Title: RelationAdapter: Learning and Transferring Visual Relation with Diffusion Transformers
Venue: NeurIPS 2025
Accepted: yes
Keywords: Diffusion model

In-context 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

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. Project page: https://github.com/gy8888/RelationAdapter

1 Introduction

Humans excel at learning from examples. When presented with just a single pair of images, comprising an original and its edited counterpart, we can intuitively infer the underlying transformation and

apply it to new, unseen instances. This paradigm, known as edit transfer or in-context visual learning [6, 28, 58, 64], provides an intuitive and data-efficient solution for building flexible visual editing systems. Unlike instruction-based editing methods [18, 25, 61] that rely on textual prompts-where 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 a 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.

In-context learning-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 a dedicated bypass module that can efficiently extract and inject editing intent from example image pairs, thereby facilitating image editing tasks. Nevertheless, building a scalable and general-purpose framework for image-pair-driven editing still presents several fundamental challenges: (1) accurately extracting visual transformation signals from a 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 to 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 edits-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 RelationAdapter , the first DiT-based adapter module designed to extract visual transformations from paired images, enabling efficient conditional control for generating high-quality images with limited training samples. A dual-branch 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] (e.g., SigLIP) to extract visual features, subsequently 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-Context Editor , a consistency-aware 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. A 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 [4, 22, 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, usercustomized generation, and style-guided 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 a unified and scalable foundation for training and evaluating future image-pair editing models.

2 Related Work

2.1 Diffusion Models

Diffusion models have emerged as a dominant paradigm for high-fidelity image generation [42, 69, 70], image editing[32, 71, 72], video generation [50, 51, 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 structure-preserving and personalized editing techniques. Recent advances have shifted from convolutional U-Net backbones to Transformerbased architectures, as exemplified by Diffusion Transformers (DiT) [37, 73] and FLUX [1]. 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 enabling conditional guidance. ControlNet [68], T2I-Adapter [33], and MasaCtrl [5] inject external conditions-such as edges, poses, or style cues-into pretrained models without altering base weights. These zero-shot or plug-and-play methods offer flexibility in structure-aware generation. In parallel, layout- and skeleton-guided frameworks such as GLIGEN [27] and HumanSD [24] enable high-level spatial control. Fine-tuning-based strategies, including Concept Sliders [15] and Finestyle [66], learn attribute directions or attention maps to enable consistent manipulations. In the era of Diffusion Transformers, some methods concatenate condition tokens with denoised tokens and achieve controllable generation through bidirectional attention mechanisms or causal attention mechanisms [16, 22, 49, 51, 52, 55]. Despite their success, many of these methods rely on fixed condition formats or 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-Prompt [18] and InstructPix2Pix [4] 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 [62] supports joint conditioning on text and image modalities, expanding the space of compositional control. Complementary to existing methods that often introduce a substantial number of additional parameters, our proposed RelationAdapter 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-grained structural control and robust style preservation.

3 Methods

In this section, we present the overall architecture of our proposed methods in Section 3.1. Next, Section 3.2 outlines our RelationAdapter module, which serves as a 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) [21] 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:

RelationAdapter. RelationAdapter is a lightweight module built on the DiT architecture. By embedding a novel attention processor in each DiT block, it captures visual transformations and

Out Projector

MM Attention

LoRA

V

QKV Projector

K

Q

KV Projector

K

V

Q

RA Attention

VAE

VAE

...

...

...

Text

Encoder

+

+

Clone

Position Encoding

...

...

...

Linear

Linear

...

...

SigLip

SigLip

Relation Adapter

DiT Block

DiT Block

DiT Block

...

'Apply a crayon

drawing style to

the real-world

image.'

In-Context Editor

Legends

Image Prompt Tokens

Image Reference Tokens

Image Souce Tokens

Text Tokens

Noised Latent Tokens

Position Embedding

Frozen

Learnable

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 a 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-driven editing while preserving the pre-trained DiT architecture for compatibility and efficiency.

3.2 RelationAdapter

Our method can be formulated as a function that maps a set of multimodal inputs, namely, a visual prompt image pair ( I prm , I ref ) , a source image I src, and a textual prompt T prm to a post-edited image as a target image I tar :

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] as an image encoder, but its limited ability to preserve fine-grained visual details hinders high-fidelity customization. To overcome this, we adopt the SigLIP-SO400M-Patch14-384 [65] model for its superior semantic fidelity in extracting visual prompt features from paired visual prompts I prm and I ref. Let c P and c R denote the representations of the sequence of features of I prm and I ref, respectively. The visual prompt representation c V is constructed by concatenating c P and c R .

Revisiting Visual Prompt Integration. To enhance the representational flexibility of the DiT based model, we revisit the current mainstream image prompt based approaches (e.g., FLUX.1 Redux [3], which directly appends visual features to the output of the T5 encoder [31]).

