PairEdit: Learning Semantic Variations for Exemplar-based Image Editing
Authors: Haoguang Lu
Jiacheng Chen
Zhenguo Yang
Aurele Tohokantche Gnanha
Fu Lee Wang
Li Qing
Xudong Mao
Venue: NeurIPS 2025 poster ()
Keywords: Diffusion Models
Image Editing
Generative Models
Primary area: deep_learning

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.

PairEdit: Learning Semantic Variations for Exemplar-based Image Editing
Haoguang Lu
Jiacheng Chen'
Zhenguo Yang? Aurele Tohokantche Gnanha² 3 Qing Li Guangdoog University of Technology
Fu Lee Wang
Xudong Mao
Sun Yat-sen University
3 Huawei Noah's Ark Laboratory *Hong Kong Metropolitan University
Thh Hong Kong Polytechnic University
Abstract
Recent advancements in text-guided image editing have achieved notable success by leveraging natural language prompts for fine-grained semantic control. How- ever, 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 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 target noise prediction that explicitly models semantic variations within paired images through a guidance direction term. Moreover, we introduce content- content-preserving preserving noise schedule facilitate more effective semantic learning. We also propose optimizing distinet 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 available at https: gi thub. com wwwpo//doooooooooooooo// Peto//aairiittt
Source
Target
Original
Edited
Original
Edited
Original
Edited
Source
Target
Original
Edited
Original
Edited +
Source
Target
Original
Edited
Source
Target
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Edited
Figure 1: Editing results of PairEdit trained on three image pairs (1st-2nd rows) single image pair (3rd row). Our method effectively captures semantic variations between source and target images.
"Conesponding author kxudong.xdmook (oddmmm@gmill.ommm gmail.com).
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
48 have significantly improved the quality and diversity of visual outputs, particularly in text-to-image synthesis tasks. The versatility of diffusion- based frameworks has further expanded their applicability beyond image generation into sophisticated image editing domains. Notably, text-guided editing has emerged as powerful method, enabling
Recent advancements in diffusion models 23
fine-grained control over semantic attributes through natural language prompts 122 42) Additionally,
diffusion models have been effectively employed in image-guided editing tasks [67 10), facilitating
the transformation of visual inputs guided by reference images, and in instructional editing tasks [ [9]. allowing intuitive edits through explicit instructions.
Among these, text-guided image editing has achieved remarkable success, enabling precise and flexible editing. Nevertheless, certain editing semanties are challenging to specify clearly through textual descriptions alone. A practical alternative involves learning semantics directly from paired Staggs--coniise of before-and-afit editing examples. However, existing exemplar-ban based editing methods typically rely on large language models or manual efforts to provide text prompts describing the change from source to target images [21 9 or require encoding the change into the latent space
of pre-trained instructional editing models [41 [6]]. Notably, Concept Slider [18] introduces loss
function designed to train a single LoRA [24] with opposing scaling factors (positive and negative) to capture semantic variations by compelling predictions of identical noise. However, illustrated in Figure 3 this method still struggles with learning complex semantics and maintaining content consistency between original and edited images.
In this paper, we introduce PairEdit, novel visual editing method capable of effectively learning complex semantics from small set of image pairs or even from a single image pair, without using any textual guidance. We explore optimizing LoRA to capture semantic variations between source and target images. To this end, we introduce a guidance-based noise prediction for LoRA optimization, explicitly modeling semantic variations by converting paired images into guidance direction (i.e., Starget Escurce). ). Furthermore, we propose content content-preserving noise schedule designed to align the guidance scale with the LoRA scaling factor, enabling more effective semantic learning.
To disentangle semantic variation from content within paired images, we propose separating their learning processes by jointly optimizing two distinct LoRA modules: content LoRA and a semantic LoRA. This optimization strategy encourages the content LoRA to reconstruct the source image while guiding the semantic LoRA to capture semantic variations from source target images.
Our approach facilitates visual image editing based on a limited number of paired examples, effec- tively learning various semantics such as appearance change, age progression, and stylistic trans- formation. Moreover, our approach enables continuous control over the semantics by adjusting the scaling factor of the learned semantic LoRA. We demonstrate the effectiveness our method through comprehensive qualitative and quantitative evaluations against several state-of- of-the-art methods. The results show that PairEdit achieves superior performance in terms of both identity preservation and semantic fidelity compared to existing baselines.
Related Work
Text-to-Image Diffusion Models. Diffusion models 54 57 23
have emerged as a dominant paradigm in text-to-image synthesis, which progressively refines Gaussian noise into high-quality images. In particular, latent diffusion models [48] employ U-Net architectures [49 to efficiently denoise compressed latent spaces, setting the stage for notable improvements resolution and
scalability, Recent developments [14] 32) have initiated shift from U-Net to vision transformer- based architectures, known as Diffusion Transformers (DiTs) 45 These models utilize global attention mechanisms and advanced positional encodings to enhance model capacity and performance.
