NEP: Autoregressive Image Editing via Next Editing Token Prediction
Authors: Huimin Wu
Xiaojian Ma
Haozhe Zhao
Yanpeng Zhao
Qing Li
Venue: NeurIPS 2025 poster ()
Keywords: image editing
auturegressive image generation
test-time scaling
Primary area: deep_learning

Abstract: Text-guided image editing involves modifying a source image based on a language instruction and, typically, requires changes to only small local regions. However, existing approaches generate the entire target image rather than selectively regenerate only the intended editing areas. This results in (1) unnecessary computational costs and (2) a bias toward reconstructing non-editing regions, which compromises the quality of the intended edits. To resolve these limitations, we propose to formulate image editing as $\textbf{N}$ext $\textbf{E}$diting-token $\textbf{P}$rediction (NEP) based on autoregressive image generation, where only regions that need to be edited are regenerated, thus avoiding unintended modification to the non-editing areas. To enable any-region editing, we propose to pre-train an any-order autoregressive text-to-image (T2I) model. Once trained, it is capable of zero-shot image editing and can be easily adapted to NEP for image editing, which achieves a new state-of-the-art on widely used image editing benchmarks. Moreover, our model naturally supports test-time scaling (TTS) through iteratively refining its generation in a zero-shot manner.

NEP: Autoregressive Image Editing via Next Editing Token Prediction
Huimin Wu
Xiaojian Ma'
Haozhe Zhao²
Yanpeng Zhao
Qing Li
State Key Laboratory of General Artificial Intelligence, BIGAI
"Peking University
Project website: happbigaa.githhb....
Input
Mask
Ground truth
MagicBrush
NEP (ours)
"add tall shrubs"
"erase the sheeps"
Our approach avoids full-image generation and does not introduce unintended changes as the previous diffusion iiffusion-mmoee--beee editing approach [58].
Figure 1:
Abstract
Text-guided image editing involves modifying a source image based on a language instruction and, typically, requires changes to only small local regions. However, existing approaches generate the entire target image rather than selectively regener- ate only the intended editing areas. This results in (1) unnecessary computational costs and (2) a bias toward reconstructing non-editing regions, which compromises the quality of the intended edits. To resolve these limitations, we propose formu- late image editing as Next Editing-toker Prediction (NEP) based on autoregressive image generation, where only regions that need to be edited are regenerated, thus avoiding unintended modification to the non-editing areas. To enable any-region editing, propose to pre-train any-order autoregressive text-to-image (T2I) model. Once trained, it is capable zero-shot image editing and can be easily adapted to NEP for image editing, which achieves new state-of-the-arr on widely used image editing benchmarks. Moreover, our model naturally supports test-time scaling (TTS) through iteratively refining its generation zero-shot manner.
Introduction
Text-driven image editing aims to modify a source image following given language instruction. Typically. modifications are confined to small local regions (editing regions) while most of the image remains unchanged (non-editing regions). A predominant paradigm for solving task through
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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the diffusion model 40 39 31, but standard diffusion models struggle with controllable editing, that is, editing only a target region without altering the surrounding areas. To tackle this challenge. an inversion technique has been proposed and augmented with diffusion-based image generation models [39] 21] The core idea of this method to find the mapping of non-editing regions to the corresponding subspace of Gaussian noise. It requires that the initial Gaussian noise that can be decoded into the source image should be pre-defined, which is, however, hard to obtain exactly, and further leads to unintended edits [л]]
more controllable paradigm is to pre-define editing regions and edit only the specified areas while preserving the rest [i 23]. However, these approaches perform full generation of the target image, including regions that are not required to be edited, and thus are suboptimal in terms of efficiency. This inefficiency is pronounced in training- basedd editing approaches [2] [5) which also demand significant computational resources learn reconstruct. Moreover, the reliance on full-image generation introduces learning bias during training; that is, image editing models tend to prioritize reconstruction for the non-editing regions over regeneration for the intended editing regions (50).
To address these issues, we introduce Next Editing-token Prediction (NEP), a new formulation of text-guided image editing based on autoregressive (AR) image generation. NEP primarily focuses on regeneration for the editing region and removes the need for optimizing reconstruction for the non- editing areas. Consequently, it improves efficiency and circumvents the learning bias simultaneously, Since the standard AR model employs a fixed raster-scan generation order, it is incompatible with NEP's requirement to generate arbitrary editing regions. To address this, we develop NEP using two-stage training strategy. First, we pre-train RIlamaGien a robust random-order AR-based text-to-image (T2I) model that supports arbitrary-order generation and zero-shot local editing. In the second stage, we fine-tune RLlamaGen to optimize NEP's editing performance. Additionally, NEP enables test-time scaling through iterative refinement, improving generation outcomes. We summarize our contributions follows:
We propose new formulation of image editing as next editing-token prediction. It simplifies the learning objectives to regeneration only, leading to higher efficiency and better editing quality. Our approach sets up new records on region-based editing tasks and achieves competitive results on free-form editing benchmarks.
We propose a two-stage training regime for NEP, where the first stage creates RI lamaGenn, a new T2I model capable of arbitrary-order full image generation and zero-shot local editing.
We analyze the test-time scaling behaviors by embedding NEP in iterative refinement loop.
2
Methods
In this section, we first introduce the pre-training approach RIlamaGeen that can generate image tokens in any user-specified order (82.T) Then, elaborate on NEP for image editing ($2.2). Finally, we introduce test-time scaling strategies ($2.3) by integrating NEP in an iterative refinement loop.
2.1 NEP Pre-training
Preliminaries on LlamaGen. The NEP framework is versatile and compatible with various design choices (2]ices[[2]4]] 49 48). In this work, we build upon LlamaGen (2), the first open-source text-conditioned autoregressive model outperform diffusion models, leveraging its robust architecture enable NEP's random-order generation and iterative refinement for enhanced image editing and generation. To maintain potential unification with text modality, the architecture design of LlamaGen largely follows one of the popular LLMs, Llama [5] 46]. The conditioning text embeddings are extracted from FLAN T5 M7] followed by projector for dimensionality alignment. The text embeddings are left-padded to a fixed length LT and prefilled to generate image tokens. Images are firstly tokenized by the encoder and quantizer of VQGAN (8) and generated token ids are mapped to RGB pixels by the decoder. Image tokens with length I. are generated in next-token prediction fashion. Formally, given a text sequence T,, the sequentialized image tokens I = {11, I2; ..., } ). are generated by:
p(T) = II p(L (.:
(1)

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Have squirrel be looking the
Text Enceding
Image Tokenization
Mask
Image
Tokenization
Detokenization
PP8
+PR
+PEg
+P8w
+PR
+PM4
NEP
Text embeddings
T
Image embeddings 1s
Mask embeddings
Editing tokens
Figure 2: Overview of Next- Soxt-Editing-toee Prediction. The input sequence comprised of: 1) text embeddings, extracted from FLAN-T5, 2) source image embeddings, tokenized by VQGAN, and 3) mask embeddings, sequence of interleaved editing and non-editing embeddings. The output editing tokens (in raster scan order) filled back to the source image based on the editing mask. PE, denotes the learned positional embeddings that specify the token generation order.
RelamaGen: Randomized Autoregressive Text-to-lmage Generation. To address LlamaGen's limitation of generating image tokens solely in raster scan order, we extend it to create RllamaGen, which supports generating image tokens in any user-specified order, enabling flexible, arbitrary generation [26 [5] 25]. To add order awareness to the model, following [26] [5], we learn an
extra sequence positional embeddings PF1, PF2, ...; Pf,,, which is shuffled based on a random order to define the generation sequence. For each input image token, the positional embedding corresponding to the next token in the assigned order is added. Formally, the generation of an image sequence To in the order of O [01, 02.,.. is defined as:
p(oo) II p(l,, Io, PEngi lo PE
RLlamaGen supports zero-shot editing by regenerating tokens at given positions, allowing seamless transferability image editing.
2.2 NEP: feett-dditing-tker Prediction
NEP leverages three types of conditioning for region-based editing: 1) text instructions tokens, 2) source images tokens, and 3) editing region masks tokens. The tokenization of text instructions and images remains consistent with the pre-training stage. We detail the construction of editing region conditioning sequences derived from pixel-level mask M {0, #
Editing Region Conditioning (ERC) We firstly patchify the pixel-level editing mask by max- pooling each non-overlapping sliding window with the size of p x p. Subsequently, the patched mask into a sequence M = {m1, m2.... E {0. 1) The masking sequence IM {IM :.... I2/ is tokenized by querying two-sized codebook comprising an editing embedding Ermb and non-editing embedding Urmb, which is formally defined as:
flatten
Egmh Unmb
if M =
IM
otherwise
Our editing model processes LT + x L input tokens and generates LE editing tokens, corresponding to the masked target image tokens, denoted as TF. The generation order corresponds to the positions of the editing tokens within the raster scan order, denoted as O = [of,..., o..
Formally, our NEP strategy defined as:
Ig
p(E)) = II
pl1 1,F + PE.g,.. Lo + PE,5; I..... L.
3

