Title: PairEdit: Learning Semantic Variations for Exemplar-based Image Editing
Venue: NeurIPS 2025
Accepted: yes
Keywords: Diffusion Models

Image Editing

Generative Models
Abstract: Recent advancements in text-guided image editing have achieved notable success by leveraging natural language prompts for fine-grained semantic control. However, certain editing semantics are challenging to specify precisely using textual descriptions alone. A practical alternative involves learning editing semantics from paired source-target examples. Existing exemplar-based editing methods still rely on text prompts describing the change within paired examples or learning implicit text-based editing instructions. In this paper, we introduce PairEdit, a novel visual editing method designed to effectively learn complex editing semantics from a limited number of image pairs or even a single image pair, without using any textual guidance. We propose a target noise prediction that explicitly models semantic variations within paired images through a guidance direction term. Moreover, we introduce a content-preserving noise schedule to facilitate more effective semantic learning. We also propose optimizing distinct LoRAs to disentangle the learning of semantic variations from content. Extensive qualitative and quantitative evaluations demonstrate that PairEdit successfully learns intricate semantics while significantly improving content consistency compared to baseline methods. Code is available at https://github.com/xudonmao/PairEdit.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper propose PairEdit, a novel exemplar-based image editing framework that learns semantic transformations directly from a small number of source-target image pairs without relying on any textual descriptions. The method introduces a dual-LoRA architecture, consisting of a content LoRA that reconstructs the source image and a semantic LoRA that captures the semantic variation between the paired images. By modeling the difference in noise predictions as a guidance direction, and introducing a content-preserving noise schedule, PairEdit enables stable and effective disentanglement of content and semantics. During inference, only the semantic LoRA is applied to the diffusion model, allowing for precise and continuous editing from the same noise seed while preserving content structure. The framework supports high-quality editing, continuous control via LoRA scaling, and composable edits through LoRA merging. Extensive qualitative, quantitative, and user study evaluations demonstrate that PairEdit outperforms existing exemplar-based methods in both semantic fidelity and identity preservation, despite using minimal training data.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper introduces a fully visual approach to exemplar-based editing, eliminating the need for textual prompts or language models. This is a significant advancement in editing controllability, especially in scenarios where language fails to capture nuanced visual changes. The ability to learn from 1–3 image pairs demonstrates high data efficiency and reduces reliance on large annotated datasets.\n\n2. The dual-LoRA architecture, jointly training content and semantic LoRAs on the same base model, offers a clean and modular separation of identity-preserving reconstruction and semantic transformation. This not only improves visual fidelity but also enhances interpretability and edit-specific reusability, which is both technically sound and practically beneficial.\n\n3. The proposed formulation of semantic variation as a guidance direction in noise space, inspired by classifier-free guidance, is both mathematically grounded and intuitively appealing. The introduction of a content-preserving noise schedule to stabilize this process reflects thoughtful model design and facilitates reliable training under limited supervision.\n\nWeaknesses\n\n1. Each semantic variation requires its own LoRA to be trained from scratch. There is no shared representation or meta-learning mechanism to transfer knowledge across similar edits. This limits the scalability of the approach in multi-attribute editing scenarios and makes it less practical for real-world systems with a large or open-ended set of editing intents.\n\n2. The training image pairs are either manually curated or generated through semi-automatic pipelines involving region transfer and SDEdit. This limits the automation and reproducibility of the method, and introduces a potential bottleneck when scaling to large or diverse datasets. Additionally, pair alignment quality directly affects performance, introducing variability.\n\n3. While PairEdit supports continuous control of semantic strength via LoRA scaling, the method lacks a thorough analysis of how sensitive the editing behavior is to the choice of scaling factors. There is no principled guideline for setting this parameter, and over- or under-scaling may lead to either negligible or distorted edits. This raises concerns about consistency, interpretability, and usability across different semantics.\n\nQUESTIONS:\n1. Since each semantic concept requires training a separate LoRA, could the authors comment on whether they envision a way to share or transfer knowledge across related edits (e.g., glasses and sunglasses)? Is it possible to extend the current framework to support multi-attribute or compositional learning without retraining separate LoRAs?\n\n2. The method relies on manually curated or semi-automatically generated image pairs. Could the authors clarify how scalable this pairing process is in practice? Would the model still work effectively with noisy or weakly aligned pairs, or is high-quality alignment essential?\n\n3. Continuous editing via LoRA scaling is one of the key features of PairEdit, a more thorough ablation or sensitivity analysis of scaling values across semantics would significantly strengthen the claim. Have the authors evaluated how scaling affects visual quality and identity preservation across a wider range of γ values?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

comments: SUMMARY:
This paper introduces PairEdit, a novel visual editing method that learns complex editing semantics from a limited number of image pairs (even a single pair) without relying on textual guidance.