Given the visual prompt features c V and the backbone DiT input features c B , FLUX.1 Redux applies a bidirectional self-attention mechanism over the concatenated feature sequence. The resulting attention output Z ′ is computed as:

and c B,V denotes the concatenation of backbone DiT input features c B and visual features c V .

Decoupled Attention Injection. A key limitation of current approaches is that visual prompts c V are typically much longer than textual prompts c T , which can weaken or even nullify text-based guidance. We design a separate key-value (KV) attention projection mechanism, W ′ k and W ′ v , for the visual prompts . Crucially, the cross-attention layer for visual prompts shares the same query Q with the backbone DiT branch:

Then, we fuse the visual attention output Z V (from the RelationAdapter) with the original DiT attention output Z B before passing it to the Output Projection module:

where α is a 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 incontext image editing framework. Both the source image I src and the target image I tar are encoded into latent representations, c S and z respectively, via a Variational Autoencoder (VAE) [26]. After cloning positional encodings, the latent tokens are concatenated along the sequence dimension to enable Multi-modal Attention [36], formulated as:

Here, Z = [ z ; c S ; c T ] denotes the concatenation of noisy latent tokens z , source image tokens c S , and text tokens c T , where z is obtained by adding noises to 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 a Position Encoding Cloning strategy that explicitly embeds latent spatial correspondences into the generative process. Specifically, we enforce alignment between the positional encodings of the source condition representation c S and the noise variable z , establishing a 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-grained 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 a context learning-formatted editing dataset comprising 251,580 samples (see Section 3.4). We then applied LoRA fine-tuning to the DiT module for parameter-efficient adaptation. Specifically, we employed high-rank LoRA by freezing the pre-trained weights W 0 and injecting trainable low-rank matrices A ∈ R r × k and B ∈ R d × r 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 as input to a step-wise denoising process. However, this often disrupts the source features, causing detail loss and reduced pixel fidelity. To address this, we propose a noise-free paradigm that preserves the source features c S from I src throughout all denoising stages. By maintaining these features in a clean state, we provide a stable and accurate reference for generating the target image I tar. Combined with position encoding cloning and a Multi-scale Modulation Attention (MMA) mechanism, this design enables precise, localized edits while minimizing unintended modifications.

3.4 Relation252K Dataset

We curate a 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.

Low Level

Image Editing

Depth

Surface Normal

Origami Effect

...

Close Eyes

Add Object

...

Customized Generation

Lego Form

...

...

Raise Arms

Van Gogh Style

Woodcut Effect

3D PhotoFrame

Source Caption: A person wearing a white shirt with rolled- up sleeves, posing casually.

Target Caption: A person  wearing  a white shirt  with buttoned sleeves against a red background.

Edit  Instruction: Apply  an  ID  photo  transformation  to  the portrait, front-facing, red background.

Unlabeled Image Pair

GPT-4o

You are an image edit expert, There is a comparison image (left and right). Please describe what you see on the left and right side ....

LNCON

Automatic Image Pairs Generation. We introduce a semi-automated pipeline for constructing a high-quality dataset. A custom script interfaces with a Discord bot to send /imagine commands to MidJourney, generating high-fidelity images. Also using the GPT-4o [35] multimodal API, we generate context-aware images from original inputs and edits. For low-level tasks, we additionally curate a subset of well-known benchmark datasets[10, 13, 14, 17, 34, 38, 39, 44] through manual 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 [11, 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 ( I src) and the corresponding edited image ( I tar) as a joint input to the GPT-4o API. A structured prompt guides the model to produce three outputs: (1) a concise description of I src ; (2) a concise description of I tar; and (3) a human-readable editing instruction describing the transformation from I src to I tar . An example illustrating the pipeline is shown in Figure 4. To conform with the model's input specification, image pairs are sampled and arranged via rotational permutation, with up to 2,000 instances selected per task to ensure distributional balance. In each sample, the upper half is used as visual context for the RelationAdapter, and the lower half is input to the In-Context Editor module. Directional editing instruction ( I src → I tar) are provided solely as text prompt, without detailed content descriptions.

4 Experiments

4.1 Settings

We initialize our model with FLUX.1-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 a rank of 128. Training spans 100,000 iterations on 4 H20 GPUs, with an accumulated batch size of 4. We use the AdamW optimizer and bfloat16 mixed-precision training, with an initial learning rate of 1 × 10 -4 . The total number of trainable parameters is 1,569.76 million. Training takes 48 hours and consumes ∼ 74GB of GPU memory. At inference, the model requires ∼ 40 GB of GPU memory on a single H20 GPU. The RelationAdapter employs a dual-branch SigLIP visual encoder, where each branch independently processes one image from the input pair and outputs a 128-dimensional feature token via a two-layer linear projection network. The attention

fusion coefficient α 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 to encoding.