These DiT-based diffusion models, such as Flux [32] and Stable Diffusion [[5], have consistently
demonstrated state-of- tatee--f-the-eee the--hhrtt art generation quality, with performance scaling predictably with model
size. Moreover, flow-matching objectives [33 34 have further enhanced the generation quality of
these DiT-based models. Leveraging these advancements, Flux has achieved remarkable success in
various applications such as image editing [3 50 61, personalized generation
16 29 66 and
reference image generation [25] 37

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Image Editing. Generative adversarial networks 20 38 have been extensively studied in the context of image editing by leveraging their expressive latent spaces 77 [2]. Recently, diffusion models, known for their superior capabilities in text-to-image generation, have attracted significant attention in image editing. Various input conditions have been investigated in diffusion- based image editing methods, as reviewed in [26]. Among these, text-guided image editing has achieved great success, offering an intuitive and flexible way for users to describe desired edits. This category includes approaches utilizzig either descriptive texts for the edited image [22] 31 30 43
600 6 44
or explicit editing instructions [5] 53
17 27]. Additionally, some methods employ masks as
input conditions to achieve precise control 669
12 63 [7]]. while others utilize reference images
to guide the editing [70] 68, 58
10
Another notable approach involves learning semantics
directly from paired examples
64
18
Exemplar-based Image Editing. Exemplar-basee image editing has emerged as a powerful
paradigm in image editing, effectively leveraging paired examples rather than relying on explicit textual instructions. MAE-VQGAN B] first poses this problem as an image inpainting task, a
framework that has since been adopted by several approaches [59] 64 336 (1). Alternative techniques
utilize CoetrolNet-based architectures (70), as demonstrated by methods such as InstructGlE [40] and PromptDiffusion (65), which treat example images spatial conditions. Other approaches build on the generalization capabilities of InstructPix2Pix [5] by inverting visual instructions into textual embeddings 4T or into LoRA weights [56]. Pair Customization (28) explicitly learns separate LoRAs for style and content within an image pair. Concept Slider [18] introduces a loss function that encourages single LoRA with opposing scaling factors (positive and negative) to capture semantic variations by constraining them to predict the same noise. Despite these advancements, existing methods still face significant challenges in learning complex editing semantics from paired examples while maintaining content consistency between the original and edited images.
3 Method
3.1
Preliminaries
Rectified-Flov Models. Our approach is based the Flux model, type of rectified-flow for text-to-image generation. Rectified-flow models define a transition from a Gaussian noise distribution P1 to the real data distribution Pu- Given empirical observations from two distributions Zg ~ Pu. 21 ~ P1, and E [0, 1]. the forward process of rectified- models is modeled continuous path:
model 33 35
(1 t) + ttc, c~ 1))
To reverse this process and recover data from noise, a velocity prediction network up is trained to predict the velocity v of the flow. This network can serve as a noise prediction network eg using the reparameterization technique introduced in [14)
Classifier-Free Guidance. Classifier-Free Guidance (CFG) is technique introduced improve the quality and controllability of samples generated by diffusion models without requiring an external classifier. CFG leverages the same prediction network eœ both conditional and unconditional modes. During sampling, predictions from the conditional model and the unconditional model are combined using a guidance scale y.
Eg (a Ø) + +7 (Co( co Ø))
The term c0( v) ea ( 2) is often referred to the guidance direction. Our approach aims to construct a guidance direction corresponding to the target semantic variation observed within the paired images.
3.2 Learning Semantic Variations with PairEdit
Our goal is to learn semantic variations from a small set of image pairs. The key challenge lies in extracting accurate semantics that generalize well to editing new images. We introduce novel LoRA-based method enabling precise and continuous image editing using only a few image pairs or even single pair. Our method is based on three main ideas. First, propose guidance-based
3

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Training
Source image Inference
Original image
LoRA
Pretrained
Content LoRA
LoRA
Semantic LoRA
Target image.
Edited Image
Figure 2: Overview of EairEdit. (Left) Given a pair of source and target images, we jointly train two LoRAs: a content LoRA, which reconstructs the source image using the standard diffusion loss (Eq. 3), and semantic LoRA, which captures the semantic difference between the paired images using the proposed semantic loss (Eq. TO). (Right) During inference, when applying the learned semantic LoRA, the original image edited towards the target semantic.
noise prediction that helps LoRA learn semantic variations from source to target images. Second, we introduce a content pantent-preserving noise schedule for more effective semantic learning. Third, we propose separating semantic variation from content within image pairs by using two distinct LoRA adapters. An overview of the proposed PairEdit framework is depicted in Figure 2
Separating Semantic Variation and Content. Our approach leverages the fact that paired images share the same content but differ only in target semantics. Inspired by recent studies in image stylization [28] (), we jointly optimize two distinct LoRAs: a content LoRA, which reconstructs the source image, and a semantic LoRA, which captures semantic differences between source and target images. As illustrated in Figure 2 given the noised source image as input, the content LoRA aims to reconstruct the source image, while the semantic LoRA transforms the noised source image into the target image. Formally, we denote the content and semantic LoRA weights as 0g and 0,, respectively. For the content LoRA, we employ a standard diffusion loss for reconstruction:
Ceontent
Egg. .oot
[lleo
co. (x7 2)
where zó and xz" denote the original and noised source images, respectively. For the semantic LoRA, however, we cannot simply rely the reconstruction loss, as it involves denoising the noised source image toward the target image. Therefore, we explicitly model the semantic variation using a guidance direction term.
