RelationAdapter: Learning and Transferring Visual Relation with Diffusion Transformers
Authors: Yan Gong
Yiren Song
Yicheng Li
Chenglin Li
Yin Zhang
Venue: NeurIPS 2025 poster ()
Keywords: Diffusion model
In-context learning
Primary area: deep_learning

Abstract: Inspired by the in-context learning mechanism of large language models (LLMs), a new paradigm of generalizable visual prompt-based image editing is emerging. Existing single-reference methods typically focus on style or appearance adjustments and struggle with non-rigid transformations. To address these limitations, we propose leveraging source-target image pairs to extract and transfer content-aware editing intent to novel query images. To this end, we introduce RelationAdapter, a lightweight module that enables Diffusion Transformer (DiT) based models to effectively capture and apply visual transformations from minimal examples. We also introduce Relation252K, a comprehensive dataset comprising 218 diverse editing tasks, to evaluate model generalization and adaptability in visual prompt-driven scenarios. Experiments on Relation252K show that RelationAdapter significantly improves the model’s ability to understand and transfer editing intent, leading to notable gains in generation quality and overall editing performance.

RelationAdapter Learning and Transferring Visual Relation with Diffusion Transformers
Yan Gong'
Yiren Song² Yicheng Li' Chenglin Li'
Yin Zhang'
National University of Singapore {gongyan, yichengli, chenglinli, zhangyin98) edu. cn yirencnus edu.sg
'Zhejiang University
(a) Low Level Image Processing
(b) Style Transfer
Input (c) Image Editing
Result
Input
Result
(d) Customized Generation
Input
Result
Input
Result
Our framework, Relation A dapterr, effectively perform variety of image editing tasks by relying on exemplar image pairs and the original image. These tasks include (a) low-level editing, (b) style transfer, (c) image editing, and (d) customized generation.
Figure I:
Abstract
Inspired by the in-context learning mechanism of large language models (LLMs), new paradigm of generalizable visual prompt-base based image editing emerging. Existing single-reference methods typically focus on style appearance adjust- ments and struggle with non-rigid transformations. propose leveraging source-target image pairs to extract and transfer content-aware editing intent novel query images. To this end, we introduce Relation Adapter, lightweight module that enables Diffussin Transformer (DiT) based models effectively capture and apply visual transformations from minimal examples. We also introduce Relation252K. comprehensive dataset comprising 218 diverse edit- ing tasks, to evaluate model generalization and adaptability in visual prompt-driven scenarios. Experiments improves the model's 's ability to understand and transfer editing intent, leading to notable gains in generation quality and overall editing performance. Project page: Adapter
address these limitations, we
Relation252K show that RelationAdapter significantly
Introduction
Humans excel at learning from examples. When presented with just a single pair of images, compris- ing an original and its edited counterpart, we can intuitively infer the underlying transformation and
"Corresponding author: Yin Zhang.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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apply it to new, unseen instances. This paradigm, known as edit transfer or in-context visual learning 6, [58 64] provides an intuitive and data-efficient solution for building flexible visual editing that rely on textual prompts- whhhee ambiguity and limited expressiveness can hinder precision image pairs inherently encode rich, implicit visual semantics and transformation logic that are often difficult to articulate in language. By directly observing visual changes, models and users alike can grasp complex edits such as stylistic shifts, object modifications, or lighting adjustments with minimal supervision. As result, this paradigm offers a highly intuitive and generalizable modality for a wide range of image manipulation tasks, from creative design to personalized photo retouching.
systems. Unlike instruction-based editing methods [8 25 61
In-context learning- beanning-based based methods 6 23 28 58 64 have proven effective in extracting editing intent from image pairs. However. inputting image pairs into the model by concatenating them with the original image leads to several issues, including high memory consumption during inference and degraded performance of text prompts. To address these issues, we aim to develop dedicated bypass module that can efficiently extract and inject editing intent from example image pairs, thereby facilitating image editing tasks. Nevertheless, building scalable and general-purpos. framework for image-pair-driven editing still presents several fundamental challenges: (I) accurately extracting visual transformation signals from single image pair, including both semantic modifications (e.g.. object appearance, style) and structural changes (e.g., spatial layout, geometry); (2) effectively applying these transformations novel images while maintaining layout consistency and high visual fidelity; and (3) achieving strong generalization to unseen editing tasks such as new styles or unseen compositional without requiring retraining.
In this paper, we propose a unified framework composed of modular components that explicitly decouples the extraction of editing intent from the image generation process and enables more interpretable and controllable visual editing.
Our main contributions are summarized as follows:
First, we propose DelationAdapter the first DiT-based adapter module designed to extract visual transformations from paired images, enabling efficient conditional control for generat- ing high-quality images with limited training samples. A
adapter is designed to explicitly model and encode visual relationships between the pre-edit and post-edit images. It utilizes a shared vision encoder [40 65) (c.g., SigLIP) to extract visual features, subse- quently injecting these pairwise relational features into the Diffusion Transformer (DiT) (37 backbone to effectively capture and transfer complex edits. As a result, our framework robustly captures transferable edits across semantic, structural, and stylistic dimensions.
Second, We introduce In- In-Context Editor, consistency- framework for high-fidelity semantically aligned image editing with strong generalization to unseen tasks. It performs zero-shot image editing by integrating clean condition tokens with noisy query tokens. This mechanism enables the model to effectively align spatial structures and semantic intentions between the input and its edited version. key innovation introduced in this method is positional encoding cloning, which explicitly establishes spatial correspondence by replicating positional encodings from condition tokens to target tokens, thus ensuring precise alignment during the editing process.
Third, to facilitate robust generalization across a wide range of visual editing scenarios 48 we construct a large-scale dataset comprising 218 diverse editing tasks. These scenarios span from low-level image processing to high-level semantic modifications, user- customized generation, and style-uided transformations. The dataset consists of 33,274 image pairs, which we further perform permutation to obtain a total of 251,580 training instances. This extensive and heterogeneous dataset improves the model's generalization to unseen styles and edits. Furthermore, this dataset provides unified and scalable foundation for training and evaluating future image-pair editing models.
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2 Related Work
2.1 Diffusion Models
Diffusion models have emerged dominant paradigm for high-fidelity image generation 42 69
70], image editing[32] 72). video generation [50] 56) and other applications [9 47 53 54
Foundational works such as Denoising Diffusion Probabilistic Models [20] and Stable Diffusion 42
established the effectiveness of denoising- based iterative generation. Building on this foundation, methods like SDEdit (32) and DreamBooth [43] introduced pruucture-presevinng and personalized editing techniques. Recent advances have shifted from convolutional U-Net backbones to Transformer- based architectures, exemplified by Diffusion Transformers (DiT) [37 73 and FLUU [T]. DiT incorporates adaptive normalization and patch-wise attention to enhance global context modeling, while FLUX leverages large-scale training and flow-based objectives for improved sample fidelity and diversity. These developments signal a structural evolution in diffusion model design, paving the way for more controllable and scalable generation.
2.2 Controllable Generation
Controllability in diffusion models has attracted increasing attention, with various approaches en- abling conditional guidance. ControlNet [68). T2I-Adapter [3], and MasaCtrl [5] inject external conditions-suce edges, poses, or style cues cues-into pretrained models without altering base weights. These zero-shot or plug-and-play methods offer flexibility in structure-awar generation. In parallel, layout- and skeleton-guided frameworks such as GLIGEN (27 and HumanSD (24] enable high-level spatial control. genne-tuning-be learn attribute directions attention maps to enable consistent manipulations. In the of Diffu- sion Transformers, some methods concatenate condition tokens with denoised tokens and achieve controllable generation through bidirectional attention mechanisms causal attention mechanisms 52 [55. Despite their success, many of these methods rely on fixed condition formats
strategies, including Concept Sliders [5] and Finestyle [66),
16
22
49
51
require significant training overhead 30 46 60
2.3 Image Editing
Text-based and visual editing with diffusion models has seen rapid development. Prompt-to- -rrmmpt [18] and InstructPix2Pix allow fine-grained edits using prompt modifications or natural language instructions. Paint by Example [63] and LayerDiffusion [67] exploit visual references and layered generation to perform localized, high-quality edits. Versatile Diffusion
[2] supports joint condition- ing on text and image modalities, expanding the space of compositional control. Complementary to existing methods that often introduce substantial number of additional parameters, our proposed Relation provides a lightweight yet effective solution that leverages DiT's strong pretrained visual representation and structural modeling capacity, enabling few-shot generalization to novel and complex editing tasks. By injecting learned edit intent into DiT's attention layers, our method supports fine-gras grained structural control and robust style preservation.
3 Methods
In this section, we present the overall architecture of our proposed methods Scction 3.1 Next, Section 3.2 outlines our RelationAdapter module, which serves visual prompt mechanism to effectively guide image generation. We then integrate the In-Context Editor module (Section 3.3 by incorporating the Low-Rank Adaptation (LoRA) (2 fine-tuning technique into our framework. Finally, Section 3.4 presents a novel dataset of 218 in-context image editing tasks to support a comprehensive evaluation and future research.
3.1 Overall Architecture
As shown in Figure 2 our method consists of two main modules:
IalationAdapter RelationAdapter lightweight module built on the DiT architecture. By embedding novel attention processor in each DiT block, it captures visual transformations and

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Legends
Image Prompt oy"
Image Tokens eg Tokens oy:
"Appyy creyon drawing
the
Fyrcs
Image Takens og
Textt image.
Siglip
SipUip
Nolsed Latent Tokens
G Position Embedding
Frozen
RA Attention
MM Attention
DiT Block
Position Encoding
Ky Projector
[KV Projector
DiT Block
Chne
Relation Adapter
Ca-Coneext Editor
Figure 2: The overall architecture and training paradigm of RelationAdapter We employ the RelationAdapter to decouple inputs by injecting visual prompt features into the MMAttention module control the generation process. Meanwhile, high-rank LoRA used to train the In-Context Editor on a large-scale dataset. During inference, the In-Context Editor encodes the source image into conditional tokens, concatenates them with noise-added latent tokens, and directs the generation via the MMAttention module.
injects them into the hidden states. This enhances the model's relational reasoning over image pairs without modifying the core DiT structure.
In-Context Editor.
In-Context Editor frames image editing as conditional generation task during training. It jointly encodes the images and textual description, enabling bidirectional attention between the denoising and input branches. This facilitates precise, instruction-driver editing while preserving the pre-trained DiT architecture for compatibility and efficiency.
3.2
RelationAAdaper
Our method can be formulated function that maps set of multimodal inputs, namely, a visual prompt image pair (T ppm, Trer), source image snc and a textual prompt 7 pmm to post-edited image as target image T
Inr E(pmmy Iief, Juc, T Tmm) D (R(Ipm, Trer), Isc, T Tpm)
where 'D denotes the Diffusion Transformer, and 'R refers to the RelationAdapter module integrated into the Transformer encoder blocks of the DiT architecture.