4.2 Benchmark

We selected 2.6% of the dataset (6,540 samples) as a benchmark subset, covering a 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 [28]. Both baselines follow an in-context learning setup and are evaluated within the shared training task space to ensure a 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 ground-truth 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 alignment of generated images with real ones in the feature space of a 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 I prm to the reference image I ref , and evaluate the predictions based on two dimensions: Editing Consistency (GPT-C), which measures alignment with the source image I src , 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 in Table 1, our method consistently outperforms the baselines in MSE, CLIP-I, and FID metrics. Compared to Edit Transfer, our model achieves a 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 to 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 a 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 to VisualCloze, our method is less affected by the reference image I ref, especially in tasks like depth prediction and clothes try-on. VisualCloze tends to overly rely on I ref, 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 RelationAdapter module, we conducted an ablation study by directly concatenating the visual prompt features with the condition tokens c S . For a fair comparison, this baseline was trained for 100,000 steps, identical to RelationAdapter. As shown in

Table 2, our model consistently outperforms the in-context learning baseline across all five evaluation metrics on both seen and unseen tasks. This improvement is attributed to the RelationAdapter, which enhances performance by decoupling visual features and reducing redundancy.

Seen Tasks

Input Images

Input Images

Ours

VisualCloze

Ours

VisualCloze

Ours

VisualCloze

Ours

Edit

Transfer

Ours

Edit

Transfer

Ours

Edit

Transfer

Input Images

Input Images

Input Images

Input Images

1

Unseen Tasks

Table 2: 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.

Although latent-space concatenation (i.e., directly merging four input images before V AE encoding) is effective, it 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 RelationAdapter provides a more efficient alternative, enabling the model to capture and apply the semantic intent of editing instructions with minimal computational cost. Figure 6 demonstrates that our approach yields higher editing accuracy and consistency in both task settings.

Seen Tasks

Unseen Tasks

Ours

In-Context

Learning

Ours

In-Context

Learning

Ours

In-Context

Learning

Input Images

Input Images

Input Images

4.7 User Study

We conducted a 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 a 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:

Edit Accuracy: Which image better aligns with the editing intent implied by the prompt pair?

Edit Consistency: Which image better preserves the structure and identity of the source image?

Overall Preference: Which image do you prefer overall?

The aggregated results of the user study are summarized in Figure 7. When compared with an incontext 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 in 97.11% of cases for Edit Accuracy, 78.89% for Edit Consistency, and 75.78% for Overall Preference.













































































5 Discussion

As shown in Figure 8, RelationAdapter 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.

We evaluated RelationAdapter on four classification tasks of varying complexity. As shown in Table 3, it excels in complex tasks like style transfer and customized generation, showing strong semantic

alignment 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 a more complete assessment.

Seen Tasks

Unseen Tasks

Result

Result

Result

Input Images

Input Images

Input Images

PROGRAMMER

PROGRAMMER

PROGRAMMER

Manger 2

SrgowCs4

HSEAMI

GEOMETRIC

ILLUSTRA

TOGETHER.

FOREVER.

6 Limitation

Although our model performs well across various editing tasks, it sometimes fails to accurately render text details in the generated images. This is a common problem with current Diffusion models. In addition, the model may perform slightly differently on rare 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 our approach, we construct a large-scale dataset comprising 218 visual prompt-based editing tasks. We further introduce two training paradigms-position encoding cloning and a 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.

References

Black Forest Labs. Flux: Official inference repository for flux.1 models. https://github.com/ black-forest-labs/flux , 2024. Accessed: 2025-05-14.

Black Forest Labs. Flux.1-dev: A 12b parameter rectified flow transformer for text-to-image generation. https://huggingface.co/black-forest-labs/FLUX.1-dev , 2024. Accessed: 2025-05-14.

Black Forest Labs. Flux.1 redux-dev. https://huggingface.co/black-forest-labs/FLUX. 1-Redux-dev , 2024. Accessed: 2025-05-14.

Tim Brooks, Aleksander Holynski, and Alexei A Efros. Instructpix2pix: Learning to follow image editing instructions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 18392-18402, 2023.

Mingdeng Cao, Xintao Wang, Zhongang Qi, Ying Shan, Xiaohu Qie, and Yinqiang Zheng. Masactrl: Tuning-free mutual self-attention control for consistent image synthesis and editing. In Proceedings of the IEEE/CVF international conference on computer vision , pages 22560-22570, 2023.

Lan Chen, Qi Mao, Yuchao Gu, and Mike Zheng Shou. Edit transfer: Learning image editing via vision in-context relations. arXiv preprint arXiv:2503.13327 , 2025.

Xi Chen, Yutong Feng, Mengting Chen, Yiyang Wang, Shilong Zhang, Yu Liu, Yujun Shen, and Hengshuang Zhao. Zero-shot image editing with reference imitation. Advances in Neural Information Processing Systems , 37:84010-84032, 2024.