Guidance-based Semantic Variation. As illustrated at the bottom of Figure 2 the content and semantic LoRAs to denoise the noised source image toward the target image. To achieve this, the predicted noise by these two LoRAs should incorporate the semantic variation from source to target images. Inspired by CFG (Eq, ( we propose encoding the semantic variation into the CFG guidance direction. Thus, the target prediction noise e for content and semantic LoRAs defined as:
we jointly leverage
=
(EE; Ø) c (2A, 2)),
where 0* denotes the "ground truth" weights for content and semantic LoRAs, and y controls the strength of the guidance. In this equation, the first term Egg corresponds to content reconstruction, while the guidance direction term EØ. Eg- corresponds to semantic variation. For simplicity, denote cæz (at Ø) and co: (mr. Ø) as ct and el respectively. Note that the first term cØ: can be replaced with the true noise eg added to the source image. Thus, we reformulate Eq. as:
- co + 7
At 1( Atca) - (x - Atc!)).
(5)
Applying the denoising formula (i.e., Ht-At source image towards the target image, we derive:
Ale), and considering that ef denoises the
cu + At (2i-st x
(6)

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4 Utilizing Eq. 1 and applying identical noise to both source and target images, we obtain - At)(2 x²)), yielding:
E" co 2 Al (1-
x
(7)
Here, the weight 2 (1 (1-t++ + At) is time-dependent. However, in practice. is beneficial establish fixed weight aligned with constant scaling factor of LoRA during optimization. To address this issue, we introduce new noise schedule designed to make the weight of #5 z ieneinndependen..
Content-Preserving Noise Schedule. To achieve a time- iieeiindependent weight for x/) 20 we propose a new noise schedule defined as:
T2 Io + tte,
(8)
where 3 controls the strength of the noise. Compared to the standard noise schedule (Eq. D. our method preserves content information when 1: hence, we refer as the content-preserve noise schedule. Using this schedule, we derive 27-2: 2 = z - z when applying identical
noise to 2d and x.p Consequently, the target noise prediction becomes:
Ben + 7((0" =0));
where 1) Σe
As illustrated at the bottom of Figure 2 our semantic variation loss encourages the predicted noise eBe, by content and semantic LoRAs towards the target noise E", which is defined as:
Lsemantic Bag.af.ron .rost [lle* EB_.. (zt Ø 2
(10)
important note that we optimize only the semantic LoRA weights with this loss, stopping gradient flow to the content LoRA weights. The benefits of our content-preservie noise schedule are two-fold. First, we set fixed 1) aligned with a constant scaling factor of the semantic LoRA, which stabilizes the training process. Second, for large values, our method preserves content information, resulting in meaningful semantic differences 2i-si xf-se (Eq, 6) In contrast, with the standard noise schedule, -Ã- -xfAt hecomes meaningless as both a. and of approach pure noise.
Although our noise schedule differs from the original one of the pretrained diffusion model, the pretrained model already has a general capability to handle and effectively denoise noisy inputs. Furthermore, LoRA can adapt the model's s existing knowledge to this new noising approach. During inference, we follow the approach [39 18 by disabling the semantic LoRA for the initial steps to maintain content structure. Thus, the semantic LoRA does not need to learn denoising from purely noisy inputs.
Our full objective is:
0- arg min Ceontent + AC 0,,0,
(11)
where À controls the strength of the semantic loss.
4 Experiments
4.1 Implementation and Evaluation Setup
Implementation Details. Our implementation is based on the publicly available FLUX.1-der with both model weights and text encoders frozen. The rank of LoRA weights is set to 16. The parameter 3 set to for global editing semantics (e.g., stylization) and for local editing semantics (c.g., smile). For all experiments, 1) and À are set to 4 and 1, respectively. We jointly train content and semantic LoRAs for 500 steps using a learning rate of 10-3 3 The entire training process takes approximately 8 minutes on single NVIDIA A100 80GB GPU. Following [39 [8]. we set the LoRA scaling factor to during the initial 14 steps to maintain the structure of the original image. Additional implementation details for our method and baseline methods are provided in Appendix A
5

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Source
Target
Original
VISI
Transfer
Slider
Ours
Figure 3: Qualitative comparison. We present exemplar- based image editing results of our method and three baseline methods, including VISII [41]. Transfer [9], and Slider [8]. Our method demon- strates superior performance in accurately editing the original image while preserving its content.
Datasets. We create paired source and target images follows: First, apply existing image editing techniques, such as SDEdit [39], to translate source images into preliminary target images. Next, we transfer edited regions from the preliminary target images onto the corresponding regions of source images, generating the final target images. Additionally, some image pairs are collected from the web sourced from (28). For semantie learning, PairEdit is trained using either three image pairs (e.g., age, chubbiness, and elf ears) or a single image pair (e.g., stylization, lipstick, and dragon eyes).
Evaluation Setup. We compare our method with four exemplar-based editing methods: VISII 41, Edit Transfer [9] Pair Customization (28] and Visual Concept Slider [18], as well as two text-based editing methods that support continuous editing: SDEdit [39] and Textual Concept Slider [88. For quantitative evaluation, assess each method across four distinct semantics: age, smile, chubbiness,

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Source
Target
Source
Target
Source
Target
Source
Target
Original
PairCustom
Original
PairCustom
Original
PairCustom
Original
PairCustom
Original
Ours
Original
Ours
Original
Ours
Original
Ours
E
Figure 4: Qualitative comparison to Pair Customization [28]
As the official implementation of Pain
Customization produces different outputs than the official FLUX. 1-dev model for the same seed, use different original images for comparison.