Image Encoder. Most personalized generation methods use CLIP 40
an image encoder, but its limited ability to preserve fine-grained visual details hinders high-fidelity customization. overcome this, we adopt the fidelity in extracting visual prompt features from paired visual prompts 4pmm and Let. Let Cp and eR denote the representations of the sequence of features of 4pm and Lets respectively. The visual prompt representation Cy is constructed by concatenating Cp and CR.
14-384
(55 model for its superior semantic
Revisiting Visual Prompt Integration. To enhance the representational flexibility of the DiT based model, we revisit the current mainstream image prompt based approaches (c.g., FLUX.1 Redux [3), which directly appends visual features to the output of the T5 encoder B1).
Given the visual prompt features cy and the backbone DiT input features Cp. FLUX.1 Redux applies bidirectional self-attention mechanism over the concatenated feature sequence. The resulting attention output Z' computed as:
QK vd
Z' Attention Attention(Q,.E. K, V) = Softmax
V
(2)

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Q - C Wa,
c Way V= W
(3)
and CB.V denotes the concatenation of backbone DiT imput features eg and visual features cy.
Decoupled Attention Injection. A key limitation of current approaches is that visual prompts cy are typically much longer than textual prompts CT, which can weaken or even nullify text-based guidance. We design separate key-value (KV) attention projection mechanism, W and W for the visual prompts. Crucially, the cross-attention layer for visual prompts shares the same query Q with the backbone DiT branch:
Q(K)T T vd
Zy Attention Q K', V' Softmax
V
Q = CB W K' cy W
V cy
(5)
Then, we fuse the visual attention output Zy (from the RelationAdapter) with the original DiT attention output nn before passing it to the Output Projection module:
Znew = ZB + a Zv
(6)
where a is tunable scalar coefficient that controls the influence of visual prompt attention.
3.3 In-Context Editor
In-Context Editor builds upon a DiT-based pretrained architecture, extending it into a robust in- context image editing framework. Both the source image Lare and the target image 4ur are encoded into latent representations, and respectively, via Variational Autoencoder (VAE) [26). After cloning positional encodings, the latent tokens are concatenated along the sequence dimension to enable Multi-modal Attention [3)], formulated as:
QK Vd
MMA ([25(2205t ([25
orl) softmax
V
Here, Z = [z; C5s er] denotes the concatenation of noisy latent tokens z, source image tokens cs. and text tokens er. where is obtained by adding noises target image tokens.
Position Encoding Cloning. Conventional conditional image editing models often struggle with pixel-level misalignment between source and target images, leading to structural distortions. To address this, we propose Position Encoding Cloning strategy that explicitly embeds latent spatial correspondences into the generative process. Specifically, we enforce alignment between the posi- tional encodings of the source condition representation es and the noise variable z, establishing consistent pixel-wise coordinate mapping throughout the diffusion process. By sharing positional encodings across key components, our approach provides robust spatial guidance, mitigating artifacts such as ghosting and misplacement. This enables the DIT to more effectively learn fine-grad -naiiid correspondences, resulting in improved editing fidelity and greater theoretical consistency.
LoRA Fine-Tuning. To enhance the editing capabilities and adaptability of our framework to diverse data, we constructed context learning- editing dataset comprising 25 251,580 ,880 samples (see Section 3.4. We then applied LoRA fine-tuning to the DiT module for parameter-efie -ffficieet adaptation. Specifically, we employed high-rank LoRA by freezing the pre-trained weights Wo and injecting trainable low-rank matrices A E gr* and B E Rdxr into each model layer.
Noise-Free Paradigm for Conditional Image Features. Existing In-Context Editor frameworks concatenate the latent representations of source and target images input to step- wise denoising process. However, this often disrupts the source features, causing detail loss and reduced pixel fidelity. To address this, we propose noise-free paradigm that preserves the source features es from I throughout all denoising stages. By maintaining these features clean state, we provide stable and accurate reference for generating the target image Jur Combined with position encoding cloning and a Multi-scale Modulation Attention (MMA) mechanism, this design enables precise, localized edits while minimizing unintended modifications.
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3,4 Relation252K Dataset
We curate large-scale image editing dataset encompassing 218 diverse tasks, categorized into four main groups based on functional characteristics: Low-Level Image Processing, Image Style Transfer, Image Editing, and Customized Generation. The dataset contains 33,274 images and 251,580 editing samples generated through image pair permutations. Figure 3 provides an overview of four task categories. We open-source the full dataset to encourage widespread usage and further research in this field.
Transfer
You odit There (left and right) Please describe what the left and
Depth
Surface Normal
Organi Effect
Gogh Style
Numooval
Weodoot
GPT-40
Unlabeled Image Pair
Source Caption: A person wearing a white with rolled- up sleeves, posing casually.
Image Editting
Customized Generation
Cooe byea
Add Object
Form
20 Phototrame
Target Caption: A
wearing white shirt with
buttoned sleeves against red background.
Edit Instruction: Apply an ID photo transformation to the portrait, front-facing, red background.
Overview of four main task cate- gories in our dataset. Each block lists represen- tative sub-tasks (with ellipses indicating more), along with image-pair examples.
Figure 3:
Figure 4: Overview of the annotation pipeline using GPT-4o. GPT-4o generates a set of source caption, target caption, and edit instruction de- scribing the transformation from T to har-
Automatic Image Pairs Generation. We introduce semi-automated pipeline for constructing high-quality dataset. A custom script interfaces with Discord bot to send /imagine commands to MidJourney, generating high-fidelity images. Also using the GPT-4o 335 generate context-awart images from original inputs and edits. For lewelevel tasks, we additionally through manual
multimodal API, we
curate a subset of well-known benchmark datasets I0 13
39 44
collection to ensure coverage of classic image processing scenarios. Furthermore, part of our original dataset is derived from several existing facial and human image collections, including [T] 12 29 45
To improve annotation efficiency and scalability, we leverage the multimodal capabilities of GPT-4o to automatically generate image captions and editing instructions. Specifically, we concatenate the source image (Tae) and the corresponding edited image (Tur) as joint input to the GPT-4o API. A structured prompt guides the model to produce three outputs: (1) a concise description of L (2) a concise description of I and (3) a human-readable editing instruction describing the transformation from Sre to har An example illustrating the pipeline shown Figure 4 To conform with the model's 's input specification, image pairs sampled and arranged via rotational permutation, with up to 2,000 instances selected per task to ensure distributional balance. each sample, the upper half is used visual context for the RelationAdapter, and the lower half is input to the In In-Context Editor module. Directional editing instruction (Iare + Tux) are provided solely as text prompt, without detailed content descriptions.
Experiments
4.1
Settings
We initialize our model with FLUX. I-dev (2) within the DiT architecture in training. To reduce computational overhead while retaining the pretrained model's generalization, we fine-tune the In-Context Editor using LoRA, with rank of 128. Training spans 100,000 iterations on 4 H20 GPUs, with an accumulated batch size of 4. We use the Adam optimizer and 6 mixed-precision training, with an initial learning rate of x 10-4, The total number of trainable parameters is 1,569.76 million. Training takes 48 hours and consumes 74 GB of GPU memory. At inference, the model requires 40 GB of GPU memory on single H20 GPU. The RelationAdapter employs dual-branch IigL.IP visual encoder, where each branch independently processes one image from the input pair and outputs 128-dh 28-dimensional feature token via two-layer linear projection network. The attention

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fusion coefficient is fixed to 1. To balance computational efficiency, input images are resized, maintaining their aspect ratio, such that the longer side does not exceed 512 pixels prior encoding.
4.2
Benchmark
We selected 2.6% of the dataset (6,540 samples) benchmark subset, covering diverse range of 218 tasks. Among these, 6,240 samples correspond to tasks seen during training, while 300 represent unseen tasks used to evaluate the model's generalization capability.
4.3 Baseline Methods
To assess the performance of our method, we compare it against two baselines: Edit Transfer [6] and VisualCloze (8). Both baselines follow in-context learning setup and are evaluated within the shared training task space to ensure fair comparison, using the official implementation and recommended hyperparameters to ensure reproducibility.
4.4 Evaluation Metrics
We evaluate model performance using five key metrics: Mean Squared Error (MSE), CLIP-based Image-to-Image Similarity (CLIP-I), Fréchet Inception Distance (FID), Editing Consistency (GPT-C), and Editing Accuracy (GPT-A). MSE [59] quantifies low-level pixel-wise differences between the generated and foound-truthe images. To capture perceptual and semantic fidelity, we employ both CLIP-I 41 and FID 19]. CLIP-I measures high-level semantic similarity by computing the cosine distance between CLIP embeddings of generated and reference images, while FID evaluates the overall realism and distributional aligmment of generated images with real ones in the feature space of pretrained Inception network, where a lower value indicates higher visual quality. To further assess editing quality from a human-centered perspective, we leverage GPT-4o to interpret the intended transformation from the prompt image [prm to the reference image reef, and evaluate the predictions based on two dimensions: Editing Consistency (GPT-C), which measures alignment with the source image Lare. and Editing Accuracy (GPT-A), which assesses how faithfully the generated image reflects the intended edit.
4.5 Comparison and Evaluation
Quantitative Evaluation. As shown Table I our method consistently outperforms the baselines in MSE, CLIP-I, and FID metries. Compared to Edit Transfer, our model achieves significantly lower MSE (0.020 vs. 0.043), a higher CLIP-I score (0.905 vs. 0.827), and a reduced FID (4.201 vs. 4.908), indicating better pixel-level accuracy, semantic consistency, and overall visual quality. Similarly, when compared with VisualCloze, our method achieves a notable improvement, reducing the MSE from 0.049 0.025, boosting CLIP-I from 0.802 to 0.894, and lowering FID from 7.218 to 4.801. These results demonstrate the effectiveness of our approach in producing both visually accurate and semantically meaningful image edits. Our method also consistently outperforms two state-of-the-art baselines in GPT-C and GPT-A metrics.
Qualitative Evaluation. As shown in Figure 5 our method demonstrates strong editing consistency and accuracy in both seen and unseen tasks. Notably, in the unseen task of adding glasses to person, our approach even outperforms Edit Transfer, which was explicitly trained on this task. In contrast, Edit Transfer shows instability in low-level color control (e.g., clothing color degradation). Compared VisualCloze, our method is less affected by the reference image Lust especially in tasks like depth prediction and clothes try-on. VisualCloze tends to overly rely on Ine reducing transfer accuracy, while our method more reliably extracts key editing features, enabling stable transfer. On unseen tasks, VisualCloze often shows inconsistent edits, such as foreground or background shifts. Our method better preserves structural consistency. This may be due to VisualCloze's bidirectional attention causing feature spillover. Although our method retains some original color in style transfer, it produces more coherent edits overall, indicating room to further improve generalization.