Xi Chen, Zhifei Zhang, He Zhang, Yuqian Zhou, Soo Ye Kim, Qing Liu, Yijun Li, Jianming Zhang, Nanxuan Zhao, Yilin Wang, et al. Unireal: Universal image generation and editing via learning realworld dynamics. In Proceedings of the Computer Vision and Pattern Recognition Conference , pages 12501-12511, 2025.

Hai Ci, Pei Yang, Yiren Song, and Mike Zheng Shou. Ringid: Rethinking tree-ring watermarking for enhanced multi-key identification. In European Conference on Computer Vision , pages 338-354. Springer, 2024.

Peng Dai, Xin Yu, Lan Ma, Baoheng Zhang, Jia Li, Wenbo Li, Jiajun Shen, and Xiaojuan Qi. Video demoireing with relation-based temporal consistency. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022.

Pronay Debnath, Usafa Akther Rifa, Busra Kamal Rafa, Ali Haider Talukder Akib, and Md Aminur Rahman. Celeb-fbi: A benchmark dataset on human full body images and age, gender, height and weight estimation using deep learning approach. arXiv preprint arXiv:2407.03486 , 2024.

Ross Ellison. Kpop idol faces. Kaggle, 2020. https://www.kaggle.com/datasets/rossellison/ kpop-idol-faces .

Egor Ershov, Alexey Savchik, Illya Semenkov, Nikola Bani´ c, Alexander Belokopytov, Daria Senshina, Karlo Košˇ cevi´ c, Marko Subaši´ c, and Sven Lonˇ cari´ c. The cube++ illumination estimation dataset. IEEE Access , 8:227511-227527, 2020.

Hao Feng, Wengang Zhou, Jiajun Deng, Yuechen Wang, and Houqiang Li. Geometric representation learning for document image rectification. In Proceedings of the European Conference on Computer Vision (ECCV) , 2022.

Rohit Gandikota, Joanna Materzy´ nska, Tingrui Zhou, Antonio Torralba, and David Bau. Concept sliders: Lora adaptors for precise control in diffusion models. In European Conference on Computer Vision , pages 172-188. Springer, 2024.

Hailong Guo, Bohan Zeng, Yiren Song, Wentao Zhang, Chuang Zhang, and Jiaming Liu. Any2anytryon: Leveraging adaptive position embeddings for versatile virtual clothing tasks. arXiv preprint arXiv:2501.15891 , 2025.

Yun Guo, Xueyao Xiao, Yi Chang, Shumin Deng, and Luxin Yan. From sky to the ground: A large-scale benchmark and simple baseline towards real rain removal. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , pages 12097-12107, October 2023.

Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-toprompt image editing with cross attention control. arXiv preprint arXiv:2208.01626 , 2022.

Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems , 30, 2017.

Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural information processing systems , 33:6840-6851, 2020.

Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, Weizhu Chen, et al. Lora: Low-rank adaptation of large language models. ICLR , 1(2):3, 2022.

Shijie Huang, Yiren Song, Yuxuan Zhang, Hailong Guo, Xueyin Wang, Mike Zheng Shou, and Jiaming Liu. Photodoodle: Learning artistic image editing from few-shot pairwise data. arXiv preprint arXiv:2502.14397 , 2025.

Yuxin Jiang, Yuchao Gu, Yiren Song, Ivor Tsang, and Mike Zheng Shou. Personalized vision via visual in-context learning. arXiv preprint arXiv:2509.25172 , 2025.

Xuan Ju, Ailing Zeng, Chenchen Zhao, Jianan Wang, Lei Zhang, and Qiang Xu. Humansd: A native skeleton-guided diffusion model for human image generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 15988-15998, 2023.

Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, and Michal Irani. Imagic: Text-based real image editing with diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 6007-6017, 2023.

Diederik P Kingma, Max Welling, et al. Auto-encoding variational bayes, 2013.

Yuheng Li, Haotian Liu, Qingyang Wu, Fangzhou Mu, Jianwei Yang, Jianfeng Gao, Chunyuan Li, and Yong Jae Lee. Gligen: Open-set grounded text-to-image generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 22511-22521, 2023.

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. arXiv preprint arXiv:2504.07960 , 2025.

Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision , pages 3730-3738, 2015.

Runnan Lu, Yuxuan Zhang, Jiaming Liu, Haofan Wang, and Yiren Song. Easytext: Controllable diffusion transformer for multilingual text rendering. arXiv preprint arXiv:2505.24417 , 2025.

Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, and Laurens Van Der Maaten. Exploring the limits of weakly supervised pretraining. In Proceedings of the European conference on computer vision (ECCV) , pages 181-196, 2018.

Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073 , 2021.

Chong Mou, Xintao Wang, Liangbin Xie, Yanze Wu, Jian Zhang, Zhongang Qi, and Ying Shan. T2iadapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. In Proceedings of the AAAI conference on artificial intelligence , volume 38, pages 4296-4304, 2024.

Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee. Deep multi-scale convolutional neural network for dynamic scene deblurring. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pages 3883-3891, 2017.

OpenAI. Gpt-4o technical report. https://openai.com/index/gpt-4o , May 2024. Accessed: 202505-14.

Zexu Pan, Zhaojie Luo, Jichen Yang, and Haizhou Li. Multi-modal attention for speech emotion recognition. arXiv preprint arXiv:2009.04107 , 2020.

William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF international conference on computer vision , pages 4195-4205, 2023.

Eduardo Pérez-Pellitero, Sibi Catley-Chandar, Ales Leonardis, and Radu Timofte. Ntire 2021 challenge on high dynamic range imaging: Dataset, methods and results. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 691-700, 2021.

Xavier Soria Poma, Edgar Riba, and Angel Sappa. Dense extreme inception network: Towards a robust cnn model for edge detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , pages 1923-1932, 2020.

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning , pages 8748-8763. PmLR, 2021.

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. arXiv preprint arXiv:2103.00020 , 2021.

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 10684-10695, 2022.

Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 22500-22510, 2023.

Thomas Schöps, Johannes L. Schönberger, Silvano Galliani, Torsten Sattler, Konrad Schindler, Marc Pollefeys, and Andreas Geiger. A multi-view stereo benchmark with high-resolution images and multicamera videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017.

Jinwoo Seo, Soora Choi, Eungyeom Ha, Beomjune Kim, and Dongbin Na. New benchmarks for asian facial recognition tasks: Face classification with large foundation models. arXiv preprint arXiv:2310.09756 , 2023.

Wenda Shi, Yiren Song, Zihan Rao, Dengming Zhang, Jiaming Liu, and Xingxing Zou. Wordcon: Word-level typography control in scene text rendering. arXiv preprint arXiv:2506.21276 , 2025.

Wenda Shi, Yiren Song, Dengming Zhang, Jiaming Liu, and Xingxing Zou. Fonts: Text rendering with typography and style controls. arXiv preprint arXiv:2412.00136 , 2024.

Wensong Song, Hong Jiang, Zongxing Yang, Ruijie Quan, and Yi Yang. Insert anything: Image insertion via in-context editing in dit. arXiv preprint arXiv:2504.15009 , 2025.

Yiren Song, Danze Chen, and Mike Zheng Shou. Layertracer: Cognitive-aligned layered svg synthesis via diffusion transformer. arXiv preprint arXiv:2502.01105 , 2025.

Yiren Song, Shijie Huang, Chen Yao, Xiaojun Ye, Hai Ci, Jiaming Liu, Yuxuan Zhang, and Mike Zheng Shou. Processpainter: Learn painting process from sequence data. arXiv preprint arXiv:2406.06062 , 2024.

Yiren Song, Cheng Liu, and Mike Zheng Shou. Makeanything: Harnessing diffusion transformers for multi-domain procedural sequence generation. arXiv preprint arXiv:2502.01572 , 2025.

Yiren Song, Cheng Liu, and Mike Zheng Shou. Omniconsistency: Learning style-agnostic consistency from paired stylization data. arXiv preprint arXiv:2505.18445 , 2025.

Yiren Song, Xiaokang Liu, and Mike Zheng Shou. Diffsim: Taming diffusion models for evaluating visual similarity. arXiv preprint arXiv:2412.14580 , 2024.

Yiren Song, Shengtao Lou, Xiaokang Liu, Hai Ci, Pei Yang, Jiaming Liu, and Mike Zheng Shou. Anti-reference: Universal and immediate defense against reference-based generation. arXiv preprint arXiv:2412.05980 , 2024.

Zhenxiong Tan, Songhua Liu, Xingyi Yang, Qiaochu Xue, and Xinchao Wang. Ominicontrol: Minimal and universal control for diffusion transformer. arXiv preprint arXiv:2411.15098 , 2024.

Cong Wan, Xiangyang Luo, Zijian Cai, Yiren Song, Yunlong Zhao, Yifan Bai, Yuhang He, and Yihong Gong. Grid: Visual layout generation. arXiv preprint arXiv:2412.10718 , 2024.

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. arXiv preprint arXiv:2411.04746 , 2024.

Xinlong Wang, Wen Wang, Yue Cao, Chunhua Shen, and Tiejun Huang. Images speak in images: A generalist painter for in-context visual learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 6830-6839, 2023.

Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing , 13(4):600-612, 2004.

Zitong Wang, Hang Zhao, Qianyu Zhou, Xuequan Lu, Xiangtai Li, and Yiren Song. Diffdecompose: Layer-wise decomposition of alpha-composited images via diffusion transformers. arXiv preprint arXiv:2505.21541 , 2025.

Sam Witteveen and Martin Andrews. Investigating prompt engineering in diffusion models. arXiv preprint arXiv:2211.15462 , 2022.