Original
Age
Original
Age
Original
Chubby
Original
Chubby
Original
Beard +
Original
Beard
Figure 5: Examples of continuous editing by our method. By adjusting the scaling factor of the learned LoRA, our method enables high-fidelity and fine-grained control over the semantic from exemplar images.
and glasses. For each semantic, generate 500 pairs of original and edited images using the same random seed across all methods.
4.2 Results
Qualitative Evaluation. Figures and 4 present visual comparisons of editing results between our method and the baselines. As the official implementation of Pair Customization (28) produces different outputs than the official FLUX. model for the same seed, we use different original
images for comparison in Figure 4 We examine various editing tasks, including facial feature transformation, appearance alteration, and accessory addition. As shown, VISII [41] struggles to accurately capture semantic variations between source and target images, generating low low-quality results. Edit Transfer [9] fails to capture semantic variations and produces images nearly identical to the original. Concept Slider [18] captures some semantic variations but consistently fails to preserve the identity of the original image. also struggles with complex semanties (e.g., elf ears and chubbiness) and exhibits limited generalization capability (e.g., adding glasses to dogs). Pair Customization (8 fails to capture complex semantics and to preserve consistency with the original image. In contrast, PairEdit successfully performs all desired edits learned from paired examples.

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Real image
Reconst.
Age
Elf car
Eye size
Chubby
Glasses
Figure 6: Real image editing. The reconstructed image is obtained by optimizing LoRA over the real image. We apply the learned semantic LoRAs to the reconstructed image by merging the LoRAs during inference.
Table 1: Quantitative comparison. We evaluate each method by measuring identity preservation when performing similar editing magnitude. Identity preservation is measured using LPIPS distance, and editing magnitude is measured using the cosine similarity over CLIP embeddings.
SDEdit
Textual Slider
Visual Slider
Ours
Semantics
CLIP
LPIPS
CLIP
LPIPS
CLIP
LPIPS
CLIP
LPIPS..
Age
0.2285
0.1956
0.2266
0.1631
0.2257
0.1716
0.2382
0.1359
Smile
0,2533
0.1419
0.2556
0.1749
0.2724
0.1380
0.2896
0.1120
Chubbiness
0,2347
0.2173
0.2332
0.1423
0.2329
0.1747
0.2420
0.0815
Glasses
0.2419
0.1370
0.2427
0.1602
0.2421
0.1706
0.2886
0.0911
Moreover, method achieves high-quality continuous editing by adjusting the scaling factor of the learned semantic LoRA, as illustrated in Figure Additional qualitative evaluations are provided in Appendix B and we also present visual comparison of editing results using single image pair in Appendix
Quantitative Evaluation. For quantitative assessment, compare method with three baselines that support continuous editing. We evaluate each method by measuring identity preservation while maintaining similar editing magnitude. To ensure valid editing for the baselines, employ a of simple semanties for evaluation. Identity preservation is quantified using the L.IPS distance [71] between the original and edited images, while editing magnitude is measured via cosine similarity between CLIP embeddings [47] of the edited images and their corresponding textual editing de- scriptions. As demonstrated in Table [ our method achieves significantly lower LPIPS distances compared to the baselines when applying comparable editing magnitudes. Additionally, we present DINO 7 results in Appendix
User Study. We also conducted user study to evaluate our method. In each question, participants were shown pair of source and target images, an original image, and two edited images: one pro- duced by our method and the other by baseline method. Participants were asked to select the image exhibiting superior editing quality while preserv- ing the original identity. A total 720 responses were collected from 24 participants, as detailed in Table [ The results clearly indicate a strong pref- erence for our method.
Table 2: User Study. Participants were asked to select the image exhibiting superior editing quality while preserving the identity.
Baselines
Prefer Baseline Prefer Ours
VISII 41
6.2%
93.8% %
Analogist
21
1.3%
98.7%
Slider 18
3.8%
96.2 %
8

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Original
+ Age
+
Smile
+ Lipstick
Glasses
Ear shape
+ Eye color
Figure 9: Composing sequential edits. Our method effectively composes different edits while preserving the original identity, Multiple semantic LoRAs are merged using the strategy illustrated in Eq.
Real Image Editing. Editing real images typically involves finding an initial noise vector that reconstructs the input image using inversion techniques [33 [T] However, we observe that directly applying existing inversion methods designed for Flux 51 with the learned semantic LoRA yields poor editing quality. This issue arises because these inversion methods fail to accurately map the input image back into Flux's ' original latent space, limitation also highlighted in [13]. Since inversion methods are not the primary focus of this paper, we adopt a simple reconstruction strategy by optimizing LoRA over the input image. To apply learned edits to reconstructed images, we merge the two LoRAs during inference as follows:
(12) where 0,, and denote the reconstruction and semantic LoRA weights, respectively, and Treal is to 0.75. We empirically find that this merging strategy improves identity preservation compared linear combination of LoRA weights, as illustrated Appendix E As shown in Figure (6] our approach achieves high-quality editing while effectively preserving the identity of real images.
Co, (2t, Ø) Treat (Co, (Ie, Ø) co, (xr, Ø)),
Composing Sequential Edits. Our method supports combining multiple edits through the merging of several learned semantic LoRAs. We employ the same merging strategy during inference as in real image editing, resulting in better editing quality compared to linear combination of LoRA weights. As illustrated in Figure 9 our method effectively composes multiple edits while preserving individual identities.