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In scenarios where region editing mask is unavailable or for global editing tasks (e.g,, style transfer). the editing tokens are predicted according the raster scan order.
2.3 Test-time Scaling with NEP
NEP can be employed to support test-time scaling by integrating it into a self-improving loop. In each refinement step, prior to NEP, revision region is proposed. Existing image reward models [5]] usually produce single value for the full image. To obtain token-level dense quality scores, we calculate Grad-CAM 335 value regarding the critic model (i.e., off-the-shelf CLIP- (LIP-ViT-B/32) These values reflect each token's ' contribution to the overall image quality score, measured by reward model (i.e, ImageReward (5T), Positions that correspond to the K lowest scores are identified as the revision regions. During revision, we adopt NEP to regenerate tokens in this region, conditioning them on the remaining high-qualing tokens. After NEP, the reward model evaluates whether the revised image surpasses the original, determining whether to accept or reject the revision. To further improve quality, for NEP, we apply a rejection sampling strategy, regenerating tokens at the revision positions in multiple random orders and selecting the revision with the highest quality score. This approach demonstrates strong scaling potential, suggesting that effective revision of initial generations can significantly enhance performance.
3 Experiments
We evaluate our framework on the image editing and text-to-image generation tasks. Firstly, we introduce the full training setup that trains the RLlamaGen and NEP stage-by-stage (3..I). Secondly, we evaluate NEP for image editing and validate its design choices from various aspects ($3.2). Then, we demonstrate the results of NEP pre-training model RLlamaGen ($3.3). Finally, we showcase the test-time scaling behaviors (3.44...
3.1 Datasets and Training settings
T21 pre-training settings. We use LlamaGen-XL. with 775M parameters the base T21 model and adapt it to RLlamaGen adding 0.3M positional embedding parameters. Our training data consists of around 16M text-image pairs and is collected from multiple open-source datasets, including ALLaVA [18), [56). and ShareGPT4V 6). We train RLlamaGen for 60, 000 steps with a batch size of 360 and an image resolution of 256 x 256. The optimizer is Fused Adam with 31, .3 set to 0.9, 0.95, respectively, and constant learning rate of le-4 used. We perform training on 8 NVIDIA Tesla A100 GPUs, which takes 39 hours.
LAION (5), CC12M [4, Kosmos-G (24). LAION-LVIS, 220 (34), LAION-COCO-II
Image Editing Training Settings. We fine-tune RLlamaGen for image editing by adding two learnable embeddings (i.e., Ereb and Uemb) to specify masking regions. This strategy is computa- tionally efficient, with only 3.6k parameters introduced. Our editing model is trained on the UltraEdit dataset (60) that comprises 4 million image pairs, where 131k samples are annotated with editing regions. For those with no editing region annotations, we use them for full-image generation.
We perform training on 4 NVIDIA Tesla A100 GPUs. The model is trained for 3.9M steps with a batch size of 100 and learning rate of 1e 4. Per common practices (58 60), we evaluate models at a higher image resolution than that used during training (specifically, 512 x 512 pixels compared to 256 x 256 pixels), and fine-tur tune them the target resolution for additional 2, 000 steps. For the Emu Edit benchmark, we train our model with a learning rate of le - for 60, 000 steps.
3.2 Results on Image Editing
Benchmarks & Evaluation Metrics. We demonstrate the superiority of our approach on two widely recognized benchmarks: MagicBrush [58] and Emu Edit [36]. The MagicBrush test set provides editing region annotations for each sample, thereby facilitating the evaluation of region-conditioned editing. This benchmark assesses both multi-turn editing, which evaluates the final image after a series of edits, and single-turn editing, which assesses the target image following individual edit.
The MagicBrush benchmark provides target images and evaluates the similarity between each generated image and the corresponding target image using various metrics, including LI distance, L2

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Table 1: Results on the MagicBrush test set for region-aware editing. We compare NEP with existing approaches under single-turn and multi-turn settings with our results labeled in gray
Settings
Methods
L1t
L24
CLIP-I1
DINOT
Global Description guideed
SD-SDEdit
0.1014
0,0278
0.8526
0.7726
Null Text Inversion
0.0749
0.0197
0.8827
0.8206
GLIDE
3,4973
115.8347
0.9487
0.9206
Blended Diffusion
3.5631
119.2813
0.9291
0.8644
Single-turn
Instruction-guided
HIVE
0.1092
0.0380
0.8519
0.7500
Pixr (IP2P)
0.1141
0.0371
0.8512
0.7437
IP2P MagicBrush
0.0625
0.0203
0,9332
0.8987
UltraEdit
0.0575
0.0172
0,9307
0.8982
FireEdit
0.0701
0.0238
0.9131
0.8619
AnySD
0.1114
0.0439
0.8676
0.7680
EditAR
0.1028
0,0285
0.8679
0.8042
Ours
0.0547
0.0163
0.9350
0.9044
Global Description-guideed
SD-SDEdit
0.1616
0,0602
0.7933
0.6211
Null Text Inversion
0.1057
0.0335
0.8468
0.7529
GLIDE
11.7487
1079.5997
0.9094
0.8494
Blended Diffusion
14.5439
1510.2271
0.8782
0.7690
Multi-turn
Instruction-guided
HIVE
0.1521
0.0557
0.8004
0.6463
InstructPix2Pix (IP2P)
0.1345
0,0460
0.8304
0.7018
IP2P MagicBrush
0.0964
0.0353
0.8924
0.8273
UdiraEdit
0.0745
0.0236
0.9045
0.8505
FireEdit
0.0911
0.0326
0.8819
0,8010
AnySD
0.0748
0,0273
0.9152
0.8623
EditAR
0.1341
0.0433
0.8256
0.7200
Ours
0.0707
0.0269
0.9107
0.8493
distance, CLIP feature similarity (CLIP-I), and DINO feature similarity. Additionally, it measures text-image consistency by comparing the CLIP feature similarity (CLIP-T) between the generated image and the caption of the target image.
The Emu Edit test set does not provide target images; therefore, the evaluation of editing region regeneration is conducted separately from the reconstruction of unedited regions. The regeneration process is assessed using two metrics: CLIP text-image similarity (CLIPout) and CLIP text-image direction similarity (CLIPdir) measure the consistency between the change in images and the change captions. The reconstruction quality is measured by comparing the edited image to the original source image in terms of L1 distance, CLIP image similarity (CLIPimg), and DINO similarity.
3.2.1 Quantitative Results
We demonstrate the superiority of NEP in terms of region-aware editing on the MagicBrush test set. The compared prior arts broadly fall into two categories: (1) global descrripioonbaased such as SD-SDEdit [20), Null Text Inversion
[21]. GLIDE (23), as well as Blended Diffusion [T] and
(2) instruction-guiide. including HIVE (59], InstructPix2Pix (2), MagicBrush (58), UltraEdit (60),
FireEdit [61), AD (54] and EditAR (22]. Table demonstrates that our approach achieves the
highest score for single-turn editing and better or comparable performance under the multi-turn setting. For the first time, autoregressive models can achieve top performance on well-recognized editing benchmarks.
demonstrate effectiveness free-form editing on the Emu Edit test set (36 We compare
NEP with state-of-the-art approaches including InstructPix2Pix (2), MagicBrush (58 Emu Edit (36
UltraFdit (60), MIGE [44, and AnySD (54). Table we can observe that, without resorting to editing masks, our approach still achieves comparable or better editing performance.
5