Xingqian Xu, Zhangyang Wang, Gong Zhang, Kai Wang, and Humphrey Shi. Versatile diffusion: Text, images and variations all in one diffusion model. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 7754-7765, 2023.

Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, and Fang Wen. Paint by example: Exemplar-based image editing with diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 18381-18391, 2023.

Yifan Yang, Houwen Peng, Yifei Shen, Yuqing Yang, Han Hu, Lili Qiu, Hideki Koike, et al. Imagebrush: Learning visual in-context instructions for exemplar-based image manipulation. Advances in Neural Information Processing Systems , 36:48723-48743, 2023.

Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer. Sigmoid loss for language image pre-training. In Proceedings of the IEEE/CVF international conference on computer vision , pages 11975-11986, 2023.

Gong Zhang, Kihyuk Sohn, Meera Hahn, Humphrey Shi, and Irfan Essa. Finestyle: Fine-grained controllable style personalization for text-to-image models. Advances in Neural Information Processing Systems , 37:52937-52961, 2024.

Lvmin Zhang and Maneesh Agrawala. Transparent image layer diffusion using latent transparency. arXiv preprint arXiv:2402.17113 , 2024.

Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF international conference on computer vision , pages 3836-3847, 2023.

Yuxuan Zhang, Yiren Song, Jiaming Liu, Rui Wang, Jinpeng Yu, Hao Tang, Huaxia Li, Xu Tang, Yao Hu, Han Pan, et al. Ssr-encoder: Encoding selective subject representation for subject-driven generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 8069-8078, 2024.

Yuxuan Zhang, Yiren Song, Jinpeng Yu, Han Pan, and Zhongliang Jing. Fast personalized text to image synthesis with attention injection. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages 6195-6199. IEEE, 2024.

Yuxuan Zhang, Lifu Wei, Qing Zhang, Yiren Song, Jiaming Liu, Huaxia Li, Xu Tang, Yao Hu, and Haibo Zhao. Stable-makeup: When real-world makeup transfer meets diffusion model. arXiv preprint arXiv:2403.07764 , 2024.

Yuxuan Zhang, Qing Zhang, Yiren Song, and Jiaming Liu. Stable-hair: Real-world hair transfer via diffusion model. arXiv preprint arXiv:2407.14078 , 2024.

Le Zhuo, Ruoyi Du, Han Xiao, Yangguang Li, Dongyang Liu, Rongjie Huang, Wenze Liu, Lirui Zhao, Fu-Yun Wang, Zhanyu Ma, et al. Lumina-next: Making lumina-t2x stronger and faster with next-dit. arXiv preprint arXiv:2406.18583 , 2024.

NeurIPS Paper Checklist

1. Claims

Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?

Answer: [Yes]

Justification: The abstract and introduction clearly summarize all major contributions, including the RelationAdapter, In-Context Editor with positional encoding cloning, and the large-scale Relation252K dataset (Section 1).

Guidelines:

The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: Section 6 discusses two key limitations: inconsistent text rendering in generated images and sensitivity to rare editing tasks.

Guidelines:

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.

The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.

The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.

The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.

If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.

While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

3. Theory assumptions and proofs

Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [NA]

Justification: The paper focuses on model design and empirical evaluation without including any theoretical results or proofs.

Guidelines:

The answer NA means that the paper does not include theoretical results.

All the theorems, formulas, and proofs in the paper should be numbered and cross-referenced.

All assumptions should be clearly stated or referenced in the statement of any theorems.

The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.

Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.

Theorems and Lemmas that the proof relies upon should be properly referenced.

4. Experimental result reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: Section 4 outlines full experimental details including architecture, dataset splits, metrics, and training setup. Reproducibility is supported through clear implementation specifications, with model checkpoints and dataset planned for release.

Guidelines:

The answer NA means that the paper does not include experiments.

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.

If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.

Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.

While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example

(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.

(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [Yes]

Justification: We provide full access to the Relation252K dataset and the codebase, including training scripts, evaluation pipeline, and detailed instructions for reproducing all experimental results. The links and setup instructions are included in the supplemental material.

Guidelines:

The answer NA means that paper does not include experiments requiring code.

Please see the NeurIPS code and data submission guidelines ( https://nips.cc/public/ guides/CodeSubmissionPolicy ) for more details.

While we encourage the release of code and data, we understand that this might not be possible, so 'No' is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).

The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines ( https://nips.cc/public/ guides/CodeSubmissionPolicy ) for more details.

The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.

The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.

At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).

Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.

6. Experimental setting/details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: Section 4 details all experimental settings, including model initialization, training configurations, data splits, evaluation metrics, and baseline procedures, sufficient to understand and interpret the results.

Guidelines:

The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment statistical significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [Yes]

Justification: Section 5 presents Table 3, which reports results as mean ± standard deviation across task groups, with task counts (n) provided. These reflect variability within tasks and support the robustness of our conclusions.

