.163
6.200
6.875
9.325
8.425
9.513
8,475
Table 9: Numerical fine-grained categorical GPT-Bal. scores of all methods on PIE-Bench. Among column names, "Attr." "Mater." "Back." stands for "Attribute". "Material", "Background", respec- tively. The best group results are marked in bold, while the second-best group results are underlined.
Method
Overall
Random
Modity
Add
Remove
Attr.
Post
Color
Mater.
Back.
Style
InstructPix2Pix
4.034
4,251
4.328
4.711
1,861
2.767
2.016
6.068
5.147
4.174
4.781
Ultradit
5.580
6.056
6.369
6.775
2.136
5.066
3.256
5.898
6.394
5.807
6,830
OmniGen
3.498
4.034
3,507
3.289
2.718
2.708
2.788
5.245
3,170
3.193
3,888
AnySD
3.326
4.022
4,923
4.183
2.703
1.814
1.805
3.695
3.752
2.892
1.832
EditAR
4.707
5,312
4.540
3,966
0.908
4.279
3.193
6.527
5.902
5.725
6.800
ACE+
2.574
2.458
0.853
3.192
0.151
1.586
1.341
4.067
3.047
3.972
5.032
ICEdit
4.933
5.539
4,961
5.756
4.056
3,289
2.645
7.866
4.750
3.814
5.611
6.99
7,330
2.761
7.112
6.942
S.717
4.291
8.032
6.502
6.950
7.348
VAREdiL-BB
8.105
8,093
7,940
8.392
8.831
5.936
6.453
8.919
7.755
8.779
8.287
22


Reviews:
[{"review_id": "VO5XESkASK", "reviewer": "Reviewer_Hejp", "summary": "This paper introduces VAREdit, the first training-based Visual Autoregressive (VAR) model for instruction-guided image editing. The key contribution is reframing image editing as a next-scale prediction problem, where the model autoregressively generates multi-scale target features conditioned on source image features and textual instructions. The authors identify a critical scale-mismatch challenge when conditioning on finest-scale source features alone: high-frequency details disrupt the prediction of coarse-grained target structures. To address this, they propose the Scale-Aligned Reference (SAR) module, which dynamically generates and injects scale-matched source information specifically into the first self-attention layer, while deeper layers continue to use finest-scale conditioning. Through systematic analysis of self-attention patterns, the authors demonstrate that this design aligns with the model's functional differentiation across layers—where the first layer establishes global structure and deeper layers perform local refinement. Extensive experiments on EMU-Edit and PIE-Bench benchmarks show that VAREdit achieves substantial improvements over state-of-the-art diffusion-based methods", "questions": "None", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper explores how to apply VAR architecture to image editing, moving away from dominant diffusion approaches. The SOTA results on balanced metrics and 2.2x speedup are significant, suggesting AR models are a highly promising direction for image editing.\n2. The core contribution is the deep analysis of the scale mismatch problem in VAR conditioning. The attention map analysis (Figure 3) is strong evidence. The resulting SAR module is an elegant and efficient solution that precisely targets the identified bottleneck (the first layer) without incurring extra inference cost.\n3. The paper rightly identifies flaws in standard metrics. Relying on GPT-Balance and especially the analysis in Figure 4 (evaluating preservation only on successful edits) provides a much more rigorous and convincing evaluation than prior work.", "weaknesses": "1. The paper's primary metrics (GPT-Suc., GPT-Over., GPT-Bal.) rely on GPT-4o as a judge. While arguably better than CLIP, this is costly, slow, and dependent on a proprietary API, making the evaluation results difficult and expensive to reproduce. The authors should provide an alternative using an open-source VLM (such as Qwen3-VL) in the rebuttal.\n2. The authors used CLIP and GPT scores as evaluation metrics, but lacked test results on benchmarks commonly used in the editing field, such as ImgEdit and GEdit-Bench. Considering the authors' statement that CLIP scores do not accurately reflect editing performance, and the overhead and uncertainty of GPT scores, using ImgEdit and GEdit-Bench can provide reproducible and stable comparative results.\n3. The training data was filtered using an external VLM (Kimi-VL), which discarded ~1M samples. This is a significant, non-reproducible step, and the final model quality may be heavily dependent on this external filter.", "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": "d8VUn0VkVv", "reviewer": "Reviewer_qmPC", "summary": "This paper introduces VAREdit, an instruction-guided image editing framework that leverages a visual autoregressive modeling paradigm. By formulating editing as a next-scale prediction problem, VAREdit conditions on both source images and textual instructions to autoregressively generate multi-scale tokenized image features. The paper identifies a scale-mismatch issue in using finest-scale-only conditioning and proposes the Scale-Aligned Reference module, which bridges this gap by injecting scale-matched information only in the Transformer’s first self-attention layer. Experiments on EMU-Edit and PIE-Bench benchmarks demonstrate that VAREdit outperforms several competitive diffusion and AR-based baselines in both instruction adherence (as measured by GPT and CLIP scores) and inference speed. Additional ablations and qualitative studies illuminate the advantages of the SAR module.", "questions": "1. How does VAREdit perform in genuinely few-shot/in-context adaptation settings? Can the model apply novel editing instructions (or new types of edits) without SGD-based fine-tuning, or does it require full retraining? Comparative numbers or case studies against recent AR-based in-context editing models would be instructive.