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Table 2: Results on Emu Edit Test for free-form editing. Our approach is highlighted in gray
Method
CLIPdirt
CLIPout
L1
CLIPimgt
DINO1
InstruetPix2Pix
0.0784
0.2742
0.1213
0.8518
0.7656
MagicBrush
0.0658
0.2763
0,0652
0.9179
0.8924
Emu Edit
0.1066
0.2843
0.0895
0.8622
0.8358
UltraEdit
0.1076
0.2832
0.0713
0.8446
0.7937
MIGE
0.1070
0.3067
0.0865
0.8714
0.8432
AnyEdit
0.0626
0.2943
0.0673
0.9202
0.8919
Ours
0.1064
0.3078
0.0781
0.8710
0.8440
Table 3: Ablation studies the MagicBrush test set under the multi-turn setting. We validate the contribution of each design choice by removing them and observing the performance drop. We ablate two aspects: 1) ERC by removing the editing & unediting tokens inferred from editing region masks, and 2) NEP vs. NTP by generating full image tokens. The default setting is highlighted in gray
Methods
#Ouiput Tokens
L14
T.2.
CLIP-I
DINOT
NEP
L
0.0712
0.0272
0.9097
0.8459
w/o ERC
L
0.0741
0.0281
0.9040
0.8372
NTP
0.0968
0.0309
0.8854
0.8235
3.2.2 Ablation Study
We perform ablation studies on the Magicbrush multi-turn test set. For each configuration, we report the results of the models trained for 30, 000 steps. We assess two critical design choices. First, we exclude mask embeddings, relying solely on text and source images as inputs, which degrades performance shown in Table 3 Qualitatively,
observe that removing ERC increases the likelihood of the model making no changes to the source model, as demonstrated in Figure 3 Second, remove the next editing token positions by generating all tokens in raster scan order, following an NTP framework. Without any priors on editing regions, this leads to a significant performance drop, highlighting the need for targeted token generation.
3.2.3 Computational efficiency
comparative results computational cost. NEP requires higher GPU resources due to the con- catenation of mask embeddings along the sequential dimen- sion (Section 2.2). which increases sequence length and attention computational cost. Despite this, our approach achieves the fastest editing speed as we only need to pre- dict editing region tokens rather than the whole image as diffusion models or AR AR-based models do.
demoostrates
Table 4: Computational cost averaged across MagicBrush test samples.
Methods Memory (GB)
Inference time (s)
UltraEdit FAHAR NEP
4.04
2.94
6.59
10.70
13.25
2.88
3.2.4 Qualitative Results
Figure presents qualitative comparisons with state-of-the-ar methods. Apart from avoiding un- intended modifications the input image, as shown Figure T our approach excels following the provided instructions to perform faithful and accurate modifications. Additionally. it is capable of making fine-grained modifications (e.g., changing the outfit), showcasing its high versatility and precision in handling complex editing instructions.

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Original
Masked
Ground Truth
Original
Masked Original
Ground Truth
Original
ERC
ERC
ERC
ERC
let there be cabin the back
add bowl of tomato soup"
Lett's add a turtle on the beach.
"add a lighthouse"
Figure 3: Visualized ablation on ERC. This demonstrates that removing Editing Region Condition- ing increases the editing model's change refuse to modify the source image. Best viewed zoomed in and color.
Input
Mask
Ground truth
IP2P
Magic Brush
UltraEdit
AnySD
NEP (ours)
"Could be glass the table?"
"let there be sports TV"
"let the designer gown"
Figure Comparative editing results. This demonstrates that our approach can make more faithful edits source images, either by updating objects (case #1, #2), or making fine-grained edits (case #3).. Best viewed zoomed and in color.
3.3 Results on NEP Pre-training
To better understand how NEP works, also evaluate the intermediate text-to-image model RLlam- aGen obtained during NEP pretraining. RLlamaGen acquires the zero-shot editing ability without sacrificing text-to-image generation performance.
3.3.1 Zero-shot Image Editing
We demonstrate that RLlmaGen is readily capable of image editing. This is achieved by regenerating tokens in the editing regions. Figure 5 demonstrates that RLlmaGen can make fine-grained and coherent edits.
Comparison with Localized Edit- ing Approaches. We compare our zero-shot editing performance against aMUSEd (27). which also capable of localized zero-shot editing. We use its publicly available checkpoint for com- parison, adhering its default configu-
Table 5: Comparative Results on Zero-shot Editing on MagicBrush test set.
Settings
Methods
L11
1.2.
CLIP-I1
DINO?
Single-turn
aMUSEd
0.0913
0.0300
0.8802
0.8131
Ours (zero shot)
0.0743
0.0211
0.9032
0.8509
Multi-turn
aMUSEd
0.1034
0.0361
0.8689
0.8092
(zeto-shol)
0.0916
0.0319
0.8798
0.7859

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Input
Prediction
Ground truth Editing mask
Input
Prediction
Ground truth Editing mask
"Pancake with brown topping and cream."
"A black refrigenator with its cabinet door closed in kitchen.
"Club sandwich with fries and mustard."
"Red surfboard on grass in backyard."
Figure 5: Examples of IllamGeen's zero-shot editing capability. It can make fine-graines edits such as adding external objects (ice cream in example #1), changing the state of input objects (cabinet door open closed in example #2), changing the semantics (chips -> fries in example #3), and changing the color (white + red in example #4). Best viewed zoomed in and in color.
rations Results the MagicBrush dataset show that approach outperforms aMUSEd. This is attributed to our method's s ability to enable fine-grained editing by keeping all source image tokens visible the generation model, whereas aMUSEd replaces edited regions with mask tokens, limiting its precision.
Ablations on Generation Order. Alter- native to the default generation order, i.e., intro-
Table 6: Ablations on Generation Order for Zero-shot Editing on MagicBrush test set.
in-mask raster scan order, duced in Section 3.3, we employ random generation order for zero-shot image edit- ing. The results in Table demonstrate that altering the generation order has neg- ligible impact on the effectiveness of our approach, confirming its robustness.
Settings
Methods
CLIP-1
DINOT
Single-turn
In-mask random order
0.0741
0.0211
0.9027
0.8482
lo-misk rister order
0,0743
0.0211
0.9032
0,859
Multi-turn
0,7833
In-mask raster scan order
0.0916
0.0319
0,8798
0,7839
3.3.2 Text-to-lmage Generation Results
Benchmarks Evaluation Metrics. We evaluate the image generation quality on MS-COCO 30K in terms of Fréchet Inception Distance (FID) and CLIP similarity. FID reflects the fidelity and diversity of generated images. It measures the distance between the ground truth image distribution and the generated image distribution, where the distributions are constituted of Inception V3 43 embeddings extracted from corresponding images. The CLIP score is used to evaluate the instruction- following ability of T2I models. measures the similarity between the vision embeddings extracted from the generated image and text encoder embeddings extracted from corresponding captions.
We demonstrate that randomized pre-training preserves raster generation capability. Moreover, employing NEP test-time scaling further improves generation performance. Table 7a shows that RLlamaGien outperforms its baseline (line 2 line 1), and performs similarly with LlamaGen tuned for the same number of steps (line 2 vs. line 3). Scaling NEP for self-refinement can obtain 1.5% improvement terms of CLIP and 11.4% reduction FID (line line 2).
3.4 Results on Test-time Scaling of NEP
We evaluate our self celf-improvement strategy on top NEP, which iteratively revises the model's previous generation. This self-improvemennt can be effectively scaled through multi-round iterative refinement. Empirical evidence suggests that masking out previously generated tokens during the revision process yields superior results; thus, adopt this approach
default method.
We demonstrate the scaling effects of NEP in Table 7b] where we observe consistent improvements as the number of revision rounds increases. This strategy can be further enhanced by utilizing stronger

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Original
Round
Round
Round
Original
Round
Round 2
Round
man holding a camera up over his left shoulder." "
"A boat floating on lake through brick bridge."
"A passenger plane that is parked the runway." "
kitchen filled with lots of counter top space.
Figure 6: Self-improving RIlamaGen. By gradually revising the original output, obtain images better aligned with instructions and with higher fidelity. Best viewed zoomed and in color.
Table Results on NEP pre-training and TTC. The pre-trained RLlamaGen enables arbitrary order generation without sacrificing generation quality. NEP can be employed for test-time scaling which enhances the generation further.
(a) Pretraining schemes. Comparative results be- tween LlamaGen baseline, RLlamaGen fine-tuned pre-defined number steps, and LlamaGen fine-tuned for the same number of steps.
(b) Test-time scaling w/o post-training. NEP can used to iteratively revise generated images. The generation quality gradually improves saturates after 2 iterations.
Methods
CLIP
FID|
# Revision rounds
CLIPT
FID,
LlamaGen
0.320
15.07
0.332
9.94
LlamaGen ft.
0.326
12,00
0.332
9.93
RelamaGen
0.325
11.49
0.332
9.85
TTS w/ NEP
0.330
10.18
0.332
9.82
verifier models and training the model for self- self-improvement. The revision process is visualized in Figure showcasing better alignment with the conditioning text prompts and higher fidelity.
Related Works
4.1
Text-to-Image Generation
Text-to-image generation has become cornerstone of modern artificial intelligence, enabling to create visual content based on textual descriptions. Pioneering models such as Generative Adversarial Networks (GANs) [00 make groundbreaking breakthroughs by generating high-fidelity images. AttiGAN [52] built on StackGAN [57] achieves better alignment with text instructions. However, GANs still faced challenges like training instability (e.g., mode collapse, where the model generates limited varieties of images) and difficulty with highly detailed or multi-object scenes, setting the stage for the next evolutionary step.
More recently, diffusion models [37 38 like Stable Diffusion 32 have emerged, creating realistic images by iteratively denoising random noise guided by text descriptions, setting standard for quality and versatility. However, the learning paradim and architectures diverge from well-established large language models (LLMs) 33., making it difficult for artificial general intelligence featuring shared framework for various modalities.
In this regard, a line of works 28 29 53 resort to autoregressive models for visual generation. Images are tokenized into a sequence of tokens and generated sequentially based on prefilled text tokens. Benefiting from large-scale models and datasets, they can create photorealistic images with remarkable text-following capability. This field further advanced by several open-soures works,
such LlamaGen 42 Emu3 [48), and Janus 49