Weakly Aligned Image Pairs. For weakly aligned image pairs, our model can still learn semantic variations. However, it may cap- ture additional unintended semantics, as it cannot explicitly identify which semantics are targeted when only 1-3 pairs are used for training. High-quality image pairs lead to improved generation quality, particularly in terms of identity preservation. Figure shows example trained weakly aligned image pairs. The edited image exhibits inconsistencies in certain aspects, such as background and hair appearance.
Source
Target
Original
Edited
Figure 7: Weakly aligned pairs.
Knowledge Transfer Across Different Ed- its. In Figure we demonstrate the transfer of learned LoRA (adding glasses) dif- ferent edit (increasing eye size). The model successfully learns the new edit while sig- nificantly reducing optimization steps from 500 to 50. This demonstrates that knowledge encoded in learned LoR As can be effectively reused to accelerate the learning of related edits.
steps
Original
50 steps
500 steps
Transferred
Figure 8: Learning "increasing eye size" by transfer-
ring from a learned "adding glasses" LoRA.

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Source
Target
Original
Variant A
Variant
Variant C
Ours
Figure 10: Ablation study. We evaluate three variants of our model: (A) replacing the semantic loss with the visual concept loss proposed in (18), (B) removing the content LoRA, and (C) replacing the content-preserving noise schedule with standard noise schedule.
4.3 Ablation Study
In this section, perform ablation study assess the effectiveness of individual components within our framework. Specifically, we evaluate three variants: (1) replacing the semantic loss with the visual concept loss proposed in (18], (2) removing the content LoRA, and (3) replacing the content- preserving noise schedule with standard noise schedule. Figure I0 presents a visual comparison of editing results generated by each variant. The results demonstrate that all proposed components are crucial for achieving identity preservation and semantic fidelity. Omitting our semantic loss significantly reduces the model's ability to capture complex editing semantics. Removing the content LoRA leads to inconsistent results, such as unintended fur color changes in the first row and hairstyle alterations the second row. Employing standard noise schedule negatively affects the generalization capability of the semantic LoRA, causing blurred glasses in the first row and inconsistent ear coloration in the second row. Additional ablation study results are provided in Appendix G
Conclusions and Limitations
In this paper, we introduced PairEdit, a novel visual editing framework designed to effectively capture complex semantic variations from limited paired-image examples. Utilizing a guidance-bass based target denoising prediction term, our method explicitly transforms semantic differences between source and target images into guidance direction. By separately optimizing two dedicated LoRAs for semantic variation and content reconstruction, PairEdit effectively disentangles semantic attributes from content information. However, limitation of PairEdit is its reliance on paired images, which not directly applicable to unpaired datasets. In future work, we aim to explore methods capable of extracting editing semantics from unpaired image sets, further enhancing the flexibility and practical applicability of our approach.
Acknowledgments
This work is supported by National Natural Science Foundation of China (No. 62176223 and No. 62302535), Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515012897 and Zhuhai Basic and Applied Basic Research Foundation (No. 2320004002745).
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A Implementation Details.
Our method leverages FLUX.I-dew with both model weights and text encoders fixed. We employ the Adam optimizer to tune the LoRA weights, setting the rank to 16. The content and semantic LoRAs are jointly trained for 500 steps using learning rate of 2 x 10-3, For all experiments, perform image generation with 28 inference steps. To preserve the structure of the original image, follow the approach described in (39 [8] setting the LoRA scaling factor to 0 for the initial 14 steps. For Textual Concept Slider (88) we utilize their official Flux implementation. For Visual Concept Slider [8]] due to the unavailability of the official Flux implementation,
implement the Flux-based model following their SDXL implementation. For other baseline methods, including 2 and Edit Transfer 9 utilize their official implementations and follow the hyperparameters described in their papers. For SDEdit [39], we use the diffusers Flux implementation. When using GPT-4o, the editing prompt is: "The first and second images represent
VISII A1. Analogist
a 'bffore and after' editing pair. Please analyze the changes made between them and apply the same edit the third image."
B Additional Qualitative Results
In Figure TT we present additional qualitative comparisons against three baseline methods: Edit Transfer [9]. GPT-4o, and Visual Concept Slider [18]. Our method demonstrates superior performance in terms of identity preservation and semantic fidelity compared to the baselines. Edit Transfer struggles to accurately capture the semantic variations between source and target images. GPT-4o shows poor identity preservation, and Visual Concept Slider also fails to preserve the original identity while struggling with complex semantic edits.
C Additional Quantitative Results
In addition the CLIP and LPIPS metrics, we present results using the DINO metric in Table Our method achieves superior performance compared to the baselines in terms of DINO score.
D Additional Real Image Editing Results
In Figure 12 we provide additional examples of real image editing. Reconstructed images are obtained by optimizing LoRAs directly over real images. We apply the learned semantic LoRAs to these reconstructed images using guidance-based LoRA fusion as described in Eq. [2] The results demonstrate the effectiveness our approach in editing real images across various semantic attributes.
E Comparison of LoRA Fusion Methods
B compare two LoRA fusion methods: (1) linear combination of LoRA weights and (2) guidance- saidance-based LoRA fusion (Eq. [2]. The linear combination approach tends to produce blurry outputs for certain semantics, whereas the guidance-based LoRA fusion provides better identity preservation and image quality.