4.6 Ablation Study
To assess the effectiveness of our proposed Relation Adapter module, we conducted an ablation study by directly concatenating the visual prompt features with the condition tokens es. For fair comparison, this baseline was trained for 100,000 steps, identical to RelationAdapter. As shown in
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2 our model consistently outperforms the in-context learning baseline across all five evaluation metries on both seen and unseen tasks. This improvement attributed the RelationAdapter, which enhances performance by decoupling visual features and reducing redundancy.
Seen Tasks
Unseen Tasks
Input
Transler
Edit
Inout images
Durr
Edit Trensfer
Input images
Cura
Trander
Edit
Input mages
VisulClooe
Input Images
Durs
Viscal Clane
Input Images
Dus
VisalClore
Figure 5: Compared to baselines, Relation demonstrates outstanding crrruuctionfoolowiin ability, image consistency, and editing effectiveness on both seen and unseen tasks.
Table I: Quantitative Comparison of Baseline Methods Trained on Common Task (ET: Edit Transfer, VC: VisualCloze). The best results are denoted as Bold.
Table 2: Ablation Study the Effectiveness of the RelationAdapter(RRA, in Seen and Unseen Tasks (-S for Seen, -U for Unseen). The best results are denoted as Bold.
Method
MSE J.LIP FID GPP. GPTA 1
Method
MSE LCUP. PTD JGPTC GPTA
EditTransfer
0.043
0.827
4.908
4.234
3,508
wlo RA -
0.055
0.787
5.968
3.909
3.597
Ours n ET
0.020
2.201
4.437
Ours
0.044
5.191
4.079
VisualCloze
0.049
0.802
7.218
3.594
3.411
we RA
0.061
0,778
5.571
3.840
3.566
Ours ( VC
0.025
0.894
4.801
4.084
3.918
Ours
0.053
0.812
5.498
4.187
4.173
Although latent-space concatenation (i.e., directly merging four input images before VAE encoding) effective, imposes a considerable computational burden during inference. This limitation restricts the resolution of generated images and compromises fine-grained details during inference. In contrast, our lightweight RelationAdappl and apply the semantic intent of editing instructions with minimal computational cost. Figure demonstrates that our approach yields higher editing accuracy and consistency in both task settings.
provides a more efficient alternative, enabling the model to capture
Seen Tasks
Unseen Tasks
Subhica
Lewering
Context
Impurt
Leaaming
Contort
Cur
Context Learnnng
Figure 6: Ablation study results. Our strategy shows better editorial consistency.

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4.7 User Study
We conducted user study to evaluate our method. Thirty volunteers were recruited to complete assessment questionnaires. In each task, participants were presented with a pair of task prompt images (representing the intended edit), one source image, and two edited results: one generated by our proposed method and the other by baseline method. For the in-context learning baseline, we used the model variant from our ablation study with the RelationAdapter module removed. All images were randomly sampled to ensure fairness across tasks. To mitigate potential bias, the order of the two edited images was randomized for each task.
Participants were instructed to interpret the intended transformation from the prompt pair and answer the following three questions:
1. Edit Accuracy: Which image better aligns with the editing intent implied by the prompt pair?
2. Edit Consistency: Which image better preserves the structure and identity of the source image?
3. Overall Preference: Which image do you prefer overall?
The aggregated results of the user study are summarized in Figure 7 When compared with an in- context learning-based method, our approach was preferred for tasks included in training in 73.19% of cases for Edit Accuracy, 80.08% for Edit Consistency, and 79.58% % for Overall Preference. Even on tasks unseen during training, users still favored our method in 57.67 % 57.00% % and 66.33% of cases, respectively. We also conducted comparisons against other representative baselines. Against VisualCloze, our method was preferred in 70.98% % of cases for Edit Accuracy, 72.55% % for Edit Consistency, and 69.22% % for Overall Preference. When compared to Edit Transfer, the preference gap widened further, with our method selected 97.11 cases Edit Consistency, and 75.78 % for Overall Preference.
cases for Edit Accuracy, 78.89 % for
(o) Compa fson with In contest (Seen)
(b) Comparison with in-contest (Unscen)
1cl Comparison with VisualCloze
[d] Comparison with Edit Transfer
100%
100%
100%
180%
75%
75%
75%
75%
90%
50%
50%
50%
25%
25%
Bsseline
Gurs
Figure 7: User study results comparing our method with baselines (in-context learning, VisualCloze and Edit Transfer) across evaluation criteria: edit accuracy, edit consistency, and overall preference.
Discussion
As shown in Figure g Relation Adapter demonstrates superior performance in various image editing tasks. This performance can be attributed to the integration of a lightweight module that performs weighted fusion with attention, leading to more precise edits. Notably, this suggests that leveraging visual prompt can be effectively decoupled from conditional generation through attention fusion, without the need for full bidirectional self-attention. This finding reveals a promising direction for designing more efficient and scalable editing models.
Table Quantitative comparison of evaluation metrics (mean + std) across four image generation lasks. Best results are shown in bold.
Tasks
MSE 1
CLIP-1
GPT-C1
GPT-A f
Low-Level (n=32)
0.028 + 0.038
0.885 + 0.067
3.943 + 0,383
3.822 + 0.406
Style Transfer (n=84)
0.051 + 0.032
0.846 + 0.036
4.077 + 0.198
4.246 + 0.285
Image Editing (n=63)
0.031 + 0.023
0.861
0.055
4.173 0.229
4.100 + 0.400
Customized Generation (n=39)
0.065 + 0.048
0.816 = 0.073
4.071 = 0.224
4.064 + 0.313
We evaluated RelationAdapter on four classification tasks of varying complexity. As shown in Table it excels in complex tasks like style transfer and customized generation, showing strong semantic

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aligmment and text-image consistency. In editing tasks, it balances reconstruction and semantics well. While GPT scores slightly drop in low-level tasks, further low-level evaluations (see supplementary materials
B.3
provide more complete assessment.
Seen Tasks
Unseen Tasks
MALe
Input Images
Input Images
Input Images
The generated results of Relation mations in example image editing pairs and apply them to the original image to achieve high-quality image editing. It demonstrates a certain level of generalization capability on unseen tasks.
Figure 8:
RelationAAaptte understand the transfor-
Limitation
Although our model performs well across various editing tasks, it sometimes fails to accurately render text details in the generated images. This is common problem with current Diffusion models. In addition, the model may perform slightly differently or previously unseen tasks, suggesting that it is sensitive to task-specific nuances.
7 Conclusion
In this work, we propose RelationAdapter, a novel visual prompt editing framework based on DIT, which strikes a previously unattained balance between efficiency and precision. We begin by revisiting the limitations of existing in-context learning approaches and introduce a decoupled strategy for re-injecting visual prompt features. Leveraging the inherent editing capabilities of DIT, our method enhances both the stability and the generative quality of the model in the in-context learning scenarios. To support prompt-based editing tasks. We further introduce two training sonddiis-possttonn encoding cloning and noise-free conditioning scheme for In-Context Editor, which significantly improve the model's editing capability. Extensive experiments validate the effectiveness of our method and demonstrate its superior performance across diverse editing scenarios. We believe this efficient and accurate framework offers new insights into visual prompt-based image editing and lays the groundwork for future research.
approach, construct a large-scale dataset comprising 218 visual
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Acknowledgement
This work was supported by the Zhejiang Provincial Natural Science Foundation of China under Grant No. LZ23F020009, the NSFC project (No. 62072399), the Fundamental Research Funds for the Central Universities (No. S20240030), MoE Engineering Research Center of Digital Library, China Research Centre Data and Knowledge for Engineering Sciences and Technology.
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NeurIPS Paper Checklist
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Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
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Answer:
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Answer: [No]
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The authors should cite the original paper that produced the code package or dataset.
authors should state which version
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13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
Justification: We introduce Relation252K, a new dataset for visual prompt-based editing, and release accompanying code for the Relation Adapter framework. All assets documented with details on data construction, licensing, usage instructions, and limitations. Documentation is included in the supplemental material and will be provided alongside released assets.
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Appendices
The Appendices provide comprehensive overview of the experimental framework used to develop and evaluate our method. It includes implementation details (Section A), comparisons with baselines (Section [B) failure case analysis (Section F), user study design (Section 4.7). and additional results (Section
A
Implementation Details
A.1 Data Annotation
We leverage the multimodal capabilities of GPT-40 automatically generate image captions and editing instructions. Specifically. we concatenate the source image lue and the corresponding target image lur as a single input to the GPT-4o API. A structured text prompt illlsstrate in Figure 9 is provided to guide the model in producing three outputs: a concise caption for Tare' a concise caption for Tur human-readabl instruction describing the transformation from Tane Tu Notably, the editing instruction is provided solely textual form, without detailed descriptions image content.
A.2 Inference Details
During inference, we set the guidance, _aallee__eeleeeeeeeeeeee_eee to 3.5, the number of denoising steps to 24, and the attention fusion weight a to 1.0. A fixed random seed of 1000 was used to ensure reproducibility.
B Details of Comparisons with Baselines
B.1 Baseline and Ablation Study Settings
We adopt the official implementations and default configurations for both VisualCloze and Edit Transfer. During inference, since VisualCloze supports layout prompts. specify the layout as: "4 images are organized into grid of 2 rows and 2 columns." Before concatenating the images into the grid layout, each individual image resized to square region with area 512 512 pixels ensure consistent resolution and layout compatibility. We fix the random seed to 1000 and use the default 30 denoising steps. For Edit Transfer, we similarly set the random seed to 1000, while keeping all other parameters at their default values.
In the ablation study, we remove all components related to the Relation dapter module and directly feed the prompt image I and the reference image Jut into the In-Context Editor. Additionally, we apply Position Encoding Cloning each input image retain spatial correspondence. All other configurations kept unchanged to ensure fair comparison.
B.2 Evaluation Details
We leverage the multimodal reasoning capabilitics GPT-4o to interpret the intended transformation from the prompt image Jpo to the reference image Tret, and evaluate model predictions from a human-centered perspective along two key dimensions: Editing Consistency (GPT-C) and Editing Accuracy (GPT-A).
To facilitate this evaluation, construct composite inputs consisting of five concatenated images: the prompt image Iprm the reference image Irer (representing the desired attribute change), the source image Jures and two generated results fpred, and Ipredy. GPT-4o is then prompted to interpret the intended edit and assess each prediction based on the above criteria. The specific text prompt provided to GPT-4o is illustrated in Figure TO
B.3 Perceptual Capability Evaluation
We evaluate the model's 's perceptual capability across a series of low-level image editing tasks, including depth estimation, surface normal prediction, edge detection, and semantic segmentation. We further compare its performance against the current -tate-of-the-art general-purpose image generation framework, VisualCloze, using multiple evaluation metrics. Detailed results are provided in Tables and
B.4 Additional Explanation on Baseline Selection
RelationAdapter is designed around a unique before-after pair formulation, in which the model learns visual transformations directly from exemplar pairs. Among existing approaches, only Edit Transfer (6) and Visual- Cloze (28) share this paired-context setup, making them the most appropriate baselines for direct comparison.