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4.2 Image Editing
Image editing builds on text-to-image generative models by conditioning outputs on source images, but preserving unedited regions poses challenge for diffusion models. These models require looking for mapping latent representations for the original RGB values, often using inversion techniques [39 [1] However, such methods typically demand inference-time tuning, such as tuning textual embeddings [ model weights 33 47 or null-text embeddings [2] to enable classifier-fres
guidance 12]. Even when noise trajectories across varying levels are available, maintaining unedited regions is not assured. For instance, Coopp-tooPronp [1] introduces time threshold to prioritize generating target object geometry through text-to-image steps without source image conditioning, trading off reconstruction accuracy for generative flexibility.
Efforts to guide edits using sper-fid masks have been explored in both training-free free M and training-basee -ased approaches 23 58 60) Training-fres methods apply masks across all diffusion steps to blend source image latents with text-conditioned outputs, while training-basee based methods append an extra channel to the source image for guidance. Despite these advancements, both approaches require full image regeneration, which hampers efficiency during training and inference.
In contrast, work enables localized editing by regenerating tokens solely within user-defined regions, preserving pixels outside these areas without modification. Leveraging user-provided masks introduces minimal limitations, thanks to recent advances in segmentation techniques [[5 30 [6].
4.3 Test-time Scaling for Text-to-Image Generation
The success of LLMs' inference-tine scaling motivates the exploration of similar behavior for text-to-image generation. Existing approaches mainly investigate diffusion model scaling. either by increasing the denoising step 14 or employing best-of-N sampling [9]. More recently, new test-time scaling approaches have emerged that enable revising prior generations by incorporating corrections and feedback into the context [17. However, an additional post-training stage is required to support their iterative refinement, limiting their flexibility and increasing computational demands. In this work, we investigate inference-time scaling autoregressive image generation models that can conduct ceff-improvement utilizing NEP, offering a new perspective on enhancing model performance during testing without dedicated post-trate pest-training.
5 Conclusion
In this work, we propose a next-editing poken-prediction pipeline for text-driven image editing. It allows for easy localized editing without making unintended modifications to the non-editing region. To support regeneration at any user-specified position, we pre-train an any-order autoregressive T21 model that can generate tokens in arbitrary orders. Furthermore, we demonstrate NEP can be integrated into an iterative refinement loop for test-time scaling.
Limitations and Broader Impacts
Limitations. While the proposed approach demonstrates promising results, it relies on user-pror ser-provided masks for guidance to prevent unintended modifications to the source image. This requirement adds extra computation annotation, making the process less efficient. We plan to address automated and unified masking region localization in future work. Additionally, the robustness of Neural Editing Propagation (NEP) to noise in editing region masks remains uncertain. Imperfect user-specified masks lead to two primary scenarios: 1) the segmentation mask is larger than the ground truth editing region, and 2) the segmentation mask is smaller. In the first scenario. NEP exhibits robustness, achieving comparable results, as shown in Table 2 for free-form image editing without a mask. In the second scenario, our approach lacks specific optimization. We plan to develop pipeline for automatically refining user-specified masks in future work.
Social impacts. Our primary motivation for developing image editing algorithms is to foster innovation and creativity: however, we recognize that they also present significant ethical and societal challenges. We are committed to minimizing these risks by filtering training images for unsafe content and restricting the model's 's use to research purposes only upon release. In the future, we will actively engage in discussions and initiatives aimed at mitigating these risks.
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Reviews:
[{"review_id": "fwIfIhDdsL", "reviewer": "Reviewer_TJgQ", "summary": "Next Editing-token Prediction (NEP) is an innovative autoregressive approach to text-guided image editing that simply regenerates the tokens under user-defined editing masks, so the proposed method doesn't have to produce the whole image. This strategy fixes problems with inefficiencies and unplanned changes that are frequent in diffusion-based approaches. NEP goes through two steps of training: first, trains RLlamaGen, a randomized autoregressive text-to-image model that can generate tokens in any sequence, and then fine-tunes for localized editing using the UltraEdit dataset. NEP can do zero-shot editing, region-based control, and test-time iterative refining. The proposed method gets excellent results on MagicBrush and is competitive on EmuEdit.", "questions": "1. Mask Robustness: How does NEP handle noisy or misaligned editing masks? Have you tested its robustness to common segmentation imperfections?\n\n2. Computational Efficiency: Can you detail NEP’s GPU hours, memory usage, and inference cost including the impact of iterative refinement compared to diffusion or AR baselines?\n\n3. Generalization & Failures: Beyond UltraEdit, how does NEP perform on natural, real-world images and masks? Can you share any failure case studies?", "limitations": "While NEP offers strong editing performance, it relies on synthetic training data and clean user-provided masks, which may limit its generalization to real-world scenarios involving noisy or imperfect inputs. The model’s robustness to such imperfections is not evaluated. Its autoregressive nature introduces error accumulation during generation, and although iterative refinement helps, it adds inference overhead.", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths: \n\n1. Efficient and Targeted Editing: NEP regenerates only user-defined masked regions, avoid unnecessary full-image reconstruction, which makes NEP computationally  friendly, it also eliminates editing bias common in diffusion-based approaches.\n\n2. State-of-the-Art Results: NEP achieves good performance on MagicBrush (L1: 0.0547 single-turn, 0.0707 multi-turn) and performs competitively on EmuEdit.\n\n3. Well-Designed Framework: The two-stage pipeline pre-training RLlamaGen for arbitrary-order generation and fine-tuning for editing supports both zero-shot and fine-grained editing. Test-time iterative refinement further boosts quality in a scalable way.\n\nWeaknesses: \n\n1. Baseline Coverage: Lacks evaluation against recent AR editing methods like EditAR [1] and ControlVAR [2], which leads to gaps in comparative analysis.\n\n2. Synthetic-Only Training Data: Fully reliant on UltraEdit-generated masks, which may not reflect real-world editing noise and could limit generalization of the NEP. \n\n3. Engineering Over Invention: While NEP shows a robust and practical autoregressive image editing method, efficient mask-constrained editing and gradual refinement, NEP stands on the shoulders of existing techniques rather than forging a new theoretical frontier in generative modeling.\n\n*References*\n\n[1] Mu et al. *EditAR: Unified Conditional Generation with Autoregressive Models*, arXiv:2501.04699\n\n[2] Li et al. *ControlVAR: Exploring Controllable Visual Autoregressive Modeling*, arXiv:2406.09750", "was_revised": true, "final_justification": "The author addressed most of my concerns.", "rebuttal": "We appreciate the reviewer’s constructive and encouraging feedback on the novelty, efficiency, and effectiveness of NEP. To address your concerns, we have included additional comparative results to further demonstrate NEP’s empirical effectiveness (W1) and efficiency (Q2). We have also clarified its generalization ability (W2, Q3). Furthermore, we have expanded our discussion on distinctions from prior works (W3) and elaborated on limitations, particularly regarding reliance on user-specified masks (Q1). For all raised concerns, we have listed our point-to-point responses in the following.\n\n> W1. Baseline Coverage: Lacks evaluation against recent AR editing methods like EditAR [1] and ControlVAR [2], which leads to gaps in comparative analysis.\n\nThank you for pointing out this important question. We agree that including more comparitive results with recent advances is beneficial to the solidness of our work. However, we respectfully wish to clarify the distinction between our work and ControlVAR. ControlVAR primarily focuses on image-driven editing, utilizing visual inputs such as canny edge maps, depth maps, or segmentation maps as the primary guidance. In contrast, our approach centers on text-driven editing, where textual descriptions or prompts serve as the guiding input. For a detailed discussion of the differences between these two tasks, please refer to Section 3.1.4 of the survey [1]. Due to their distinct input modalities and objectives, these methods address different problem domains and are not directly comparable.\n\nTherefore, we only comprare with EditAR. As EditAR was not accepted at the time of our initial submission, it was not included in the original manuscript. In response to your feedback, we have conducted a comparative experiment on the Magicbrush dataset for the revised version. We use EditAR’s publicly available checkpoint, inference code, and default hyperparameters for the editing task. The results are presented below:\n\n\n| Settings | Methods | L1↓ | L2↓ | CLIP-I↑ | DINO↑ |\n| --- | --- | --- | --- | --- | --- | \n| Single-turn | EditAR | 0.1028 | 0.0285 | 0.8679 | 0.8042 | \n| Single-turn | Ours | 0.0547 | 0.0163 | 0.9350 | 0.9044 |\n| Multi-turn | EditAR | 0.1341 | 0.0433 | 0.8256 | 0.7200 | \n| Multi-turn | Ours | 0.0707 | 0.0269 | 0.9107 | 0.8493 | \n\n\nOur approach demonstrates significantly improved performance. This advantage stems from NEP’s optimization, which focuses exclusively on editing and regeneration tasks. In contrast, EditAR must balance both regeneration and reconstruction, which can compromise its editing performance. \n\nWe have included this comparative results and clarified this distinction in the revised manuscript, to better highlight NEP’s strengths. We appreciate the reviewer’s valuable input and welcome further suggestions to refine our analysis.\n\nPlease kindly refer to our response to Reviewer KK48 W1 for more comparative experiments.\n\n\n[1] Xiong, Jing, et al. Autoregressive models in vision: A survey. arXiv preprint arXiv:2411.05902 (2024)\n\n\n\n\n\n\n> W2. Synthetic-Only Training Data: ...could limit generalization of the NEP.\n\nWe sincerely thank the reviewer for their insightful question. We respectfully wish to clarify that using synthetic datasets for training editing models is a standard practice due to the high cost and complexity of acquiring real-world editing datasets. We select UltraEdit as our training dataset given its widespread use and established relevance in the field. The strong performance of our model, NEP, on real-world test sets such as Magicbrush and Emu Edit demonstrates its robust generalization capabilities. We welcome any suggestions from the reviewer regarding suitable real-world datasets that could further enhance our training pipeline.\n\n> W3. Engineering Over Invention: ... NEP stands on the shoulders of existing techniques rather than forging a new theoretical frontier in generative modeling.