STRENGTHS AND WEAKNESSES:
Strength
1. No Textual Guidance Needed: A key strength is that PairEdit eliminates the need for text prompts to describe editing semantics, which can be challenging to specify precisely using text alone. This makes it more practical for certain editing tasks.
2. Effective Learning from Limited Data: The method can effectively learn intricate semantics from a very small dataset, even a single image pair. This is a significant advantage over methods that require large datasets or extensive manual annotation.
3. Extensive experiments and good results: The paper has extensive quantitative and qualitative experiments and the results shows the effectiveness of the method with high fidelity.

Weakness
1. Reliance on Paired Images: The most significant limitation explicitly stated by the authors is its "reliance on paired images, which is not directly applicable to unpaired datasets". This limits its applicability to scenarios where such pairs can be readily obtained. Especially for real-world images captured by phones.
2. Limited backbones tested: The method is tested only with the FLUX.1-dev and LoRA adapters; its effectiveness on other diffusion models or fine-tuning schemes remains untested.

QUESTIONS:
1. I am wondering if exemplar-based image editing has some real-world application scenarios? 
2. I am also wondering the effect of proposed method on other backbones.

LIMITATIONS:
The proposed method requires an image pair to complete the editing process, which may limit its generalization ability.
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-03

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces PairEdit, a novel visual editing method that learns complex editing semantics from paired source-target image examples without relying on textual descriptions. Unlike existing exemplar-based methods that depend on text prompts, PairEdit utilizes target noise prediction to explicitly model semantic changes between paired images through a guidance direction term. It also employs a content-preserving noise schedule to enhance semantic learning, and optimizes distinct LoRAs to separate semantic variation learning from content retention. Extensive evaluations show that PairEdit effectively captures intricate semantics while greatly improving content consistency compared to existing methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The proposed method is remarkably efficient, requiring only a few image pairs or even a single pair for training. This efficiency significantly reduces the amount of data necessary for effective model training, which is a notable advantage in resource-limited scenarios.\n- It delivers strong performance results, with the generated images being particularly impressive in terms of quality and fine-grained detail. This highlights the method's capability to produce high-quality edits that are visually striking.\n- The approach is both innovative and well-founded, addressing a clear gap in the field with a novel solution. The methodology is logically structured and motivated, making it a significant contribution to the area of image editing.\n\nWeaknesses:\n- To provide a more comprehensive evaluation, it would be beneficial to include examples of less successful cases. This would help illustrate the method's limitations and identify potential areas for further improvement or refinement.\n- The evaluation of the proposed method is currently limited to the FLUX.1-dev model. Testing on additional models would help verify the general applicability and robustness of the method across different scenarios and datasets.\n- The quantitative comparisons are primarily based on LPIPS and CLIP distance metrics. Expanding the range of evaluation metrics could provide a more thorough assessment of the method's performance, capturing various aspects of image quality and semantic coherence beyond these two metrics.\n\nQUESTIONS:\nSee weaknesses.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper tackles the problem of exemplar-based image editing. It takes in several images pairs of the same editing as the exemplar and will output the same editing given new images. It is in a test-time finetuning manner with two sets of learnable parameters:  Content LoRA for identity keeping and semantic LoRA for semantic of the edits. It proposes a CFG suitable for the two sets of LoRA and reformulates the CFG formula customized for the rectified flow setting. Also, it reset the temporal scheduling for content preservation purposes.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The paper is easy to follow and well written.\n\n2. The algorithm looks promising because the performance seems better than previous methods. (though unclear to me)\n\n3. The authors have mentioned the limitation.\n\nWeakness\n\n1.\tComparison unfair. The comparison methods use SD-1.5 backbone, which is weaker than Flux1-dev that this paper uses, and VISII & Analogist are designed for zero training, which has different speed and computes requirement than this paper. Therefore, those comparison methods do not provide enough information for me to judge the significance of this paper. I think the closest comparison method is Pair Customization, which also use Two LoRAs (content + style) as this paper did. I think the most fair comparison is to compare with Pair Customization with the same backbone and same num of training example.\n\n2.\tThe idea wise is lacking novelty. LoRA for content and semantic is similar to Pair Customization, and the temporal scheduling seems common in the diffusion-based image editing model.\n\n3.\tThe task setting is less common. Given three pairs of the images with the same edit. It is hard to find the same edits of more than one pairs. Do the glass of the three exemplar pairs in Fig. 1 need to be the same look? If yes, it is hard to get those editing pairs in my view. So the practical use is only for one exemplar pair, which is less effective.\n\nQUESTIONS:\n1. I think the most fair comparison is to compare with Pair Customization with the same backbone and same num of training example (suggesting 1 training pair for practice)\n\n2. If the editing task given complex context should be moved to MLLM structure for better study since they have longer context window and better contexts understanding capability?\n\nLIMITATIONS:\nSee weakness.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-07"}