In Figure
F Learning with a Single Image Pair
In this section, we evaluate PairEdit when trained using only a single image pair. As shown in Figure [4] our method outperforms baseline methods both identity preservation and semantic fidelity. However, we observe that providing multiple image pairs further helps the model learn complex semanties and enhances its generalization capability (e.g.. adding glasses dogs).
G Additional Ablation Study
As discussed in Section 4
we evaluate three variants of our model: (1) replacing the semantic loss with the visual concept loss from [18), (2) removing the content LoRA, and (3) substituting the
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content- preserving noise schedule with standard noise schedule. Additional results of the ablation study are presented in Figure
H
Training Data
For the paired training samples, the target image is created by coarsely copying the target region into the source image. Even though the pasted target images often have obvious artifacts around the boundaries, we find that model is able effectively learn the semantic edits while ignoring these inconsistencies. In Figure 16 we present examples of our training data. Our model trained using either three image pairs (e.g., elf ears, glasses, and chubbiness) or single image pair (e.g.. stylization, dragon eyes, and lipstick).
I Implementation with SDXL
In this section, evaluate the performance our model with SDXL 446 in Figure 17] the model remains capable of learning meaningful editing semantics. However, its performance not as strong with FLUX in terms identity preservation and editing quality. This performance gap stems from differences in the mathematical modeling of the backbone models our approach theoretically grounded in the noise formulation (Eq. I used in FLUX from flow- matching perspective, whereas SDXL. employs DDPM 23 noise schedule.
the backbone, As shown
J Failure Cases
When the source and target images exhibit significant structural differences, our method may struggle to capture the semantic variations. For example, illustrated in Figure when a person's pose changes from arms hanging naturally to arms crossed over the chest, our model cannot learn this transformation. Such transformations exceed the intended scope of method.
K
User Study
As described in Section 4.2 we conducted user study to evaluate our method against the basclines. Figure 19 shows an example question from the user study. Given a pair of source and target images, an original image, and two edited images: one produced by our method and the other by baseline method. Participants were asked select the image exhibiting superior editing quality while preserving the original identity. The results are presented in Table
L Societal Impact
Similar to existing image editing techniques, our approach enables users to effectively edit images by optimizing LoRA weights of large-scale pre-trained diffusion models. By allowing individuals to manipulate images using their own data, this method supports a wide range of applications, such as novel content generation and artistic creation. Despite these positive outcomes, the use of generative models also introduces risks, including the creation misleading or false information. To address these concerns, it is essential advance reliable detection methods for distinguishing real images from synthetic ones 62
M Licenses for Pre-trained Models and Datasets
Our implementation is based on the publicly available FLUX. 1-dev, which is licensed under the FLUX. I-dev Non-Commercial License. Most of the images used for evaluation are created using FLUX.1 1 dev and SDEdit 39] Some image pairs are collected from the web sourced from [28 The license information for these images is not available online.
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Source
Target
Original
Transfer
GPT-4o
Slider
Ours
Figure 11: Additional qualitative comparison. We present exemplar- image editing results from our method and three baseline methods: Edit Transfer 19, GPT-4o, and Slider [18], Our method demonstrates superior performance accurately editing the original image while preserving its content.
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Real image
Reconst.
Smile
Eye size
Chubby
Elf
Lipstick
Figure 12: Additional real image editing results. The reconstructed image is obtained by optimizing a LoRA on the real image. We apply the learned semantic LoRAs to the reconstructed image by merging the LoRAs during inference.
Real image
Linear Comb.
Ours
Real image
Linear Comb.
Ours
Eli ear
Smile
Age
Figure 13: Comparison of two LoRA fusion methods: (1) linear combination of LoRA weights and (2) guidance-based LoRA fusion (Eq, [2]. Guidance-basen LoRA fusion achieves better identity preservation, whereas linear combination of LoRA weights tends to generate blurry images for certain semantics.
25

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Source
Target
Original
VISI
Analogist
Slider
Ours
Figure 14: Comparison of PairEdit with three baseline methods under a single-image training setting. Our method demonstrates superior performance in both identity preservation and semantic fidelity.
Source
Target
Original
Variant A
Variant
Variant C
Ours
Figure 15: Additional ablation study results. We evaluate three variants of our model: (A) replacing the semantic loss with the visual concept loss proposed in [88, (B) removing the content LoRA, and (C) replacing the content-preserving noise schedule with a standard noise schedule.
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Table 3: Quantitative comparison using the DINO metric
DINO
SDEdit
Textual Slider
Visual Slider
Ours
Chubbiness
0.8420
0,9152
0,8882
0.9588
Glasses
0.8408
0,8951
0.8810
0.9065
Smile
0.9086
0.8853
0,9143
0.9150
Age
0.8597
0.8875
0.8564
0.8885
Source
Target
Source
Target
Source
Target
Target
Figure 16: Examples of training data. Our model trained using either three image pairs (e.g., elf ears, glasses, and chubbiness) or single image pair (e.g., stylization, dragon eyes, and lipstick).
Source
Target
Original
Edited
Original
Edited
Figure 17: Results of our model with SDXI. 46
as the backbone.
Source
Target
Original
Edited
Original
Edited
Figure 18: Failure examples. When the source and target images exhibit significant structural differences, method may struggle capture the semantic variations.
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?
First Row of images: Observe the editing effects from the left image to the right image (highlighted in the red box).
Our is apply editing effect from the first the image the second
Please choose image below achieves superior editing quality while preserving the original identity.