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Table 4: Edge detection perfor- mance on the BSDS500 dataset.
Table 5: Segmentation perfor- mance on the COCO dataset.
Table 6: Depth estimation (d1) on multiple datasets.
Metric
VisualCloze
Ours
Metric
VisualCloze
Ours
Dataset
VisualCloze
Ours
Procision f
0.3476
0.2266
Pixel Acc. 1
0.7817
0,7810
BSDS500
0.1492
0.1833
Recall
0.0817
Acc. f
0.3959
COCO
0.1515
0.1750
Fi-score 0.1227
0.2150
Mean IoU f
0.3143
0,3642
0.2954
Table Surface normal estimation results. Lower error and higher accuracy indicate better perfor- mance. Mean/Median Angular Error measure deviation from ground truth ("), while Accuracy @ X reports the percentage of predictions within degrees. Best results are highlighted in bold.
Metric/ / Dataset
BSDS500
COCO
BIPED
NYUv2
VisualCloze
Ours
VisualCloze
Ours
VisualCloze
Ours
VisualCloze
Ours
Mean Angular Eror (")
$2.29
27.15
63.30
35.62
$3.15
29.83
43.19
31.69
Median Angular Error
49.15
24.39
61.00
32.66
51.01
28.39
37.78
28.30
Accuracy (<11 (<11.25")
6.76
16.12
2.21
11.90
4.09
12.28
4.47
11.46
Accuracy (<22.5")
22.10
47.89
9.59
36.30
15.06
38.57
20.74
37.33
Accuracy (<30%)
33.52
65.88
18.13
51.68
25.56
54.87
36.38
53.75
This task formulation distinguishes our method from broad range of existing image editing and generation frameworks.
Methods such Prompt-to-Prompt [8] and RF-Edit [5 operate purely in the text-driven editing paradigm without utilizing visual exemplars, and therefore cannot model transformation relationships between images. Zero-shot Image Editing (7) and OminiControl (55) focus on reference-conditionee generation, where auxiliary visual signals such as edge maps, depth maps, or segmentation masks are used to guide image synthesis. Their goal is to apply pre-defined visual conditions rather than learn transferable transformation mappings. UniReal 8 addresses multi-image compositional generation task, e.g., combining the subject from image with the background of another under mask guidance, which fundamentally differs from exemplar-based transformation learning.
In contrast to the above methods, RelationAdapter learns to infer the transformation itself from paired visual exemplars, enabling the transfer of edit intent to unseen content domains. This formulation requires both source and edited target image context, providing explicit supervision for relational transformation understanding.
B.5 Comparison with Midjourney
Although Midjourney (MJ) represents a strong general- purpose image generation system, it does not support pairwise or multi multi-image conditioning for transformation based editing. Its interface only distinguishes between character reference (-cref) and style reference (-sref). without the capability to process relational transformations hetween visual exemplars. In contrast, RelationAddaterr interprets exemplar pair as direct demonstration the intended visual change, which constitutes a distinct learning paradigm.
completeness, we evaluated Midjourney by assigning the source image of each exemplar pair as the -cref and the edited image as the -aref, using the following standardized prompt format:
[text prompt] --cref <source image> --aref <edited image> --cw 90 --sw 70 --v 6.
reports the quantitative comparison on unseen style transfer tasks. Despite Midjourney's 's strong generative priors, RelationAdapter consistently achieves superior performance across all evaluation metries, indicating better perceptual consistency and transformation fidelity.
Table 8: Comparison of RelationAdapter and Midjourney (MJ) on unseen style transfer tasks. The best results are denoted in bold.
Method
MSE
CLIP-11
FID
GPT-C1
GPT-A 1
MJ
0.107
0.681
5.836
3.285 4.203
3.200
Ours
0.062
0.774
5.715
4.278
C Advantage over the In-Context Based Variant
We analyze the effectiveness and efficiency gains of RelationAdapter over the in in-context variant. The in- context method required approximately 77 GB of GPU memory and 51.5 hours of training time, whereas
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RelationAdapter used around 74 GB of memory and completed training in about 48 hours, corresponding to memory saving of roughly GB and 6.8% reduction in total training time. For inference, editing single image at resolution 1024 x 1024 took over seconds required less than seconds, achieving a 30.8% speed-up. These improvements stem from crucial architectural distinction: the in-context approach concatenates all tokens from exemplar and target contexts, leading to increased attention computation and slower inference, whereas RelationAdapter employs decoupled attention efficiently. Moreover, the same mechanism contributes to the
in-context method, while RelationAdapter
mechanism that processes and fuses them observed gains editing accuracy and consistency by preventing feature contamination between exemplar pair and target image, enabling more targeted and coherent transformations. Both quantitative evaluations (Table 2 and qualitative visualizations (Figurefo) consistently confirm that Relation achieves superior perceptual fidelity and structural consistency while offering notable improvements in memory efffccieccy and processing speed.
D Effect of Attention Fusion Coefficient and Visual Encoder Choice
attention fusion coefficient a controls relative contribution hetween the visual prompt attention generated by the IllationAdapte and the base Multi-Modal Attention (MMA) within the Diffusion Transformer. As during training maintain balanced integration hetween visual guidance
specified in Section 3.2] we set and generative consistency, and adopt the same value during inference for training dedd--deepeeent consistency. To further assess its influence, we varied a across (0.5, 1,2) and report the results Table The results indicate that maintaining a = yields the most stable and optimal generation performance across both seen (-S) and unseon (-U) tasks, while deviating from this setting slightly degrades fidelity and consistency.
Table 9: Effect of adjusting the attention fusion coefficient a on image editing quality. "-S" and "-U" denote seen and unseen tasks, respectively.
Method a 2-S
MSE
CLIP-1
FID
GPT-C
GPT-A 1
0.044
0.827
5,564
3.855
3.536
a = 1-S
0.044
0.852
5.191
4.115
4.258
a 0.5-S
0.050
0.832
5.895
4.099
3.591
a 2-U
0.054
0.794
5,805
3.858
3.527
Q 1-U
0.053
0.812
5.498
4.211
4.377
Q 0.5-U
0.056
0.808
5.724
4.149
3.620
E
Effect of Model Size and Low-Rank Configuration
To assess the impact of model size on performance, we conducted an additional experiment using lower-rank configuration in the LoRA modules. The numher of trainable LoRA parameters decreases from 358.6M to configurrie
44.8M when reducing the rank from 128 16, corresponding As shown Table I0 the proposed method remains robust effective under this lightweight configuration. exhibiting only marginal performance degradation.
87.5% reduction in trainable parameters.
Table 10: Comparison between LoRA rank 16 and the original configuration. "-S" and "_U" denote seen and unseen tasks, respectively.
Method
MSE
CLIP-11
FID 1
GPT-C1
GPT-A 1
Ours-S 16)nk=16)
0.048
0.828
5.757
4.035
3,607
Ours-S
0.044
0.852
5.191
4.145
4.219
Ours - (Rank=16)
0.064
0.792
5.924
4.026
3.505
Ours
0.053
0.812
5.498
4.195
4.239
F Failure Cases
Figure II illustrates set of challenging editing tasks. While the model successfully captures edit intentions in several cases, struggles with fine-gran grained spatial alignment and the restoration of detailed textual elements. future solution could involve training on risoeut-rsolution data to better capture spatial nuances.
G
Additional Results
As shown Figures 12
13 and 14 our method demonstrates strong performance across diverse editing tasks, effectively handling spanial transformatinn and capturing complex semantic modifications with high fidelity.
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Text Prompts
This is side-by-side comparison image (left and right). Please describe what you see on the left and right side respectively, and provide a transformation or edit instruction from left to right. Return only JSON object with the following fields:
"left_image leatt__maaedeeerriipeee deserripisirnnn
2. 'right_imen limmaadddscriptiioo cdit_instruction
Do not include any other text explanation.
Figure 9: Structured prompt used for labeling image pairs and extracting transformation instructions.
Text Prompts
You are given composite image with two columns. The left column contains three images arranged vertically: Left Column: A (original image), A1 (edited version of A), B (another original image).
The right column contains two images: B1 and B2, which are two independently edited versions of image B.
Your task is independently score B1 and B2 based
dimensions:
1. Edit Consistency (1-5): How visually consistent is the edited image (B1 or B2) with the original image B? Focus: Are key objects, colors, and structures consistent with the source?
2. Edit Accuracy (1-5): Assess how accurately the editing operation applied (to produce
B1 or B2) mirrors the transformation seen from A - A1. Focus: Did the editor apply similar changes, in the correct location, with the same degree of modification?
Avoid giving tied scores unless B1 and B2 are truly indistinguishable. Ensure scores reflect nuanced differences in both consistency and accuracy between B1 and B2. Be critical. Reserve scores of 4-5 for highly consistent/accurat edits. Be objective and concise in your assessment.
Return your answer in the following JSON format: ( "B1": "=ccura "B2": ["consistene""><155 "accuracy":<1-551
("consistency::<1555>
Figure 10: Evaluation prompt used to assess edit consistency and accuracy between two generated outputs, leveraging GPT-4o for interpretation and scoring.
Input
Result
Input
Result
Figure 11: Failure cases gesture editing, background pedestrian removal. document rectification, and image-to-sken sketch conversion. The model shows partial success with room for improvement.

---

Input
Result
Input
Result
Figure 12: Additional experimental results of RelationAdapter.
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MASTER OATS
BLUE
BOY
Input
Result
Input
Result
Figure 13: Additional experimental results of RelationAdapter.
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IBM IBM
24 HOUR
24 HOUR
Input
Result
Input
Result
Figure 14: Additional experimental results of RelationAdapter.