\n\n\nThank you for acknowledging the strengths of our method. We respectfully wish to clarify that NEP fundamentally differs from existing autoregressive (AR) models in two key aspects. First, it enables editing at any position by leveraging the next token's positional embedding to \"probe\" the input token, while AR-based models have to generate the whole image, even if the editing is just local. Second, NEP's \"editing any position\" regime makes **self-improvement** during image generation possible. Please kindly refer to our response to Reviewer KK48 W4 and Sec. 3.4 in the main text for test-time scaling results. These distinctions enable NEP to deliver robust, real-world performance that surpasses AR models. We are grateful for the reviewer’s insights and are eager to further refine our approach while exploring new theoretical advancements in generative modeling.\n\n> Q1. Mask Robustness: How does NEP handle noisy or misaligned editing masks? Have you tested its robustness to common segmentation imperfections?\n\nThank you for raising this important question. We agree that further expanding discussions on NEP’s dependence on masks enhances the manuscript. As noted in the limitations section, NEP relies on accurate editing region masks. In cases where user-specified masks are imperfect, we identify two primary scenarios: 1) the segmentation mask is larger than ground truth editing region, and 2) the segmentation mask is smaller. For the first scenario, NEP demonstrates robustness, achieving comparable results, as shown in Table 2 in the main manuscript, where we tested an free-form image editing without mask region. For the second scenario, our current approach is not specifically optimized. In response to your feedback, we have added a discussion of this limitation in the revised manuscript and plan to develop an editing pipeline that automatically refines user-specified masks in future work. We appreciate your valuable input and believe these revisions strengthen our contribution.\n\n\n\n\n> Q2. Computational Efficiency: Can you detail NEP’s GPU hours, memory usage, and inference cost including the impact of iterative refinement compared to diffusion or AR baselines?\n\nWe thank the reviewer for raising this insightful question, as validating the inference efficiency is crucial to our contribution. Please see the following for the evaluation results:\n\n|Methods|GPU hours (h)| Memory (GB) | Inference time (s) |\n|-|-|-|-|\n|UltraEdit|Unavailable|4.04|2.94|\n|EditAR|Unavailable|6.59|10.70|\n|NEP|1560|13.25|2.88|\n\nNEP requires higher GPU resources due to the concatenation of mask embeddings along the sequential dimension (Section 2.2), which increases sequence length and attention computational cost. Despite this, our approach achieves the fastest editing speed as we only need to predict editing region tokens rather than the whole image as AR-based models do. We have included this analysis in the revised manuscript. Thank you for your valuable input, and we welcome further suggestions to enhance our discussion.\n\nRegarding iterative refinement, a direct comparison with autoregressive (AR) models is not feasible, as they lack support for test-time scaling. Although diffusion models can scale during testing, a fair and direct comparison remains challenging due to fundamental differences in their architectures and objectives. We have clarified this point in the revised manuscript. We appreciate the reviewer’s feedback and welcome further suggestions to strengthen our analysis.\n\n\n\n> Q3. Generalization & Failures: Beyond UltraEdit, how does NEP perform on natural, real-world images and masks? Can you share any failure case studies?\n\nWe sincerely thank the reviewer for the valuable question. To clarify, all our evaluations were conducted on natural images from established open-source benchmarks, including MagicBrush and Emu-Edit. The qualitative comparisons presented in Tables 1 and 2 of the main manuscript demonstrate NEP’s strong generalization from synthetic to real-world datasets. Regarding failure cases, we are unable to share visualized results due to this year’s rebuttal policy. However, in response to your suggestion, we have included a detailed analysis of failure cases in the revised manuscript. We appreciate your feedback and welcome further suggestions to enhance our work."}, {"review_id": "AdunYDQMJK", "reviewer": "Reviewer_Zgfx", "summary": "This paper introduces NEP (Next Editing-token Prediction), a novel framework for text-guided image editing that avoids regenerating the entire image and instead focuses only on the user-specified editing regions. Built on a pre-trained, arbitrary-order autoregressive model called RLlamaGen, NEP enables efficient, high-fidelity localized edits while preserving non-edited areas. It uses editing masks and random-order token generation to target only relevant regions, reducing computational cost and unintended changes. The proposed method achieves state-of-the-art performance on major benchmarks like MagicBrush and Emu Edit.", "questions": "Q1. Could the authors explore alternative strategies for NEP beyond the one described in Eq. (3)? For instance, are there different methods to represent or encode the editing regions that might yield better performance or flexibility?\n\nQ2. For further questions and concerns, please refer to the Major and Minor Weaknesses sections above.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n\nS1. The paper demonstrates strong experimental results; NEP consistency outperforms a variety of baseline methods across several quantitative metrics, as shown in Table 1. This indicates the effectiveness of the proposed method.\n\nS2. The introduction of a test-time scaling strategy is another strength of a paper, as it provides a practical mechanism for iterative self-improvement in image quality without additional training.\n\n**Major Weakness**\n\nW1. Absence of a direct comparison with other autoregressive image editing methods: Given the autoregressive nature of NEP, such comparisons are essential and would greatly strengthen the empirical validation.\n\nW2. The novelty of the proposed method is somewhat limited. It remains unclear how NEP fundamentally differs from prior works that leverage autoregressive methods for image editing, and this lack of distinction weakens the contribution.\n\nW3. The paper lacks an analysis of the computational overhead associated with the proposed method. Important details, such as memory usage (in GB), training and inference runtimes (in seconds), should be explicitly mentioned to quantitatively assess efficiency of NEP.\n\n\n**Minor Weakness**\n\nW4. The overall writing and presentation are unclear in several parts, making it hard to understand the main strategy and contributions of the paper. A more detailed and intuitive explanation of the NEP framework, supported by a well-structured figure (i.e. improving Figure 2 for intuitively explaining Eq. (4)), would significantly enhance clarity.\n\nW5. The organization of the paper could be improved. For example, moving the related work section into Section 2 would provide a more logical flow, since the related work section is essential to fully understand the method. Also, distincting the preliminaries and the proposed method section would help readers to better understand the paper.", "was_revised": true, "final_justification": "During the rebuttal, my major concerns regarding (1) the novelty of the work and (2) the comparison with AR-based image editing baselines were resolved. In addition, the authors provided promising results on computational cost and discussed improvements in paper writing and presentation, which further alleviated my concerns. Therefore, I increase my score to 4.", "rebuttal": "We greatly appreciate the reviewer’s insightful and valuable comments. Thank you for your positive feedback on the novelty and empirical effectiveness of NEP. To address your concerns, we have added comparative experiments to evaluate effectiveness (W1) and efficiency (W3). We have also expanded discussions on the distinctions of this work (W2) and our design choices (Q1). Additionally, we have revised the manuscript to improve clarity and writing quality (W4) and enhance overall organization (W5). Below, we address your comments individually.\n\n\n> W1. Absence of a direct comparison with other autoregressive image editing methods: Given the autoregressive nature of NEP, such comparisons are essential and would greatly strengthen the empirical validation.\n\nThank you for your valuable suggestion. We agree that including more comparative results with other autoregressive image editing methods can make our work stronger. In response to your feedback, we have conducted a comparative experiment with EditAR[1], an autoregressive image editing approach accepted by CVPR 2025. EditAR was not included in the original manuscript as it was not accepted at the time of our initial submission. In response to your feedback, we have conducted a comparative experiment on the Magicbrush dataset for the revised version. We use EditAR’s publicly available checkpoint, inference code, and default hyperparameters for the editing task. The results are presented below:\n\n| Settings | Methods | L1↓ | L2↓ | CLIP-I↑ | DINO↑ |\n| --- | --- | --- | --- | --- | --- | \n| Single-turn | EditAR | 0.1028 | 0.0285 | 0.8679 | 0.8042 | \n| Single-turn | Ours | 0.0547 | 0.0163 | 0.9350 | 0.9044 |\n| Multi-turn | EditAR | 0.1341 | 0.0433 | 0.8256 | 0.7200 | \n| Multi-turn | Ours | 0.0707 | 0.0269 | 0.9107 | 0.8493 | \n\nOur approach demonstrates significantly improved performance. This advantage stems from NEP’s optimization, which focuses exclusively on editing and regeneration tasks. In contrast, EditAR must balance both regeneration and reconstruction, which can compromise its editing performance. We have included these comparative results and clarified this distinction in the revised manuscript, to better highlight NEP’s strengths. We appreciate the reviewer’s input and welcome further suggestions to refine our analysis.\n\n\nPlease kindly refer to our response to Reviewer KK48 W1 for more comparative experiments.\n\n\n> W2. The novelty of the proposed method is somewhat limited. It remains unclear how NEP fundamentally differs from prior works that leverage autoregressive methods for image editing, and this lack of distinction weakens the contribution.\n\nWe thank the reviewer for the valuable question. With all due respect, we wish to clarify that NEP fundamentally differs from existing autoregressive (AR) models in two key aspects. First, it enables editing at any position by leveraging the next token's positional embedding to \"probe\" the input token, while AR-based models have to generate the whole image, even if the editing is just local. Second, NEP's \"editing any position\" regime makes **self-improvement** during image generation possible. Please kindly refer to our response to Reviewer KK48 W4 and Sec. 3.4 in the main text for test-time scaling results. These distinctions enable NEP to deliver robust, real-world performance that surpasses AR models.\n\n\n> W3. The paper lacks an analysis of the computational overhead associated with the proposed method. Important details, such as memory usage (in GB), training and inference runtimes (in seconds), should be explicitly mentioned to quantitatively assess efficiency of NEP.\n\nWe thank the reviewer for raising this insightful question, as validating the inference efficiency is crucial to our contribution. We have listed these numbers below:\n\n|Methods|GPU hours (h)| Memory (GB) | Inference time (s) |\n|-|-|-|-|\n|UltraEdit|Unavailable|4.04|2.94|\n|EditAR|Unavailable|6.59|10.70|\n|NEP|1560|13.25|2.88|\n\nNEP requires higher GPU resources due to the concatenation of mask embeddings along the sequential dimension (Section 2.2), which increases sequence length and attention computational cost. Despite this, our approach achieves the fastest editing speed as we only need to predict editing region tokens rather than the whole image as AR-based models do. We have included this analysis in the revised manuscript. Thank you for your valuable input, and we welcome further suggestions to enhance our discussion.