O
O
An example question from the user study. Given a pair of source and target images, along with an original image and two edited images, participants were asked to select the image that demonstrated superior editing quality while preserving the original identity.
Figure 19:
28


Reviews:
[{"review_id": "hf3TporiJz", "reviewer": "Reviewer_gUZ4", "summary": "This 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.", "questions": "1. 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?", "limitations": "See weakness.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "addressed my concern.", "rebuttal": "We thank Reviewer gUZ4 for the insightful feedback! We are encouraged that the reviewer finds our method promising and our paper well-written.\n\nBelow, we address the reviewer’s concerns point by point. \n\n> **W1 & Q1: The comparison methods use the SD-1.5 backbone, which is weaker than FLUX.1-dev used in this paper. VISII & Analogist are designed for zero training, which has different speed and computation requirements compared to this paper. I think the fairest comparison is to compare with Pair Customization using the same backbone and the same number of training examples (e.g., one training pair for practice).**\n\nThank you for the suggestion. We address this concern through the following points:\n\n**1. On the fairness of using FLUX.1-dev for baselines:**\n\nWe would like to clarify that two of the main baselines in our paper—Slider [1] and Edit Transfer [2]—are both implemented using the same FLUX.1-dev backbone as our method. Note that the comparison with Edit Transfer [2] is presented in the Appendix (Figure 8).\n\n**2. On including VISII and Analogist as baselines:**\n\nDue to the limited number of exemplar-based image editing methods with open-source implementations, we included VISII and Analogist for broader context. For VISII, we would like to clarify that it is not a zero-training method. It optimizes semantic edits, represented by the source and target images, into the textual instruction space of InstructPix2Pix. The VISII paper states that it requires training for 1,000 steps per target semantic. For reference, our method takes approximately 8 minutes for optimization, while VISII takes approximately 10 minutes. Regarding Analogist, we acknowledge that it is a training-free method. Following the reviewer’s suggestion, we will replace Analogist in Figure 3 with another optimization-based method, Edit Transfer [2]. Note that the comparison with Edit Transfer is already included in the Appendix (Figure 8).\n\n**3. On adding Pair Customization as a baseline:**\n\nThank you for suggesting Pair Customization as a baseline. Following your recommendation, we compared our method with Pair Customization using the same FLUX.1-dev backbone and trained both methods with only one image pair. For simple edits (e.g., \"smile\"), Pair Customization can learn the editing semantics but significantly alters the identity of the source image. For complex edits (e.g., nose shape change in the second row of Figure 3), Pair Customization fails to learn the editing semantics. It is also important to note that Pair Customization requires a text prompt as auxiliary input, whereas our method is fully prompt-free, relying only on visual exemplars. As we cannot upload visual results in the rebuttal, we present quantitative results below, showing that our method achieves significantly lower LPIPS distances compared to Pair Customization. We will include the visual comparisons in the revised version of the paper. \n\nMoreover, the insights of our method and Pair Customization are fundamentally different. Pair Customization adopts a content/style LoRA architecture to learn the style of the reference image, while our method introduces a guidance-based semantic variation loss to directly learn the semantic variation between the source and target images. For a more detailed discussion of the differences, please refer to our response to W2 below.\n\n|   | LPIPS (PairCustom) ↓ |   LPIPS (Ours, single pair) ↓ | CLIP (PairCustom) ↑ |CLIP (Ours, single pair) ↑ | \n| -| - |-| - |-|\n|  Smile \t\t\t|  0.1964 | 0.1014  |  0.2542 |  0.2849 |\n| Chubbiness \t| 0.2270   |0.0767   | 0.2281  | 0.2365  |\n| Glasses   \t  \t| 0.1597  | 0.1076  | 0.2367  |\t0.2826 |\n|  Age       \t\t| 0.1154   | 0.0828  | 0.2199  |0.2234|\n\n> **W2: LoRA for content and semantic is similar to PairCustom. The temporal scheduling seems common in diffusion-based image editing models.**\n\nWe would like to clarify the misunderstanding regarding the insights and key contributions of our work. The content/semantic LoRA module and temporal noise scheduling strategy are NOT the key contributions of our work. Our ablation study (Figure 7) demonstrates that removing either the content/semantic LoRA module or the proposed noise scheduling (Eq. 8) still enables the model to learn semantic edits effectively. In fact, the content/semantic LoRA module is used to enhance content-semantic disentanglement, while the noise scheduling improves generalization capability. Additionally, we have acknowledged (Line 122 of our paper) that the design of the content/semantic LoRA module is inspired by Pair Customization.\n\nThe primary contribution of our work is the guidance-based semantic variation loss, formally derived from Eq. (4) to Eq. (10). This loss explicitly models the semantic variation between source and target images using a guidance-based direction term. As shown in our ablation study (Figure 7), this loss, rather than the content/semantic LoRA module, enables effective learning of the editing semantics exampled by the paired images. To the best of our knowledge, our method is the first to accurately learn complex and fine-grained semantic edits from a single exemplar image pair.\n\n\n> **W3: Given three image pairs with the same edit, it's difficult to find the same edits for more than one pair. Do the glasses in the three exemplar pairs in Fig. 1 need to be identical? If yes, it is hard to get those image pairs in my view. So the practical use is only for one exemplar pair, which is less effective.**\n\nThank you for raising this point. We would like to clarify that the glasses in the three exemplar pairs shown in Figure 1 do not need to be identical; indeed, we used different glasses for this example. Zooming in on Figure 1 shows variations in shape of the glasses. We will include all training pairs in the revised paper.