27


Reviews:
[{"review_id": "owgkcA4rqK", "reviewer": "Reviewer_cG9Q", "summary": "This paper proposes an exemplar-based image editing method capable of performing low-level editing, style transfer, customized generation, and other diverse tasks, with good generalization capability. The proposed RelationAdapter leverages a frozen image encoder to extract visual features that capture the relation between the source and target, injecting them into the diffusion process in an IP-Adapter fashion. Compared to existing in-context learning methods, this approach reduces GPU memory consumption and achieves a higher editing success rate. Strategies such as position encoding cloning and noise-free conditioning are applied to improve performance. The authors also construct a comprehensive dataset, Relation252K, for benchmarking. According to quantitative and qualitative results, RelationAdapter demonstrates superior performance on both seen and unseen tasks.", "questions": "1. The proposed dataset seems to be well-designed. However, a similar dataset Graph200K was proposed in VisualCloze. Why is this dataset not used for training and benchmarking?\n\n2. The ablation study and comparison experiments should be extended, according to the reasons stated  in 'Weakness'.\n\n3. What is the actual advantage (in terms of effectiveness and efficiency) of the proposed method compared to the in-context based variant? Please give further discussion of the claimed 'higher editing accuracy and consistency' including possible reasons behind this improvement, and add quantitative numbers of memory usage savings.\n\n4. Position encoding cloning is clearly effective for pixel-wise aligned editing tasks. However, for other tasks like customized generation, is this strategy still reasonable? This problem seems to have been discussed in previous works [1], please give more explanations.\n\n[1] OminiControl: Minimal and Universal Control for Diffusion Transformer", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "- Strengths:\n\n1. The proposed RelationAdapter pipeline is reasonably designed and clearly explained. Compared with in-context learning-based methods, explicitly extracting visual features with pre-trained VLM does reduces memory consumption in diffusion models and may lead to better semantic understanding in the editing task.\n\n2. The qualitative and quantitative results looks promising. \n\n3. The proposed benchmark dataset iswell-constructed, with diverse editing tasks ranging from pixelwise-aligned depth estimation to free-formed customized generation. High-quality datasets are a valuable contribution to the community.\n\n\n- Weakness:\n\n1. The ablation study is not comprehensive enough. The effectiveness of key designs such as position encoding cloning and noise-free conditioning are not verified.\n\n2. There are only two comparison methods. More comparisons are expected, including MidJourney (since the dataset is constructed with MidJourney, I am curious if the proposed method generate better results than the data source.)\n\n3. Although the approach is sound, the advantage of using adapter-based approach over in-context-based approach is not well demonstrated. In Figure 6, the results is only slightly better than the in-context learning ablation variant. Also, the claimed reduction in memory consumption is not provided in numbers.\n\n4. Some of the proposed strategies are debatable. For instance, position encoding cloning may not be suitable for tasks that are not pixel-wise aligned.", "was_revised": true, "final_justification": "As the rebuttal addressed most of my concerns, I will keep a positive view of the paper and raise my score to accept.", "rebuttal": "## 1. The proposed dataset seems to be well-designed. However, a similar dataset Graph200K was proposed in VisualCloze. Why is this dataset not used for training and benchmarking?\n\nWe sincerely thank the reviewer for recognizing the design quality of our proposed dataset. While Graph200K introduced in VisualCloze [1] is indeed a valuable contribution, we would like to clarify that only a small fraction of its tasks align with the core setting of our work— relation-based image editing from example pairs. The vast majority of Graph200K samples are not directly compatible with the visual relation transfer paradigm we target.\n\nMoreover, incorporating Graph200K into our training pipeline would cause a significant imbalance in task distribution due to the limited number of applicable samples per task. In contrast, our Relation252K dataset guarantees at least 210 samples per task across 218 distinct editing tasks.\n\nLastly, since there is partial task overlap between Relation252K and Graph200K, we opted not to include Graph200K in additional benchmarks to avoid redundant evaluation and ensure clarity in performance attribution.\n\n## 2. The ablation study and comparison experiments should be extended, according to the reasons stated in 'Weakness'.\n\nWe agree that broader baseline comparisons are beneficial. However, it is worth noting that RelationAdapter uniquely requires a complete 'before-after' pair as context—a formulation shared only with Edit Transfer [10] and VisualCloze [1]. We will clarify this distinction in the final version. Another reviewer referred to several related studies, which we further differentiate from RelationAdapter below:\n\nPrompt-to-Prompt [2] and RF-Edit [3] operate entirely in the text-driven editing paradigm. They do not utilize any visual examples and thus lack the capacity to learn visual editing transformations from exemplar pairs. Zero-shot Image Editing [4] and OminiControl [5] focus primarily on reference-conditioned generation, where an auxiliary image—typically a Canny edge map, depth map, segmentation mask, or color scribble—is used to provide fine-grained control signals during image generation. The core idea is to apply a predefined image condition to guide image generation, rather than to learn and transfer a transformation between image pairs. UniReal [6] addresses a different task setting—combining multiple conditioning images (e.g., “placing the cat from IMG1 onto the bed in IMG2, guided by a mask in IMG3”). \n\nIn the 'weakness' section, you also suggested using Midjourney as a comparative baseline. However, it is important to clarify that Midjourney does not support generation conditioned on three input images. Its interface only differentiates between a single character reference (cref) and a single style reference (sref). The exemplar pair in RelationAdapter serves as a visual demonstration of edit intent, which is distinct from either character or style references in Midjourney.\n\nNevertheless, we conducted experiments to evaluate Midjourney's capabilities by removing the pre-edited example image input. To maintain consistency with the original input, we used the original image as the cref and the edited example image as the sref. It is worth noting that, for fairness, we chose style transfer tasks that RelationAdapter was not trained on for comparison with Midjourney. The text prompt used in Midjourney is as follows:\n\n$text\\ prompt\\ --cref\\ <source\\ image>\\ --sref\\ <edited\\ image\\ in\\ the\\ editing\\ example\\ pair>\\ --cw\\ 90\\ --sw\\ 70\\ --v\\ 6.1$\n\nTable 1 compares the performance of our proposed method, RelationAdapter, with Midjourney (MJ) on unseen style transfer tasks. Despite not having seen these specific tasks during training, our model demonstrates consistently superior results across all evaluation metrics.\n\n**Table 1: Comparison of RelationAdapter and MJ (Midjourney) on Unseen Style Transfer Tasks. The best results are denoted as Bold.**\n\nMethod      | MSE ↓   | CLIP-I ↑  | GPT-C ↑ | GPT-A ↑\n------------|---------|---------|---------|---------\nMJ          | 0.107| 0.681  | 3.285  | 3.200\n**Ours**    | **0.062** | **0.774** | **4.203** | **4.278**\n\nPlus, we acknowledge that our current ablation study does not separately isolate the effects of position encoding cloning and noise-free conditioning. These settings are already backed by both theoretical intuition and empirical precedent in recent literature such as LayerCraft [7], OminiControl [5], EasyControl [8] and Kontext [9], reinforcing their empirical effectiveness as standard paradigms in Transformer-based diffusion models. \n\n## 3. What is the actual advantage (in terms of effectiveness and efficiency) of the proposed method compared to the in-context based variant? Please give further discussion of the claimed 'higher editing accuracy and consistency' including possible reasons behind this improvement, and add quantitative numbers of memory usage savings.\n\nRegarding the actual advantage over the in-context based variant in terms of effectiveness and efficiency, we provide the following comparative analysis: \n\nTraining Efficiency: The in-context based method required approximately 77 GB of GPU memory and a total of 51.5 hours of training time. In contrast, our proposed RelationAdapter used around 74 GB of GPU memory and completed training in approximately 48 hours. This represents a memory saving of ~3 GB and a 6.8\\% reduction in training time. \n\nInference Efficiency: For editing a single image of resolution 1024×1024, the in-context based method requires over 13 seconds of inference, whereas our method only takes less than 9 seconds, achieving a 30.8\\% speed-up in inference. \n\nThese improvements arise from a crucial architectural difference: as mentioned in the second paragraph of the introduction, the in-context method, which employs complete token concatenation, increases the computational load on the attention mechanism, leading to slower inference speeds. In contrast, RelationAdapter utilizes a decoupled attention mechanism, where computations are performed separately and subsequently fused, resulting in more efficient processing.\n\nRegarding higher editing accuracy and consistency, we still attribute these improvements to the decoupled attention mechanism. This mechanism helps avoid contamination between the original image features and the exemplar image pair features, resulting in more targeted and consistent edits. Both quantitative evaluations (e.g., Table 2) and qualitative results (e.g., Figure 6) support this rationale, showing that RelationAdapter consistently outperforms the in-context variant. We will emphasize these improvements in the final version of the paper, as we believe they clearly illustrate the concrete benefits and design motivations behind our approach.\n\n## 4. Position encoding cloning is clearly effective for pixel-wise aligned editing tasks. However, for other tasks like customized generation, is this strategy still reasonable? This problem seems to have been discussed in previous works [5], please give more explanations.\n\nPEC was designed to enforce spatial consistency without requiring strict spatial correspondence. Instead, it offers a soft spatial prior to guide attention, allowing for the editing of semantically aligned yet structurally flexible regions.\n\nOminiControl [5] does mention using different positional strategies for pixel-aligned and non-aligned tasks. However, we observe that the loss plots for shared position indexing and shifting position indexing—shown in Figure 3(b) of their paper—converge to the same value for both strategies as the number of training steps approaches 1000.\n\nSince our goal is to build an image editing model capable of handling a wide range of tasks, PEC provides a soft spatial prior to guide the model in learning flexible semantic alignment. After careful consideration, we decided not to introduce a fixed offset. Our experimental results demonstrate that PEC is effective even for complex or non-aligned tasks such as background replacement and composition editing. We will clarify this in the final version to emphasize the role of PEC as a non-strict guidance mechanism.\n\n\n\n[1] Li Z Y, Du R, Yan J, et al. VisualCloze: A Universal Image Generation Framework via Visual In-Context Learning. arXiv 2025.\n\n[2] Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention control. In ICLR, 2023.\n\n[3] Jiangshan Wang, Junfu Pu, Zhongang Qi, Jiayi Guo, Yue Ma, Nisha Huang, Yuxin Chen, Xiu Li, and Ying Shan. Taming rectified flow for inversion and editing.\n\n[4] Xi Chen, Yutong Feng, Mengting Chen, Yiyang Wang,Shilong Zhang, Yu Liu, Yujun Shen, and Hengshuang Zhao. Zero-shot image editing with reference imitation. In NeurIPS, 2025.