\n\n\n> W4. The overall writing and presentation are unclear in several parts, making it hard to understand the main strategy and contributions of the paper. A more detailed and intuitive explanation of the NEP framework, supported by a well-structured figure (i.e. improving Figure 2 for intuitively explaining Eq. (4)), would significantly enhance clarity.\n\nWe apologize for the confusion and sincerely thank the reviewer for the valuable feedback. We have proofread our manuscript again to resolve all confusing parts that we identify. Following the suggestion, we have also improved Figure 2 for better illustration.\n\n> W5. The organization of the paper could be improved. For example, moving the related work section into Section 2 would provide a more logical flow, since the related work section is essential to fully understand the method. Also, distincting the preliminaries and the proposed method section would help readers to better understand the paper.\n\nThank you for the feedback on the paper's organization. We agree that restructuring the paper could enhance its clarity and logical flow. Specifically, we have moved the related work section to Section 2 to provide better context for the method. We will also put \"Preliminaries on LlamaGen\" and \"RLlamaGen: Randomized Autoregressive Text-to-Image Generation\" into two subsections if the space permits.\n\n> Q1. Could the authors explore alternative strategies for NEP beyond the one described in Eq. (3)? For instance, are there different methods to represent or encode the editing regions that might yield better performance or flexibility?\n\nWe thank the reviewer for their insightful question. An alternative approach to encoding the editing region, as employed in InstructPix2Pix [2], involves treating the editing region as an additional image and concatenating it with the source image along the channel dimension. For InstructPix2Pix, this is straightforward, as it only requires modifying the first CNN layer to accommodate the increased input channel size. However, for transformer-based models, this approach is less flexible. Since the image token representation is directly projected from token IDs to the transformer’s hidden size, incorporating additional channels necessitates either a new projection layer to align with the hidden size or a modification of the hidden size across all transformer blocks. In our work, we opt to integrate masks by concatenating them along the sequential dimension after tokenizing them as embeddings matching the transformer’s hidden size. This approach enhances flexibility while maintaining compatibility with the model architecture. We have elaborated on this design choice in the revised manuscript and welcome further suggestions from the reviewer to improve flexibility or performance. Thank you for your valuable feedback.\n\n[1] Mu, Jiteng, et al. Editar: Unified conditional generation with autoregressive models. CVPR 2025.\n\n[2] Brooks, Tim, et al. Instructpix2pix: Learning to follow image editing instructions. CVPR 2023."}, {"review_id": "IhVVZ2Tfyz", "reviewer": "Reviewer_9n1p", "summary": "This paper presents a novel efficient way of modeling text-guided image editing task with autoregressive transformers and mask conditioning. By predicting “what really needs to change” rather than “the whole image,” it delivers controllable, fine-grained edits with higher faithfulness and markedly better compute efficiency, and it inherits the natural test-time scalability for further quality gains at inference time.", "questions": "1. please address weakness points\n2. Are output editing tokens always predicted in raster scan order? Since the positional embedding corresponding to the\nnext token in the assigned order is added, can we use arbitrary order for output editing tokens?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1. Any-order next-token editing. Reframes text-guided image editing as an any-order next-token prediction task, enabling the autoregressive transformer to flexibly generate visual tokens in arbitrary sequences rather than a fixed raster scan.\n\n2. Explicit spatial control via mask tokens. Introduces a pair of lightweight “edit/keep” mask tokens that encode the user-specified region, giving the model precise, learnable awareness of where changes are permitted.\n\n3. Variable-length, region-specific generation. By combining (1) and (2), the model produces only the subset of image tokens that fall inside the mask—of whatever shape or size—eliminating redundant computation on untouched areas and supporting fine-grained, mask-conditioned edits.\n\nWeaknesses:\n1. In table 3, the performance drop due to removing mask embeddings is minor. What are the intuitions behind this? Does it mean mask embeddings are redundant?\n2. What output orders are used for evaluating zero-shot image editing of RLlmaGen? Since it's trained on random order, it's interesting to see how it performs using different orders.", "was_revised": true, "final_justification": "The rebuttal addresses my concerns: (1) it resolves my major concern about the effectiveness of mask embeddings by detailing the two-stage masking mechanism and its performance impact; and (2) it addresses a minor concern by providing complementary results for alternative output orders, showing that random-order pretraining supports arbitrary-order generation at inference. Accordingly, I maintain my scores.", "rebuttal": "We thank the reviewer for recognizing the novelty, flexibility, and effectiveness of our approach. Your support of our work is sincerely appreciated. Below, we provide detailed replies to your comments and hope we can resolve your major concerns.\n\n\n> W1. In table 3, the performance drop due to removing mask embeddings is minor. What are the intuitions behind this? Does it mean mask embeddings are redundant?\n\nThank you for your constructive comment. We agree that expanding the discussion on the ablation study strengthens our work. The editing region mask is used by NEP twice. First, it determines which token to be predicted by NEP (we feed the \"probes\" corresponding to the position within the mask region to NEP). Second, we use them on the input to inform the model of editing regions during computation. In Table 3, we incrementally remove the second use (line 1 to line 2) and the first use (line 2 to line 3), observing a more substantial performance drop after removing the first use, suggesting that the first application is more critical. We have clarified this in the revised manuscript to avoid confusion. However, we do not consider the second approach redundant. Our qualitative ablation study suggests that omitting the input mask embedding condition increases the likelihood of the model making no changes to the source model. Due to this year's rebuttal policy prohibiting the sharing of visualized results, we have included them in the revised manuscript. We greatly appreciate your valuable input and welcome further suggestions to enhance our discussion.\n\n\n\n> W2. What output orders are used for evaluating zero-shot image editing of RLlmaGen? Since it's trained on random order, it's interesting to see how it performs using different orders.\n\nThank you for your question. The zero-shot image editing experiment, illustrated in Figure 4, employs the default generation order, i.e., an in-mask raster scan order, as we introduced in Section 3.3. Specifically, we filter the raster scan tokens, retaining only those in the editing region, and generate them sequentially. In response to your suggestion, we conduct a comparison with a random generation order for the editing tokens. The results are listed in the following table:\n\n\n| Settings | Methods | L1↓ | L2↓ | CLIP-I↑ | DINO↑ |\n| --- | --- | --- | --- | --- | --- | \n| Single-turn | Ours (In-mask raster scan order) | 0.0743 | 0.0211 | 0.9032 | 0.8509 | \n| Single-turn | Ours (In-mask random order) | 0.0741 | 0.0211 | 0.9027 | 0.8482 |\n| Multi-turn | Ours (In-mask raster scan order)| 0.0916 | 0.0319 | 0.8798 | 0.7859 |\n| Multi-turn | Ours (In-mask random order)| 0.0911 | 0.0316 | 0.8782 | 0.7833 | \n\nThe results demonstrate that altering the generation order has negligible impact on the effectiveness of our approach. We thank you for your valuable feedback and have incorporated this analysis into the revised manuscript to further clarify our method’s robustness.\n\n> Q1. Are output editing tokens always predicted in raster scan order? Since the positional embedding corresponding to the next token in the assigned order is added, can we use arbitrary order for output editing tokens?\n\nThank you for your insightful question. Please refer to our response to W2 for details."}, {"review_id": "JxXaa6fqWk", "reviewer": "Reviewer_KK48", "summary": "This paper introduces NEP (Next Editing-token Prediction) for text-guided image editing that uses an autoregressive (AR) model. The main goal for introducing this framework is to reduce unnecessary computational costs and unintended changes in non-edited regions. To enable editing of arbitrary regions, the method first involves pre-training RLlamaGen to generate image tokens in any user-specified order. This is achieved by incorporating shuffled positional embeddings during training. The model is then fine-tuned for the editing task, where it learns to use text instructions, a source image, and a mask to predict the new tokens for the editing region.", "questions": "1. Clarification on test-time scaling models: Could you provide the implementation details for the critic and reward models used in Section 2.3? What models were used, and how were they trained?\n2. Questionable baseline results: In Table 1, the L1 and L2 distance metrics for GLIDE and Blended Diffusion are several orders of magnitude worse than all other methods. Could you clarify if these models were implemented and evaluated correctly, as the results appear to be outliers?\n3. Missing comparisons: Why were other relevant token-based editing methods like MaskGIT or Painter, and other randomized autoregressive models like RAVG, not included in the quantitative comparisons in your tables?\n4. Inference latency: Could you provide a quantitative comparison of the inference time for a typical edit using NEP versus a leading diffusion model like InstructPix2Pix? This would provide concrete evidence for the claimed efficiency benefits.", "limitations": "The authors are up-front about the method's heavy reliance on user-provided masks, correctly identifying that this adds an extra layer of computation or annotation that impacts the overall efficiency of the pipeline.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "### Strengths\n\n* The paper demonstrates that NEP can be used for test-time scaling, where the model iteratively refines its own output by identifying and regenerating low-quality regions. \n\n* This approach achieves state-of-the-art results on standard image editing benchmarks like MagicBrush.\n\n* The idea of NEP is technically sound and the paper has successfully demonstrated its capabilities.\n\n***\n\n### Weaknesses\n\n* Incomplete experimental comparisons: The paper fails to compare NEP against other highly relevant models. The comparisons would be stronger if they included other token-based editing methods like MaskGIT [1] or Painter [2], and other randomized autoregressive models like RAVG [3].\n* No inference time analysis: A core claim of the paper is improved efficiency by avoiding full-image generation. However, the paper provides no quantitative data, such as an inference latency comparison against diffusion-based methods, to support this claim.\n* Underwhelming performance on free-form editing: On the Emu Edit benchmark, NEP's performance is not state-of-the-art. It is outperformed by other methods like AnyEdit and MagicBrush on most metrics, including L1 distance, CLIPimg similarity, and DINO similarity. This suggests the method is less effective without the strong guidance of a mask.