\n2. What are the observed failure modes for VAREdit, especially for large/semantic/global edits or high-text-content regions? Can the authors provide a quantitative or qualitative assessment of these, and whether they are due to model scale, dataset limitations, or issues intrinsic to the SAR module?\n3. Details on the SAR module’s construction: Are the downsampling mappings for scale-aligned features jointly learned, fixed, or derived from the tokenizer? How does SAR interact with residual connection and positional encoding in very deep Transformers?\n4. How robust is VAREdit to highly ambiguous, underspecified, or conflicting instructions? Does it have mechanisms (e.g., uncertainty estimation, output confidence) to flag instructions where adherence is questionable?\n5. Is there any plan for releasing the filtered and processed dataset used in training, and can more precise statistics be included in the public release for reproducibility?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper analyzes and highlights the limitations of both full-scale and finest-scale-only image conditioning for autoregressive editing tasks, a nuanced challenge not prominently addressed in prior work.\n2. The proposed Scale-Aligned Reference module is conceptually well-motivated, solving the scale-mismatch issue in a resource-efficient way by using scale-aligned source features solely in the first attention layer. The supporting evidence includes explicit self-attention heatmaps and precise mathematical descriptions for how SAR modifies qkv calculation.\n3. VAREdit sets a new state-of-the-art on the EMU-Edit and PIE-Bench datasets across a variety of metrics. Table 1 illustrates consistent improvements in both instruction adherence (GPT-Success, GPT-Balance) and region preservation (GPT-Over) compared to a broad selection of recent diffusion and AR-based methods.", "weaknesses": "1. The Related Work underappreciates or omits explicit discussion of closely related contemporaneous models for instruction-based or in-context image editing. [1,2,3] are especially relevant and directly relate to the manuscript’s focus and should be discussed in both the Related Work section and empirical comparisons if possible.\n\n2. The approach is mostly evaluated in the standard fine-tuned regime and does not examine how VAREdit generalizes to unseen editing types, tasks, or user instructions without retraining—settings increasingly emphasized in recent autoregressive editing work[1]. The absence of in-context or few-shot adaptation benchmarks leaves a gap in understanding VAREdit’s flexibility beyond its tuned task distribution.\n\n3. While qualitative figures suggest strong performance, a more honest (or even quantitative) exploration of limitations or failure modes (especially for global/text edits where 2.2B model underperforms) would strengthen the impact. For example, are there common instruction types, object classes, or image domains where even the SAR variant consistently underdelivers, and why? Are these failures more about model scale, data coverage, or something intrinsic to next-scale AR modeling?\n\n4. All quantitative evaluation relies on CLIP and GPT-based metrics, which, while valuable, do not wholly replace exhaustive user studies, especially regarding over-editing artifacts or semantic misalignments. Some visual results imply subjective improvement, but explicit human preferences or error typologies are missing.\n\n5. The experiments are mostly confined to two academic datasets. There is little examination of generalization to diverse, out-of-distribution, or real-world photographic content, which is critical for practical adoption. Even a small cross-dataset or wild-image ablation would be illuminating.\n\n6. While the SAR module is intuitively described, the formulation around how scale-aligned features are constructed and integrated into the attention mechanism could be mathematically tighter. For example, clarification and explicit definition of how $\\operatorname{Down}(\\cdot)$ and its learned parameters are shared or adapted across scales would improve reproducibility. The transition from Equation 2 (cumulative features) to the SAR-enhanced self-attention mechanism could also use more technical rigor regarding masking, normalization, and gradient flow. Moreover, practical details such as how SAR interacts with positional encoding or codebook lookups for each scale should be more explicitly stated, especially since Figure 2 elides some of these corners.\n\n7. While Figure 2 effectively visualizes the end-to-end pipeline, it omits some low-level components, leading to slight ambiguity (e.g., are the multi-scale VQ-Encoders shared across source and target? Are all tokens attended equally pre- and post-SAR? How are target and source regions aligned spatially or by edit mask, if at all?). Likewise, the heatmaps are highly informative, but a more interpretable color scale or clear highlighting of the causal directionality would help non-specialist readers.\n\n8. The training dataset is aggregated and post-processed using a vision-language model filter, but only high-level processing details and a prompt are provided. Concrete numbers or public release status for the filtered data subsets would enhance reproducibility. Similarly, claims that code/models will be released “after acceptance” limit the immediate reproducibility of results.\n\n9. Although the cited baselines are comprehensive for diffusion and AR models, the omission of multimodal LLM-based frameworks as competitors [2] weakens the claim of state-of-the-art. Even a partial or qualitative comparison would be helpful, as these systems are becoming increasingly prominent.