\n\n[5] Zhenxiong Tan, Songhua Liu, Xingyi Yang, Qiaochu Xue, and Xinchao Wang. Ominicontrol: Minimal and universal control for diffusion transformer.\n\n[6] Chen, Xi, et al. \"Unireal: Universal image generation and editing via learning real-world dynamics.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n[7] Zhang Y, Li J, Tai Y W. Layercraft: Enhancing text-to-image generation with cot reasoning and layered object integration. arXiv 2025.\n\n[8] Zhang, Yuxuan, et al. Easycontrol: Adding efficient and flexible control for diffusion transformer. arXiv 2025.\n\n[9] Batifol, Stephen, et al. FLUX. 1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space. arXiv 2025.\n\n[10] Lan Chen, Qi Mao, Yuchao Gu, and Mike Zheng Shou. Edit transfer: Learning image editing via vision in-context relations."}, {"review_id": "m8AU61Cp3B", "reviewer": "Reviewer_ZkYa", "summary": "The paper introduces RelationAdapter, a novel framework for visual prompt-based image editing inspired by in-context learning in large language models. RelationAdapter leverages source-target image pairs to extract and transfer editing intent to new query images, addressing limitations of existing methods that struggle with non-rigid transformations. The framework includes a lightweight RelationAdapter module to model visual relationships, an In-Context Editor for zero-shot editing with positional encoding cloning, and a large-scale dataset Relation252K (252K samples across 218 tasks) to evaluate generalization. Experiments demonstrate superior performance over baselines in metrics like MSE, CLIP-I, and GPT-based evaluations, showcasing improved editing accuracy and consistency.", "questions": "1. How is this work related to LayerCraft: Enhancing Text-to-Image Generation?", "limitations": "The authors have discussed the limitations in their manuscript.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n1.  The framework is novel at the time, with a good range of performance \n2.  The In-Context Editor and positional encoding cloning enhance spatial alignment and fidelity, supporting robust zero-shot transfer to unseen tasks.\n3.  Relation252K provides a diverse benchmark (218 tasks) for evaluating visual prompt-based editing, addressing gaps in task diversity and scale.\n4. The ablation study is thorough.\n\n### Weaknesses\n1. There is a lack of analysis on potential failure case and rate?\n2.  How well would the framework generalize to OOD examples? \n3. Since the dataset contains human figures, I am not sure if this would require some ethics reviews (Perhaps the sources of data collection should be discussed).", "was_revised": false, "final_justification": "", "rebuttal": "## 1. How is this work related to LayerCraft: Enhancing Text-to-Image Generation?\n\nWe thank the reviewer for pointing out the connection to LayerCraft [1]. We would like to clarify the relationship between RelationAdapter and LayerCraft in two key aspects:\n\n(1) Differences in the usage and source of conditional signals:\nLayerCraft employs a multi-agent pipeline (e.g., ChainArchitect) to decompose a complex text prompt \"An apple on a table\" into sub-tasks—such as “generate an apple,” “generate a table,” and “merge the two objects”—and then leverages a text-to-image model (FLUX) to generate intermediate images for each sub-component. These images, generated from decomposed text instructions, serve as structured visual conditions. In contrast, RelationAdapter operates under a visual example-pair setting, where no intermediate image generation or prompt decomposition is required. Instead, given a source-target image pair, the RelationAdapter directly extracts the editing intent (which may encompass style transfer, semantic changes, or structural modifications) and encodes this transformation into two compact 128-dimensional tokens, serving as lightweight relational prompts.\n\n(2) Differences in integration mechanisms with the diffusion backbone:\nLayerCraft incorporates multiple visual conditions by concatenating their latent features (from intermediate images) into the generation pipeline. To prevent interference, it applies foreground-background masking via bounding boxes. This approach resembles in-context learning in spirit but introduces significant overhead in token length and computation. In contrast, RelationAdapter avoids latent concatenation altogether. It reuses the MM-Attention module and performs cross-attention between the shared query from the DiT backbone and the editing-intent tokens extracted in step (1). As no additional token sequences are introduced, our method maintains constant memory consumption regardless of the input pair complexity. This design allows RelationAdapter to be a lightweight, plug-and-play adapter that efficiently applies learned visual relations without increasing token length or GPU burden, distinguishing it from existing in-context or composition-based frameworks.\n\nWe appreciate the reviewer’s suggestion and will incorporate LayerCraft into the Related Work section in the final version to properly position our method within the broader landscape of text-to-image controllable generation.\n\n## 2. There is a lack of analysis on potential failure case and rate?\n\nThank you for your thoughtful comment. We would like to clarify that we have indeed provided an analysis of potential failure cases. As shown in Figure 11 in the Appendix, we include a qualitative examination of representative failure cases across four tasks: gesture editing, background pedestrian removal, document rectification, and image-to-sketch conversion. These examples reveal two primary limitations of our model: (1) challenges in fine-grained spatial alignment, and (2) challenges in restoring intricate textual details. We explicitly present and discuss these failure cases to identify areas where the model's performance can be improved. \n\n## 3. How well would the framework generalize to OOD examples?\n\nWe appreciate the reviewer’s question regarding the generalization of our framework to out-of-distribution (OOD) examples. To evaluate this, we specifically partitioned our test set to include 300 samples drawn from 10 tasks that were never encountered during training, as introduced in Section 4.2 (Benchmark). These tasks are a subset of the full test set (comprising 218 tasks in total), and we refer to them as unseen tasks. The results on these tasks are reported in the right half of Figures 5, 6, and 7.\n\nOur framework demonstrates promising generalization to these unseen tasks. As shown in Table 1, even without task-specific adaptation, the proposed RelationAdapter (RA) consistently improves performance over the baseline across all metrics—MSE, CLIP-I, GPT-C, and GPT-A—on both seen and unseen tasks. For example, on unseen tasks, incorporating RA reduces MSE from 0.061 to 0.053 and increases GPT-C from 3.840 to 4.187. \n\n**Table 1: Ablation Study on the Effectiveness of the RelationAdapter (RA) in Seen and Unseen Tasks (-S for Seen, -U for Unseen). The best results are denoted as Bold.**\n\nMethod         | MSE ↓    | CLIP-I ↑ | GPT-C ↑ | GPT-A ↑\n---------------|----------|----------|---------|---------\nw/o RA -S      | 0.055    | 0.787    | 3.909   | 3.597\n**Ours -S**    | **0.044** | **0.852** | **4.079** | **4.106**\nw/o RA -U      | 0.061    | 0.778    | 3.840   | 3.566\n**Ours -U**    | **0.053** | **0.812** | **4.187** | **4.173**\n\n\nThese results demonstrate that our method, particularly with the RelationAdapter, retains strong performance even in truly unseen scenarios. We believe this supports the claim that our framework exhibits meaningful generalization to OOD settings.\n\n## 4. Since the dataset contains human figures, I am not sure if this would require some ethics reviews (Perhaps the sources of data collection should be discussed).\n\nThank you for raising this important concern. We would like to clarify that all the data used in our work are sourced from publicly available datasets that are widely adopted in the research community and fall within licenses that permit research and publication. Specifically, the image editing dataset was synthetically generated using GPT-4o and Midjourney, which ensures compliance with ethical and privacy standards. For the low-level vision datasets, all sources are properly cited in the paper. Since our work strictly adheres to the usage policies of the original datasets and does not involve any private data collection or interaction with human subjects, it does not require additional ethics approval.\n\n[1] Zhang Y, Li J, Tai Y W. Layercraft: Enhancing text-to-image generation with cot reasoning and layered object integration. arXiv 2025."}, {"review_id": "zskQWhcrkS", "reviewer": "Reviewer_Bkni", "summary": "The paper proposes RelationAdapter, a lightweight module for DiT-based models that enables effective transfer of visual editing intent from source-target pairs, and introduces a large-scale dataset, Relation252K, to benchmark generalizable image editing. Experiments demonstrate improved generation quality and editing performance.", "questions": "1. In the RelationAdapter section, after revisiting visual prompt integration, the authors criticize the current FLUX-1 Redux by stating: 'visual feature embeddings are typically much longer than textual prompts, which can weaken or even nullify text-based guidance.' However, based on the presented results, the primary editing guidance appears to originate from source-target image pairs rather than text-based guidance. In fact, most demonstrated cases don't even include text prompts. Does this imply that text guidance is fundamentally unimportant for this task? Consequently, might FLUX-1 Redux actually not be problematic?\n2. In the RelationAdapter section, the authors should clarify why RA Attention requires the Q from MM Attention and what specific information it encodes. Additionally, does introducing the RA Attention module double the computational cost of the diffusion process?\n3. Most demos in the supplementary materials appear to focus on human subjects. Does this indicate stronger performance on human images? The authors should provide results across all four main task categories mentioned.", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. Position Encoding Cloning appears to offer a promising solution for pixel-level misalignment between source and target images.\n2. The introduction of a new image editing dataset with full open-source commitment constitutes a valuable contribution to the image editing community.\n\nWeakness：\n1. In the RelationAdapter section, after revisiting visual prompt integration, the authors criticize the current FLUX-1 Redux by stating: 'visual feature embeddings are typically much longer than textual prompts, which can weaken or even nullify text-based guidance.' However, based on the presented results, the primary editing guidance appears to originate from source-target image pairs rather than text-based guidance. In fact, most demonstrated cases don't even include text prompts. Does this imply that text guidance is fundamentally unimportant for this task? Consequently, might FLUX-1 Redux actually not be problematic?\n2. In the RelationAdapter section, the authors should clarify why RA Attention requires the Q from MM Attention and what specific information it encodes. Additionally, does introducing the RA Attention module double the computational cost of the diffusion process?\n3. Most demos in the supplementary materials appear to focus on human subjects. Does this indicate stronger performance on human images? The authors should provide results across all four main task categories mentioned.\n4. The comparative experiments include only two baselines, which is limited. The ablation studies should further dissect individual components within each module.", "was_revised": true, "final_justification": "3: Borderline reject.", "rebuttal": "## 1. In the RelationAdapter section, after revisiting visual prompt integration, the authors criticize the current FLUX-1 Redux by stating: 'visual feature embeddings are typically much longer than textual prompts, which can weaken or even nullify text-based guidance.' However, based on the presented results, the primary editing guidance appears to originate from source-target image pairs rather than text-based guidance. In fact, most demonstrated cases don't even include text prompts. Does this imply that text guidance is fundamentally unimportant for this task? Consequently, might FLUX-1 Redux actually not be problematic?