\n* Marginal improvement from test-time scaling: The results in Table 4b show that while one round of revision offers a noticeable improvement, the gains from further iterations are minimal and can even degrade performance, as round 3 is worse than round 2. This raises questions about the robustness and practical value of multi-round iterative refinement.\n* Heavy reliance on user-provided masks: The method's precision is contingent on having an accurate editing mask. The authors acknowledge this as a limitation, as it requires either manual annotation or an external segmentation model, adding an extra step and computational overhead to the pipeline.\n* Lack of implementation details:\n    * The paper does not specify the implementation of the \"critic model\" and \"reward model\" used for test-time scaling in Section 2.3. This omission makes the test-time scaling component difficult to reproduce.\n    * Figure 2 does not clearly illustrate how the text, source image, and mask embeddings are combined and processed by the AR model to generate the final output.\n\n### References\n\n[1] Chang, H., Zhang, H., Lu, J., Koss, M., Wang, X., & Zeng, A. (2022). MaskGIT: Masked Generative Image Transformer. In *Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*.\n\n[2] Wang, X., Zhang, H., He, T., Chen, X., & Liu, S. (2023). Painter: A generalist model for in-context understanding of images. *arXiv preprint arXiv:2305.10657*.\n\n[3] Yu, Q., He, J., Deng, X., Shen, X., & Chen, L. C. (2024). Randomized autoregressive visual generation. *arXiv preprint arXiv:2411.00776*.", "was_revised": true, "final_justification": "I have read the authors' responses to my questions and concerns. The response has nicely addressed my existing concerns and therefore I increase my score from 4 to 5.", "rebuttal": "We thank the reviewer for recognizing the strengths of our approach, including its solidness and capabilities, and for providing valuable suggestions. We revised the manuscript as follows:\n- Added comparisons with related works (W1, Q3), expanded free-form editing results (W3), and updated test-time scaling results (W4).\n- Compared inference speed with state-of-the-art editing methods (W2, Q4).\n- Discussed limitations further (W5).\n- Included more implementation details (W6, Q1) and expanded MagicBrush results explanation (Q2).\n\nIn the following, we respond to the reviews point-by-point.\n\n> W1. Incomplete experimental comparisons: ...MaskGIT [1] or Painter [2], and RAVG [3].\n\nWe sincerely appreciate the reviewer's suggestion. We agree that including more comparative results is beneficial to the solidness of our work. We discuss the comparison with the mentioned approaches individually.\n1. Comparison with MaskGIT\n\nWe would like to respectfully clarify that a direct comparison with MaskGIT is not feasible, as it only supports class-conditional editing. Instead, we use its extension, MUSE [2], which supports text-prompted generation. However, MUSE's official code is not open-sourced, and reproduced implementations, such as “lucidrains/muse-maskgit-pytorch” and “huggingface/open-muse,” lack pre-trained checkpoints. To address this, we adopt aMUSEd [3], an open-source reproduction of MUSE. This approach has been integrated into the Diffusers library and we use its default configurations as outlined in its official Hugging Face blog. For a fair comparison, we compare with our zero-shot model (Section 2.1). Our results on the MagicBrush dataset show that our zero-shot approach outperforms aMUSEd. This is attributed to our method’s ability to enable fine-grained editing by keeping all source image tokens visible to the generation model, whereas aMUSEd replaces edited regions with mask tokens, limiting its precision. Please see the following for a detailed comparison.\n\n| Settings | Methods | L1↓ | L2↓ | CLIP-I↑ | DINO↑ |\n| --- | --- | --- | --- | --- | --- | \n| Single-turn | Amused         | 0.0913 | 0.0300 | 0.8802 | 0.8131 | \n| Single-turn | Ours (zero-shot) | 0.0743 | 0.0211 | 0.9032 | 0.8509 | \n| Multi-turn | Amused         | 0.1034 | 0.0361 | 0.8689 | 0.8092 | \n| Multi-turn | Ours (zero-shot)| 0.0916 | 0.0319 | 0.8798 | 0.7859 |\n\n2. Comparison with Painter\n\nWe wish to respectfully clarify that using Painter for image editing is challenging and costly because it does not accept explicit task prompts (e.g., “add a mirror to the wall”) for inference. Instead, it requires task prompts to be defined through input-output image pairs. This process is both complex and limiting, as suitable image pairs are often unavailable. In our analysis, out of 1,053 instructions in the MagicBrush test set, only 36 have corresponding input-output pairs in our UltraEdit training set (comprising 4 million pairs). To enable a fair comparison, we evaluate Painter on this subset and compare against NEP’s zero-shot inference results on the same subset. Due to the absence of complete multi-turn instruction sequences in the training data, we conducted evaluations solely under a single-turn setting. The results in the table below demonstrate that Painter significantly lags behind NEP.\n\n| Settings | Methods | L1↓ | L2↓ | CLIP-I↑ | DINO↑ |\n| --- | --- | --- | --- | --- | --- | \n| Single-turn | Painter         | 0.1443 | 0.0602 | 0.7999 | 0.6383 | \n| Single-turn | Ours (zero-shot) | 0.0797 | 0.0198 | 0.8942 | 0.8573 | \n\nThis performance gap is primarily due to the mismatch between Painter’s training tasks and image editing. It is trained on visual understanding (e.g., depth estimation, semantic segmentation, human keypoint detection, panoptic segmentation) and low-level image processing tasks (e.g., image denoising, deraining, enhancement), which differ substantially from image editing. Although it claims generalization to out-of-domain tasks (e.g., open-category object segmentation, keypoint detection, instance segmentation), these tasks, however, are limited to vision understanding. When transferred to a high-level generative task, i.e., image editing, it struggles to generate effectively. The edited images produced by Painter reveal that it often disregards the task prompt specified by the input-output image pair and applies one of its learned tasks, such as image segmentation or denoising, to process the input image. Thus, we can conclude that NEP offers superior flexibility and performance.\n\n3. Comparison with RAVG\n\nWe compare our approach with RAVG, adapted for text-to-image generation as it was originally developed for class-to-image generation. The results are presented below.\n\n| Settings | Methods | L1↓ | L2↓ | CLIP-I↑ | DINO↑ |\n| --- | --- | --- | --- | --- | --- | \n| Single-turn | RAVG adapted for T2I | 0.0711 | 0.0188 | 0.9203 | 0.8888 |\n| Single-turn | Ours                 | 0.0547 | 0.0163 | 0.9350 | 0.9044 |\n| Multi-turn | RAVG adapted for T2I  | 0.0968 | 0.0309 | 0.8854 | 0.8235 |\n| Multi-turn | Ours                  | 0.0707 | 0.0269 | 0.9107 | 0.8493 |\n\nNEP demonstrates superior performance over the adapted RAVG for image editing. This advantage stems from NEP’s optimization for editing and regeneration tasks exclusively, whereas RAVG must balance regeneration and reconstruction, resulting in compromised editing performance.\n\n> W2. No inference time analysis...\n\nWe thank the reviewer for raising this insightful question, as validating the inference efficiency is crucial to our contribution. We have listed these numbers below:\n\n|Methods| Memory (GB) | Inference time (s) |\n|-|-|-|\n|UltraEdit|4.04|2.94|\n|EditAR|6.59|10.70|\n|NEP|13.25|2.88|\n\nNEP requires higher GPU resources due to the concatenation of mask embeddings along the sequential dimension (Section 2.2), which increases sequence length and attention computational cost. Despite this, our approach achieves the fastest editing speed as we only need to predict editing region tokens rather than the whole image as AR-based models do. We have included this analysis in the revised manuscript. \n\n> W3. Underwhelming performance on free-form editing...\n\nWe sincerely appreciate the reviewer’s thoughtful feedback and the opportunity to clarify our results on the Emu Edit benchmark. We acknowledge the reviewer’s observation regarding NEP’s performance on metrics such as L1 distance, CLIPimg similarity, and DINO similarity. To provide further context, we note that these metrics measure alignment between the generated and source images, which can be less indicative of editing quality where ground-truth post-editing images are unavailable. Lower scores on these metrics may reflect a model’s tendency to copy the source image rather than editing effectiveness. In our revised manuscript, we’ve emphasized metrics like CLIPdir (consistency between caption and image changes) and CLIPout (alignment with the target caption), where NEP achieves comparable scores and, in the case of CLIPout, the highest performance. To address the reviewer’s valuable feedback, we’ve expanded our analysis in the manuscript to better explain the nuances of these metrics and their implications for free-form editing. We are deeply grateful for your insights, which have helped us strengthen our evaluation, and we warmly welcome any further suggestions to refine our work.\n\n\n\n> W4. Marginal improvement from test-time scaling...\n\nWe thank the reviewer for pointing this out. During the period between submitting our original manuscript and the rebuttal phase, we identified and corrected a bug in our test-time scaling experiment by refining predictions based on the previous round rather than the initial round. Accordingly, we have updated the results in Table 4b of the revised manuscript to reflect these corrections, as detailed below. We can observe that consistent improvement with increasing revision rounds. We appreciate the reviewer’s input and believe this revision strengthens the reliability of our findings.\n\n\n|# Revision rounds | CLIP ↑ | FID ↓ |\n| - | - | - | \n| 0 | 0.325 | 11.49 |\n| 1 | 0.330 | 10.31 |\n| 2 | 0.332 | 9.93 |\n| 3 | 0.332 | 9.85 |\n| 4 | 0.332 | 9.82 |\n\n\n\n> W5. Heavy reliance on user-provided masks...\n\nThank you for your thoughtful question. We acknowledge that the current reliance on user-provided masks introduces extra constraints to NEP's applicability, as we have discussed in our limitation section. However, we would like to respectfully point out that it is a common requirement among existing image editing methods, including Blended Diffusion, GLIDE, InstructPix2Pix, UltraEdit, and MIGE. Despite the constraint, we have evaluated NEP in a free-form editing setting (Table 2) and achieved competitive results. To address this concern, we have expanded the discussion in the revised manuscript to include our ongoing efforts to minimize mask dependency and enhance free-form editing capabilities. We greatly appreciate your feedback, as it helps guide our future work toward more seamless and intuitive editing tools.\n\n\n> W6. Lack of implementation details\n\nWe thank the reviewer for the valuable feedback and apologize for any confusion. To clarify, we utilize the off-the-shelf CLIP-ViT-B/32 as the critic model and ImageReward as the reward model. These details have been explicitly included in the revised manuscript. Additionally, the text, source image, and mask are tokenized and sequentially concatenated as conditioning for the autoregressive model. We have revised Figure 2 to enhance clarity. We appreciate your input, which has strengthened our presentation.\n\n> Q2. Questionable baseline results: ...GLIDE and Blended Diffusion...\n\nThank you for your insightful question. These numbers are released along with MagicBrush paper (Table 3). \n\n[1] Chang, Huiwen, et al. Muse: Text-To-Image Generation via Masked Generative Transformers. ICML 2023.\n\n[2] Patil, Suraj, et al. amused: An open muse reproduction. arXiv 2024."}]