Guidelines:

The answer NA means that the paper does not include experiments.

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)

The assumptions made should be given (e.g., Normally distributed errors).

It should be clear whether the error bar is the standard deviation or the standard error of the mean.

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

8. Experiments compute resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: Section 4.1 specifies the training setup (4×H20 GPUs, 100K iterations, 50 hours), memory usage (74 GB training, 40 GB inference), and hardware details.

Guidelines:

The answer NA means that the paper does not include experiments.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code of ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: We have reviewed the NeurIPS Code of Ethics and confirm that the research fully complies with its principles, including data usage, fairness, transparency, and reproducibility.

Guidelines:

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).

10. Broader impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [NA]

Justification: This work focuses on foundational research in visual prompt-based image editing without any direct deployment or application.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [No]

Justification: Although safeguards are not yet implemented, we recognize that image generation models may raise concerns of misuse. We intend to accompany model release with appropriate usage instructions to promote responsible adoption.

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [Yes]

Justification: All external assets used in the paper, including FLUX.1-dev, SigLIP, and CLIP-I, are properly cited with corresponding version references. Each asset is used in compliance with its license (e.g., Apache 2.0 for SigLIP), and license terms are included in the supplemental material where applicable.

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

The authors should state which version of the asset is used and, if possible, include a URL.

The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.

If this information is not available online, the authors are encouraged to reach out to the asset's creators.

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 RelationAdapter framework. All assets are documented with details on data construction, licensing, usage instructions, and limitations. Documentation is included in the supplemental material and will be provided alongside the released assets.

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.

The paper should discuss whether and how consent was obtained from people whose asset is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

14. Crowdsourcing and research with human subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [Yes]

Justification: The paper includes a small-scale user study with volunteer participants who were not financially compensated. All participants gave informed consent, and the instructions provided to them are included in the supplemental material.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.

15. Institutional review board (IRB) approvals or equivalent for research with human subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [Yes]

Justification: The user study involved minimal risk, all participants gave informed consent, and the procedure adheres to our institution's policy, which does not require IRB approval for low-risk volunteer-based research.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.

For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.

16. Declaration of LLM usage

Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.

Answer: [NA]

Justification: We only use LLM for proof-reading.

Guidelines:

The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.

Please refer to our LLM policy ( https://neurips.cc/Conferences/2025/LLM ) for what should or should not be described.

Appendices

The Appendices provide a 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 G).

A Implementation Details

A.1 Data Annotation

We leverage the multimodal capabilities of GPT-4o to automatically generate image captions and editing instructions. Specifically, we concatenate the source image I src and the corresponding target image I tar as a single input to the GPT-4o API. A structured text prompt-illustrated in Figure 9-is provided to guide the model in producing three outputs: a concise caption for I src; a concise caption for I tar; a human-readable instruction describing the transformation from I src to I tar . Notably, the editing instruction is provided solely in textual form, without detailed descriptions of image content.

A.2 Inference Details

During inference, we set the guidance_scale to 3.5, the number of denoising steps to 24, and the attention fusion weight α 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, we specify the layout as: "4 images are organized into a grid of 2 rows and 2 columns." Before concatenating the images into the grid layout, each individual image is resized to a square region with an area of 512 × 512 pixels to 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 RelationAdapter module and directly feed the prompt image I prm and the reference image I ref into the In-Context Editor . Additionally, we apply Position Encoding Cloning to each input image to retain spatial correspondence. All other configurations are kept unchanged to ensure fair comparison.

B.2 Evaluation Details

We leverage the multimodal reasoning capabilities of GPT-4o to interpret the intended transformation from the prompt image I prm to the reference image I ref , 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, we construct composite inputs consisting of five concatenated images: the prompt image I prm , the reference image I ref (representing the desired attribute or change), the source image I src , and two generated results I pred 1 and I pred 2 . 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 10.

B.3 Perceptual Capability Evaluation

We evaluate the model'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 state-of-the-art general-purpose image generation framework, VisualCloze, using multiple evaluation metrics. Detailed results are provided in Tables 4, 5, 6, and 7.

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 VisualCloze [28] share this paired-context setup, making them the most appropriate baselines for direct comparison.

This task formulation distinguishes our method from a broad range of existing image editing and generation frameworks.

Methods such as Prompt-to-Prompt [18] and RF-Edit [57] 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-conditioned 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 to learn transferable transformation mappings. UniReal [8] addresses a multi-image compositional generation task, e.g., combining the subject from one 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 a source and an edited target image as 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-image conditioning for transformation-based editing. Its interface only distinguishes between a character reference ( -cref ) and a style reference ( -sref ), without the capability to process relational transformations between two visual exemplars. In contrast, RelationAdapter interprets an exemplar pair as a direct demonstration of the intended visual change, which constitutes a distinct learning paradigm.