\n\nFirst of all, we sincerely thank the reviewer for this insightful question. Below is our response: \nRedux was originally positioned as a model for generating diverse image variants, which is stated on the homepage of the black-forest-labs/flux GitHub project. Image variants here refer to outputs that tend to closely resemble the input image. However, it has been widely observed by users that Redux fails to properly respond to text prompts—when a prompt is provided alongside an input image, the prompt is often ignored. This limitation has led to the development of several community-driven extensions aiming to improve prompt control within Redux, such as ComfyUIAdvancedRefluxControl. \n\nAs mentioned in our paper, after analyzing the Redux codebase, we found the root cause lies in its prompt integration strategy: the text prompt is encoded using a T5 encoder, and the resulting text token sequence is simply concatenated with the image token sequence. Due to this, the textual input is significantly shorter than the visual input and easily overwhelmed. For example, a single conditioning image of size 768×1360 produces approximately (768 / 16) × (1360 / 16) = 4080 image tokens, whereas the number of text tokens is typically capped at 256 << 4080.\n\nThis imbalance becomes even more severe in our source-target image pair setting, where three conditioning images(an original image, a source image showing the pre-edit state, and a target image showing the post-edit state) are used, leading to 4080 × 3 = 12,240 image tokens versus 256 text tokens. To address this, we propose the RelationAdapter, which encodes source and target images into two lightweight 128-token sequences. This not only reduces image token length drastically but also brings it into balance with the text sequence, resulting in a combined length of 128×2 = 256—matching the text input. Surprisingly, this compression of visual tokens not only reduces computational cost, but also improves editing performance compared to prior in-context learning approaches that directly concatenate high-dimensional image tokens.\n\nIn order to demonstrate the impact of prompts on the RelationAdapter's editing results, we conducted additional ablation experiments and generated results using input without prompts. The evaluation results are presented in Table 1. \n\n**Table 1: Quantitative Comparison of Methods with and without Prompts (-S for Seen, -U for Unseen).**\n\nMethod          | MSE ↓   | CLIP-I ↑  | GPT-C ↑  | GPT-A ↑\n----------------|---------|---------|---------|---------\nw/o Prompt -S   | 0.065   | 0.777   | **4.272**   | 3.413\n**Ours -S**     | **0.044** | **0.852** | 3.997 | **4.353**\nw/o Prompt -U   | 0.091   | 0.745   | **4.391**   | 3.222\n**Ours -U**     | **0.053** | **0.812** | 3.973 | **4.293**\n\nIn the absence of textual guidance, the model tends to avoid modifying the original image, which explains the higher GPT-C scores (a metric indicating the consistency of the edited result with the original image). This is a normal phenomenon. In contrast, the performance of MSE, CLIP-I, and GPT-A is notably worse compared to the prompted scenario.\n\nAs for the images in the supplementary material, they provide an intuitive comparison of generation results after being influenced by the text prompts. Thank you for your question — our released dataset will include all relevant content, including source-target image pairs and the corresponding editing instructions.\n\n## 2. In the RelationAdapter section, the authors should clarify why RA Attention requires the Q from MM Attention and what specific information it encodes. Additionally, does introducing the RA Attention module double the computational cost of the diffusion process?\n\n1. Why RA Attention requires the Q from MM Attention?\n\n    (1) Semantic alignment: By using DiT’s backbone queries, which are conditioned on the source image and textual cues, the RA attention mechanism ensures that the visual cues adjust the attention mechanism based on the current generation context to avoid overfitting to the cue words.\n\n    (2) Efficient control: Sharing Q-values avoids an extra query projection layer and allows the RA attention mechanism to be interpreted as a soft modulation of the original attention mechanism, improving interpretability and stability.\n\n2. What information does Q encode?\n\n    Essentially, the Q-value encodes the intent of the current generation (from the source and the text prompt), while the RA key/value encodes “how to edit” based on the example pairs. The RA module takes a sample pair and encodes the semantic shift and structural difference between the pre-edited and post-edited images. When the same Q-value is injected through the attention mechanism, it allows the model to adjust its generation based on “how similar content was edited before” while preserving the spatial context of the query.\n\n3. Does introducing the RA Attention module double the computational cost of the diffusion process?\nThe RA Attention module does not double the computational cost. RA attention introduces an additional lightweight KV attention layer in each DiT block, but it:\n\n    (1) Shares the existing Q computation with the original DiT self-attention mechanism;\n\n    (2) Uses a low-dimensional projection of SigLIP visual cue features (128D labels per image);\n\n    (3) Performs only one additional attention fusion step per block and uses a fixed scalar $\\alpha$\n    \n    When editing an image of size 1024×1024, the in-context based method requires over 13 seconds of inference, whereas our method only takes less than 9 seconds. This is because the complete concatenation of tokens greatly increases the computational time for attention, as we discussed in the second paragraph of the Introduction.\n\n## 3. Most demos in the supplementary materials appear to focus on human subjects. Does this indicate stronger performance on human images? The authors should provide results across all four main task categories mentioned.\n\nWe appreciate the reviewer's observation. Figure 5 (comparison results with baselines) does have a slightly higher proportion of human-centric examples. But this is unavoidable and is done for fair benchmarking—since Edit Transfer, one of our primary baselines, is specifically optimized for human appearance, pose, and outfit manipulation. Using non-human examples (e.g., animals, scenes) would risk an unfair comparison.\n\nPlus, each example in the supplementary corresponds to a distinct editing task, and collectively, they span all four main task categories described in the paper: 1. Low-level image processing (e.g., surfacenormal estimation, depth estimation) 2. Style transfer (e.g., crayon sketch, watercolor painting) 3. Image editing (e.g., object addition, cloth try-on) 4. Customized generation (e.g., Pokémon evolution, plush logo)\n\nAt the same time, we show the evaluation results of the four task categories in Table 3 of the paper, where the human-preferred evaluation results (edit accuracy and edit consistency) of each evaluation result are around 4 (out of 5), demonstrating that in the four task categories, the ability of RelationAdapter can cover a variety of tasks of different complexity. \n\n**Table 3: Quantitative comparison of evaluation metrics (mean ± std) across four image generation tasks. Best results are shown in bold.**\n\nTasks                         | MSE ↓        | CLIP-I ↑    | GPT-C ↑     | GPT-A ↑\n------------------------------|--------------|-------------|-------------|-------------\nLow-Level (n=32)              | **0.028 ± 0.038**| **0.885 ± 0.067**| 3.943 ± 0.383| 3.822 ± 0.406\nStyle Transfer (n=84)         | 0.051 ± 0.032| 0.846 ± 0.036| 4.077 ± 0.198| **4.246 ± 0.285**\nImage Editing (n=63)          | 0.031 ± 0.023| 0.861 ± 0.055| **4.173 ± 0.229**| 4.100 ± 0.400\nCustomized Generation (n=39)  | 0.065 ± 0.048| 0.816 ± 0.073| 4.071 ± 0.224| 4.064 ± 0.313\n\nThis ensures that the evaluation results cover the entire range of tasks, not just human-based editing. We will clarify this and annotate the category of each additional experimental result in the supplementary material in the final version."}, {"review_id": "RAZcrwBi3k", "reviewer": "Reviewer_s3JJ", "summary": "The paper proposes a novel approach for in-context learning of visual transformation from a single source-target image pair and applying it to query images in MMDiT-based text-to-image diffusion models. The authors demonstrate the generalization of the proposed approach on unseen editing tasks. Further, the paper proposes a large-scale visual editing dataset and benchmark with 218 diverse editing scenarios.", "questions": "My primary concerns are limited baseline, limited evaluation and missing ablation studies:\n1. The authors should include comparisons with atleast two additional baselines including [7] to demonstrate the effectiveness of their approach. Further, the authors should consider comparing [1] using the same LORA Rank for a fair comparison.\n2. The authors should evaluate the quality of generated images with FID score.\n3. The authors should discuss the effect of parameter $\\alpha$ on generation. Further, the authors should provide an experiment to back their claim that SigLIP is a better alternative compared to VAE or CLIP for extracting visual prompt features from paired images.", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strength**\n\n1. The paper proposes a novel adapter for MMDiT-based architecture to extract visual transformation between a source-target image pair and apply transformation to a query image. \n2. The authors demonstrate the generalization of the proposed approach on unseen editing tasks along with a user-study demonstrating improved editing preference over baseline.\n3. The paper is overall well-written and easy to follow with clear motivation.\n\n**Weakness**\n\n1. Limited baselines: The proposed method is compared with just two baselines, Edit Transfer[1] and VisualCloze[2]. Additional baselines[3,4,5,6,7] should be considered and differences with existing MM-DiT based methods[1,2] should be discussed in related work. Further, comparison with Edit Transfer[1] is not fair as it is trained with much smaller LORA rank 16 while this work uses a high LORA rank of 128. \n2. Missing evaluation: The work is missing evaluation comparing the quality of the generated images, of which MSE is not a good indicator, and metrics such as FID should be considered.\n3. Missing ablation studies: The work is missing ablation study for attention fusion coefficient $\\alpha$ in Eq 6. Further, quantitative comparison should be provided to back claims regarding SigLIP being a better alternative compared to VAE or CLIP for extracting visual prompt features from paired images. \n4. While the work claims that the training is parameter efficient, the total number of trainable parameters are 1.5B which is relatively high.  \n\n\n[1] Lan Chen, Qi Mao, Yuchao Gu, and Mike Zheng Shou. Edit transfer: Learning image editing via vision in-context relations.\n\n[2] Zhong-Yu Li, Ruoyi Du, Juncheng Yan, Le Zhuo, Zhen Li, Peng Gao, Zhanyu Ma, and Ming-Ming Cheng. Visualcloze: A universal image generation framework via visual in-context learning.\n\n[3] Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention control. In ICLR, 2023.\n\n[4] Jiangshan Wang, Junfu Pu, Zhongang Qi, Jiayi Guo, Yue Ma, Nisha Huang, Yuxin Chen, Xiu Li, and Ying Shan. Taming rectified flow for inversion and editing. \n\n[5] Xi Chen, Yutong Feng, Mengting Chen, Yiyang Wang,Shilong Zhang, Yu Liu, Yujun Shen, and Hengshuang Zhao. Zero-shot image editing with reference imitation. In NeurIPS, 2025.\n\n[6] Zhenxiong Tan, Songhua Liu, Xingyi Yang, Qiaochu Xue, and Xinchao Wang. Ominicontrol: Minimal and universal control for diffusion transformer. \n\n[7] Chen, Xi, et al. \"Unireal: Universal image generation and editing via learning real-world dynamics.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.", "was_revised": true, "final_justification": "While my concerns regarding quality of generated images, comparison with baselines under same LORA rank, and motivation for the use of SigLIP have been resolved, my concerns around limited baselines, ablation for parameter , and large number of trainable parameters still remain. 