Author rebuttal:

Meta-review: This work introduces NEP (next editing token prediction), where only to-be-edited regions are regenerated, avoiding unintended modifications to other regions. To enable any region editing for autoregressive models, the authors pretrain a randomized autoregressive model for text-to-image. Promising results are demonstrated on MagicBrush and Emu Edit.

Initially, the reviewers raised several concerns, which are outlined below:

* Reviewer TJgQ: Lack of recent baselines for comparisons, reliance on synthetic training data, limited novelty.

* Reviewer Zgfx: Lack of other baselines, limited novelty, lack of discussion on computational overhead, writing issues.

* Reviewer 9n1p: Explanation on performance drop in Tab. 3, output orders.

* Reviewer KK48: Lack of other baselines, latency comparison, weak performance on free-form editing and test-time scaling, reliance on user-provided masks, lack of implementation details.

The rebuttal and subsequent author-reviewer discussions effectively addressed most of the reviewers' concerns. After carefully considering the reviews, rebuttal, and discussion, the AC concurs with the reviewers’ assessment and thus recommends acceptance of the paper. 

Additionally, the AC kindly notes that in addition to [52], the work RandAR [A] also enables any-region editing by training autoregressive models with random permutation. A proper discussion and citation of the related work would strengthen the revision. Finally, the authors are encouraged to incorporate the rebuttal experiments into the manuscript and address the reviewers’ feedback in the final revision.

[A] RandAR: Decoder-only Autoregressive Visual Generation in Random Orders. CVPR 2025.
Decision: Accept (poster)