\n\n10. While the SAR mechanism is well-motivated, the extent to which it represents a substantial departure from architectural adjustments (as opposed to an incremental fix) is debatable. The key technical novelty (selective multi-scale reference injection in the first layer) may be seen as a relatively modest extension rather than a fundamentally new paradigm.\n\n[1] Unleashing In-context Learning of Autoregressive Models for Few-shot Image Manipulation.\n\n[2] Guiding Instruction-Based Image Editing via Multimodal Large Language Models\n\n[3] Fireedit: Fine-grained instruction-based image editing via region-aware vision language model", "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": "4brBWpt9EQ", "reviewer": "Reviewer_jZVb", "summary": "The paper proposes VAREdit, a visual autoregressive (VAR) framework that reframes instruction-guided image editing as a next-scale prediction problem. Given an instruction and a source image, a multi-scale tokenizer plus a VAR Transformer predicts target residuals from coarse to fine. A key observation is that using only finest-scale source features to condition coarse target predictions causes a scale mismatch. The authors introduce a Scale-Aligned Reference (SAR) module that injects scale-matched conditioning only in the first self-attention layer, while subsequent layers keep using finest-scale conditioning. On EMU-Edit and PIE-Bench, VAREdit reports higher CLIP and GPT-based metrics than diffusion and AR baselines, and achieves ~1.2 s latency for 512*512 edits with an 8.4B model.", "questions": "Following the Weakness section, my questions are as follows:\n\nQ1: Could you provide the self-attention heatmaps under the SAR setting? I would like to visually confirm how SAR changes the attention distribution compared to the full-scale setting.\n\nQ2: Can you provide references to support the claim in lines 131-132: *Compared to diffusion\nmodels, AR approaches generally offer superior prompt adherence and faster inference.*\n\nQ3: Please can you include performance comparisons with existing frontier editing models (e.g., GPT-4o-Image, Qwen-Image, etc.) in your experiments?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The paper introduces a novel method called Scale-Aligned Reference (SAR), which effectively balances computational efficiency and editing performance. \n- The proposed method achieves strong performance on EMU-Edit and PIE-Bench, with moderate latency, outperforming several diffusion-based and autoregressive (AR) baselines.\n- The paper includes comprehensive experimental analysis; it conducts attention-level investigations to motivate the design of SAR, provides both qualitative and quantitative results, and performs ablation studies to demonstrate the effectiveness of SAR.", "weaknesses": "- the paper provides self-attention heatmaps based on the full-scale setting to motivate the design of SAR, but a similar analysis of the tuned model is missing; including this will cross-validate the modeling choice and strengthen the motivation.\n- the paper relies on GPT score to evaluate editing models; however, GPT judges may introduce hallucinations and prompt-induced biases. A better way would be to use a controlled human study to cross-validate the reliability of using GPT as the evaluator.\n- while the method demonstrates strong performance, it does not compare against frontier editing models such as GPT-4o-Image, FLUX.1 Kontext, or Qwen-Image.\n- the authors claim their model as \"the first tuning-based visual autoregressive model for instruction-guided image editing.\", which is false, see [NEP: Autoregressive Image Editing via Next Editing Token Prediction](https://arxiv.org/pdf/2508.06044). VAR broadly means autoregressive modeling of visuals. The authors might be using it to indicate \"image generation via next scale prediction,\" but it is very misleading. It would be better to spell it out.", "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": "AELc243xSx", "reviewer": "Reviewer_dF8r", "summary": "This paper introduces VAREdit, the first tuning-based visual autoregressive model for instruction-guided image editing. It proposes the novel SAR module to mitigate the scale mismatch problem in the VAR tuning process. VAREdit achieves SOTA across different image editing benchmarks. The paper is well written and organized, despite the lack of reasoning-based and multi-step image editing analysis.", "questions": "See the weakness", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Novel Paradigm: The paper introduces VAREdit, the first tuning-based visual autoregressive (VAR) framework for instruction-guided image editing, which reframes editing as a next-scale prediction task. The proposed Scale-Aligned Reference (SAR) module effectively mitigates the scale mismatch problem by injecting scale-matched conditioning only into the first self-attention layer. The motivation and design are well-analyzed through self-attention heatmap studies.\n\n2. Efficiency and Scalability: VAREdit achieves 2.2× faster inference than comparable diffusion models while maintaining superior quality. The scalability from 2.2B to 8.4B models shows consistent improvement across all editing types.\n\n3. Well Writing and Structure: The paper is clearly structured, with detailed methodological explanations, visualizations, and ablations. It maintains excellent reproducibility and clarity in presentation", "weaknesses": "Because of its AR-based nature, I wonder if such a VAR-based image editing method can show strong ability in reasoning-based image editing and multi-step image editing tasks. If the author can include some relevant experiments, such as WISE or GEdit, I would consider raise the score.", "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)