1) Example pair used in the proposed method can be interpreted as visual conditions and text prompt can be used to guide UniReal[7] to generate image from source image following visual relation of the pair (for example, \"Generate RES1 from IMG3 by applying the visual relation of IMG2 from IMG1\"). 2) It is expected that the  used in training would be the optimal value during inference. However, effect of varying  in the training is unclear and  might not be the optimal value for best performance. 3) Rank 16 does not lead to a significant reduction in the parameter count which reduces from 1.5B to around 1.25B. Hence, I will keep my score.", "rebuttal": "## 1. The authors should include comparisons with at least two additional baselines including [7] to demonstrate the effectiveness of their approach. Further, the authors should consider comparing [1] using the same LORA Rank for a fair comparison.\n\nThank you for your feedback. Below, we elaborate on the rationale behind our baseline choices:\n\nRelationAdapter requires a complete pair of 'before' and 'after' edit examples to be provided as context, whereas none of the other methods mentioned above rely on such a pair. This formulation is unique to Edit Transfer [1] and VisualCloze [2].\n\nPrompt-to-Prompt [3] and RF-Edit [4] operate entirely in the text-driven editing paradigm. They do not utilize any visual examples and thus lack the capacity to learn visual editing transformations from exemplar pairs. Zero-shot Image Editing [5] and OminiControl [6] focus primarily on reference-conditioned generation, where an auxiliary image—typically a Canny edge map, depth map, segmentation mask, or color scribble—is used to provide fine-grained control signals during image generation. The core idea is to apply a predefined image condition to guide image generation, rather than to learn and transfer a transformation between image pairs. UniReal [7] tackles a different task setup—the combination of multiple conditioning images (e.g., \"place the cat from IMG1 onto the bed in IMG2 guided by a mask in IMG3\"). \n\nRegarding the comment on using the same LoRA rank as [1] for fair comparison, we note that our Relation252K dataset is significantly larger and more diverse (218 tasks) than that of Edit Transfer. Based on our experience, a larger dataset generally requires a higher LoRA rank.\n\nNevertheless, we have also tested the performance of our model with a LoRA rank of 16 (Table 1). For ease of comparison, Table 2 includes results with LoRA rank 128, showing that our method performs well even with lower ranks.\n\n**Table 1: Comparison of RelationAdapter and ET (Edit Transfer) with LoRA rank=16. The best results are denoted as Bold.**\n\nMethod            | MSE ↓    | CLIP-I ↑   | FID ↓    | GPT-C ↑   | GPT-A ↑\n------------------|----------|----------|----------|-----------|---------\nEditTransfer      | 0.043    | 0.827    | 4.908   | 4.278    | 3.716\n**Ours∩ET (Rank=16)**| **0.021** | **0.868**| **2.892**| **4.483** | **4.533**\n\n**Table 2: Quantitative Comparison of ET (Edit Transfer) Trained on a Common Task. The best results are denoted as Bold.**\n\nMethod          | MSE ↓     | CLIP-I ↑   | FID ↓     | GPT-C ↑    | GPT-A ↑\n----------------|-----------|------------|-----------|------------|-----------\nEditTransfer    | 0.043     | 0.827      | 4.908    | 4.234      | 3.508\n**Ours∩ET (Rank=128)**| **0.020** | **0.905**  | **2.201**| **4.437**  | **4.429**\n\nNote that due to the inherent variability in GPT model scoring, some fluctuations in the GPT-C and GPT-A metrics are normal.\n\n## 2. The authors should evaluate the quality of generated images with FID score.\n\nWe appreciate the reviewer’s valuable suggestion. We have now included FID as a new evaluation metric to further assess the quality of the images produced by RelationAdapter. \n\nAs shown in Table 3, our model outperforms the baseline methods in the FID score, indicating a clear improvement in the quality of generated images.\n\n**Table 3: FID Results for Baseline Comparisons (ET: Edit Transfer, VC: VisualCloze).**\n\nMethod        | FID ↓\n--------------|---------\nEditTransfer  | 4.908\n**Ours∩ET**   | **2.201**\nVisualCloze   | 7.218\n**Ours∩VC**   | **4.801**\n\nAs shown in Table 4, compared with the in-context based method (without using RelationAdapter), our model also has a observable improvement in FID score.\n\n**Table 4: FID Results for Ablation Study (-S for Seen, -U for Unseen).**\n\nMethod         | FID ↓\n---------------|---------\nw/o RA -S      | 5.968\n**Ours -S**    | **5.191**\nw/o RA -U      | 5.571\n**Ours -U**    | **5.498**\n\n\n## 3. The authors should discuss the effect of parameter $\\alpha$ on generation. Further, the authors should provide an experiment to back their claim that SigLIP is a better alternative compared to VAE or CLIP for extracting visual prompt features from paired images.\n\nThe attention fusion coefficient $\\alpha$ controls the relative influence between the visual prompt attention produced by the RelationAdapter and the base Multi-Modal Attention (MMA) within the Diffusion Transformer. As described in Section 4.1 (Settings), we set $\\alpha$ = 1 during training to maintain a balanced integration of visual guidance and generative consistency.\n\nSince $\\alpha$ is part of the training configuration, we adopt the same value ($\\alpha$ = 1) during inference to ensure consistency between training and deployment. Nevertheless, we further investigated the impact of varying $\\alpha$ and report the results in Table 5. The evaluation confirms that maintaining $\\alpha$ = 1—aligned with the training setup—yields the most stable and optimal generation performance.\n\n**Table 5: Effect of Adjusting Attention Fusion Coefficient $\\alpha$ on Image Editing Quality (−S for Seen, −U for Unseen Tasks).**\n\nMethod         | MSE ↓   | CLIP-I ↑  | FID ↓   | GPT-C ↑  | GPT-A ↑\n---------------|---------|---------|---------|----------|---------\n$\\alpha$ = 2 -S       | **0.044** | 0.827   | 5.564   | 3.855    | 3.536\n**$\\alpha$ = 1 -S**   | **0.044** | **0.852** | **5.191** | **4.115**  | **4.258**\n$\\alpha$ = 0.5 -S     | 0.050   | 0.832   | 5.895   | 4.099    | 3.591\n$\\alpha$ = 2 -U       | 0.054   | 0.794   | 5.805   | 3.858    | 3.527\n**$\\alpha$ = 1 -U**   | **0.053** | **0.812** | **5.498** | **4.211**   | **4.377**\n$\\alpha$ = 0.5 -U     | 0.056   | 0.808   | 5.724   | 4.149  | 3.620\n\nPlus, our decision to use SigLIP in place of CLIP for visual prompt feature extraction is motivated by both prior empirical findings and architectural considerations. Specifically, CLIP has been widely observed to exhibit limited performance on fine-grained visual representation. Several studies replaced CLIP with SigLIP for generative and editing tasks, with ablations validating its effectiveness:\n\nFlux.1-Redux-dev [8] employs SigLIP as the image encoder in a diffusion framework for visual editing, citing improved visual prompt fidelity and detail preservation (Black Forest Labs). Generative Visual Instruction Tuning [9] replaces CLIP with SigLIP for paired visual-text instruction learning, demonstrating improved grounding and fidelity. MultiGen [10] leverages SigLIP to extract reference features for multi-modal prompt-based image generation, showing stronger visual-semantic alignment compared to CLIP. Insert Anything [11] integrates SigLIP into a DiT-based pipeline for object insertion, enabling better semantic conditioning from reference images. Calligrapher [12] uses SigLIP for personalized text-image customization and finds it superior in capturing local stylistic variations. UniToken [13] unifies visual understanding and generation using SigLIP to encode dense visual tokens in lieu of CLIP, enhancing both reconstruction and cross-modal alignment.\n\n## 4. The total number of trainable parameters are 1.5B which is relatively high.\n\nWe conducted experiments with a lower-rank setting (rank=16), which resulted in a significantly smaller parameter footprint of approximately 1.25B. As shown in Table 6, our method remains effective even under this lightweight configuration. \n\n**Table 6: Comparison Between LoRA Rank=16 and Our Original Experimental Setting (−S for Seen, −U for Unseen).**\n\nMethod              | MSE ↓   | CLIP-I ↑  | FID ↓   | GPT-C ↑  | GPT-A ↑\n--------------------|---------|---------|---------|----------|---------\nOurs -S (Rank=16)   | 0.048   | 0.828   | 5.757   | 4.035    | 3.607\n**Ours -S**         | **0.044** | **0.852** | **5.191** | **4.145**  | **4.219**\nOurs -U (Rank=16)   | 0.064   | 0.792   | 5.924   | 4.026    | 3.505\n**Ours -U**         | **0.053** | **0.812** | **5.498** | **4.195**  | **4.239**\n\nIn practice, we observe that the optimal LoRA rank correlates with dataset size and diversity. Given the scale of our Relation252K dataset, we opted for a higher rank (128) to fully exploit its richness. \n\n\n[1] Lan Chen, Qi Mao, Yuchao Gu, and Mike Zheng Shou. Edit transfer: Learning image editing via vision in-context relations.\n\n[2] Zhong-Yu Li, Ruoyi Du, Juncheng Yan, Le Zhuo, Zhen Li, Peng Gao, Zhanyu Ma, and Ming-Ming Cheng. Visualcloze: A universal image generation framework via visual in-context learning.\n\n[3] Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention control. In ICLR, 2023.\n\n[4] Jiangshan Wang, Junfu Pu, Zhongang Qi, Jiayi Guo, Yue Ma, Nisha Huang, Yuxin Chen, Xiu Li, and Ying Shan. Taming rectified flow for inversion and editing.\n\n[5] Xi Chen, Yutong Feng, Mengting Chen, Yiyang Wang,Shilong Zhang, Yu Liu, Yujun Shen, and Hengshuang Zhao. Zero-shot image editing with reference imitation. In NeurIPS, 2025.\n\n[6] Zhenxiong Tan, Songhua Liu, Xingyi Yang, Qiaochu Xue, and Xinchao Wang. Ominicontrol: Minimal and universal control for diffusion transformer.\n\n[7] Chen, Xi, et al. \"Unireal: Universal image generation and editing via learning real-world dynamics.\" In CVPR, 2025.\n\n[8] Black Forest Labs. (2024).\n\n[9] Hernandez, J., Villegas, R., and Ordonez, V. Generative Visual Instruction Tuning. arXiv 2024.\n\n[10] Wu, Z.-F., Huang, L., Wang, W., Wei, Y., and Liu, Y. MultiGen: Zero-shot Image Generation from Multi-modal Prompts. In ECCV, 2024.\n\n[11] Song, W., Jiang, H., Yang, Z., Quan, R., and Yang, Y. Insert Anything: Image Insertion via In-Context Editing in DiT. arXiv 2025.\n\n[12] Ma, Y., Bai, Q., Ouyang, H., Cheng, K. L., Wang, Q., Liu, H., … Chen, Q. Calligrapher: Freestyle Text Image Customization. arXiv 2025.\n\n[13] Jiao, Y., Qiu, H., Jie, Z., Chen, S., Chen, J., Ma, L., and Jiang, Y.-G. UniToken: Harmonizing Multimodal Understanding and Generation through Unified Visual Encoding. arXiv 2025."}]

Author rebuttal:

Meta-review: 1x Accept, 1x Borderline Accept, 2x Borderline Reject: The paper proposes an in-context learning method for image editing, guided by a single "before-and-after" example. Reviewers acknowledge several strengths of the paper, including technical soundness, clear presentation, and a comprehensive dataset with 218 diverse editing tasks. The weaknesses noted by reviewers include limited baseline comparisons (only two methods) and insufficient ablation studies. In the rebuttal, the authors provided extensive new experiments including FID scores, Midjourney comparisons, ablations, and lower-rank LoRA tests addressing concerns about the 1.5B parameter count. They also clarified that suggested baselines like Prompt-to-Prompt are text-only methods that don't use visual examples, while others like OminiControl use auxiliary control images (depth maps, edges) rather than learning transformations from example pairs. Regarding UniReal, they noted its repository only includes dataset construction code without training or inference implementations. Two reviewers found the rebuttal convincing (cG9Q changed to Accept, ZkYa's concerns were addressed), while Bkni provided minimal engagement and s3JJ maintained baseline concerns about UniReal despite partial resolution.

The AC recommends acceptance. The paper presents a technically sound approach with compelling empirical results. The authors' comprehensive rebuttal with new experiments adequately addresses the primary concerns and support acceptance. While the 2-2 split reflects differing views, Bkni provided minimal engagement and the inability to compare to s3JJ's suggested baseline UniReal due to lack of available implementation makes this concern impractical.

Authors are advised to revise based on reviewers' comments to make the final version more solid.
Decision: Accept (poster)