For completeness, we evaluated Midjourney by assigning the source image of each exemplar pair as the -cref and the edited image as the -sref , using the following standardized prompt format:

[text prompt] --cref <source image> --sref <edited image> --cw 90 --sw 70 --v 6.1

Table 8 reports the quantitative comparison on unseen style transfer tasks. Despite Midjourney's strong generative priors, RelationAdapter consistently achieves superior performance across all evaluation metrics, indicating better perceptual consistency and transformation fidelity.

C Advantage over the In-Context Based Variant

We analyze the effectiveness and efficiency gains of RelationAdapter over the in-context variant. The incontext method required approximately 77 GB of GPU memory and 51.5 hours of training time, whereas our

RelationAdapter used around 74 GB of memory and completed training in about 48 hours , corresponding to a memory saving of roughly 3 GB and a 6.8% reduction in total training time. For inference, editing a single image at a resolution of 1024 × 1024 took over 13 seconds with the in-context method, while RelationAdapter required less than 9 seconds , achieving a 30.8% speed-up. These improvements stem from a 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 a decoupled attention mechanism that processes and fuses them more efficiently. Moreover, the same mechanism contributes to the observed gains in editing accuracy and consistency by preventing feature contamination between the exemplar pair and target image, enabling more targeted and coherent transformations. Both quantitative evaluations (Table 2) and qualitative visualizations (Figure 6) consistently confirm that RelationAdapter achieves superior perceptual fidelity and structural consistency while offering notable improvements in memory efficiency and processing speed.

D Effect of Attention Fusion Coefficient and Visual Encoder Choice

The attention fusion coefficient α controls the relative contribution between the visual prompt attention generated by the RelationAdapter and the base Multi-Modal Attention (MMA) within the Diffusion Transformer. As specified in Section 3.2, we set α = 1 during training to maintain a balanced integration between visual guidance and generative consistency, and adopt the same value during inference for training-deployment consistency. To further assess its influence, we varied α across { 0 . 5 , 1 , 2 } and report the results in Table 9. The results indicate that maintaining α = 1 yields the most stable and optimal generation performance across both seen (-S) and unseen (-U) tasks, while deviating from this setting slightly degrades fidelity and consistency.

E Effect of Model Size and Low-Rank Configuration

To assess the impact of model size on performance, we conducted an additional experiment using a lower-rank configuration in the LoRA modules. The number of trainable LoRA parameters decreases from 358.6M to 44.8M when reducing the rank from 128 to 16, corresponding to an 87.5% reduction in trainable parameters. As shown in Table 10, the proposed method remains robust and effective under this lightweight configuration, exhibiting only marginal performance degradation.

F Failure Cases

Figure 11 illustrates a set of challenging editing tasks. While the model successfully captures edit intentions in several cases, it struggles with fine-grained spatial alignment and the restoration of detailed textual elements. A future solution could involve training on higher-resolution data to better capture spatial nuances.

G Additional Results

As shown in Figures 12, 13, and 14, our method demonstrates strong performance across diverse editing tasks, effectively handling spatial transformations and capturing complex semantic modifications with high fidelity.

Text Prompts

This is a 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 a JSON object with the following fields:

'left_image_description'

'right_image_description'

'edit_instruction'

Do not include any other text or explanation.

Text Prompts

You are given a 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 to independently score B1 and B2 based on two dimensions:

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?

Edit Accuracy (1-5): Assess how accurately the editing operation applied to B (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/accurate edits. Be objective and concise in your assessment.

Return your answer in the following JSON format : { "B1": {"consistency":<1-5>, "accuracy":<1-5>}, "B2": {"consistency":<1-5>, "accuracy":<1-5>} }

Input

Result

Input

Result

口

REDFOX

BLAMERGASEM

FOREVER

JoyeRyer

TOGETHER

MASTER

OATS

BLUE

BOY'S

BLEND

®

WYOAFRUIT

CATMEAL

Input

Result

Input

Result

IBMIBM

24HOUR

24HOUR

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\nMy 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

comments: 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.

STRENGTHS AND WEAKNESSES:
### Strengths
1.  The framework is novel at the time, with a good range of performance 
2.  The In-Context Editor and positional encoding cloning enhance spatial alignment and fidelity, supporting robust zero-shot transfer to unseen tasks.
3.  Relation252K provides a diverse benchmark (218 tasks) for evaluating visual prompt-based editing, addressing gaps in task diversity and scale.
4. The ablation study is thorough.

### Weaknesses
1. There is a lack of analysis on potential failure case and rate?
2.  How well would the framework generalize to OOD examples? 
3. 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).

QUESTIONS:
1. How is this work related to LayerCraft: Enhancing Text-to-Image Generation?

LIMITATIONS:
The authors have discussed the limitations in their manuscript.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n- 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.\n\nQUESTIONS:\n1. 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\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}