\n\nRegarding the number of training pairs, our method can learn meaningful edits from a single exemplar pair for all semantic edits presented in the paper. Section E in the Appendix (Figure 11) presents comparisons using only one image pair. However, multiple pairs improve our model's generalization capability. For instance, in the “glasses” example, using three image pairs allows generalization of the “add glasses” edit even to a dog image, difficult with only one pair.\n\nWe appreciate the reviewer’s suggestion, and will highlight these clarifications explicitly in the revised paper.\n\n> **Q1: I think the fairest comparison is to compare with Pair Customization with the same backbone and same num of training example (suggesting 1 training pair for practice)**\n\nPlease refer to our detailed response to \"W1 & Q1\" above.\n\n> **Q2: For editing tasks involving complex context, should the method be moved to an MLLM structure for better study since they have longer context window and better contexts understanding capability?**\n\nThank you for this insightful question. We agree that MLLM-based image editing methods excel in tasks needing complex contextual understanding. However, our work specifically targets exemplar-based image editing with FLUX, defining edits visually through before-and-after image pairs rather than textual prompts or multimodal context. This setting is complementary to the task of MLLM-based editing and is particularly effective when visual editing goals (e.g., very specific fine-grained regional changes) are difficult to describe using text. We believe both tasks are valuable and complementary in practice. Exploring integration with MLLMs is a promising direction we plan for future work.\n\nAdditionally, Figure 8 in the Appendix compares our method with GPT-4o, which supports MLLM-based image editing, showing that our method achieves superior identity preservation and editing quality.\n\n**References**\n\n[1] Gandikota et. al. \"Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models\". ECCV, 2024.\n\n[2] Chen et. al. \"Edit Transfer: Learning Image Editing via Vision In-Context Relations\". arXiv:2503.13327, 2025."}, {"review_id": "P2XoVzN6fT", "reviewer": "Reviewer_aUji", "summary": "This 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.", "questions": "See weaknesses.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "Thanks for the rebuttal. I keep my score to accept this paper.", "rebuttal": "We thank Reviewer aUji for the insightful feedback! We are encouraged that the reviewer finds our method innovative, well-founded, and efficient. We are pleased the reviewer recognizes the strong performance of our model.\n\nBelow, we address the reviewer’s concerns point by point. \n\n> **W1: To provide a more comprehensive evaluation, it would be beneficial to include examples of less successful cases.**\n\nThank you for this helpful suggestion. We agree that including failure cases is important for a more comprehensive and transparent evaluation. Indeed, our method does have less successful cases, particularly when the source and target images involve dramatic structural changes. For instance, edits involving a person transitioning from standing to jumping, or transforming a cat into a dog, often exceed the intended scope of our method. Due to rebuttal constraints, we are unable to upload visual examples of less successful cases here. However, we will add a dedicated section in the revised paper to analyze and illustrate these less successful cases.\n\n\n> **W2: The proposed method is evaluated using 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\nWe appreciate the reviewer’s suggestion. We built our model upon FLUX.1-dev because it is one of the current state-of-the-art open-source image generation models. Following the reviewer’s recommendation, we additionally tested our method with SDXL as the backbone. We found that the model remains capable of learning meaningful editing semantics. However, its performance is not as strong as with FLUX in terms of identity preservation and editing quality. We hypothesize that this performance gap stems from differences in the mathematical modeling of the backbone models—our approach is theoretically grounded in the noise formulation (Eq. 1) used in FLUX from a flow-matching perspective, whereas SDXL employs a DDPM noise schedule. As we cannot upload images in the rebuttal, we will include the results using SDXL in the revised version of the paper.\n\n> **W3: 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.**\n\nThank you for this valuable suggestion. We agree that using multiple metrics provides a more thorough evaluation. In the current version of the paper, we followed Slider [1] by employing LPIPS and CLIP metrics for quantitative evaluation. Following the reviewer’s advice, we have conducted additional quantitative experiments using the DINO metric [2], which has been shown to effectively capture semantic similarity and structural consistency in vision tasks. The results are presented in the table below. As demonstrated, our method consistently outperforms the baselines under the DINO metric, further validating the effectiveness of our approach. We thank the reviewer for this suggestion and will definitely include these new results in the revised paper.\n\n| DINO Score ↑  | SDEdit  | Textual Slider | Visual Slider | Ours | \n| -| - | -| -|-|\n| Chubbiness |   0.8420   |  0.9152   |  0.8882   |  **0.9588**   |  \n| Glasses |  0.8408    |  0.8951 | 0.8810  |  **0.9065** |\n| Smile |  0.9086    |  0.8853  \t|  0.9143 |  **0.915** |\n| Age |  0.8597  |  0.8875  |  0.8564 |  **0.8885** |\n \n**References**\n\n[1] Gandikota et. al. \"Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models\". ECCV, 2024.\n\n[2] Caron et. al. \"Emerging properties in self-supervised vision transformers\". ICCV, 2021."}, {"review_id": "DPYlnSXGxQ", "reviewer": "Reviewer_ahKQ", "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.", "questions": "1. I am wondering if exemplar-based image editing has some real-world application scenarios? \n2. 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.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength\n1. 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.\n2. 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.\n3. 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.\n\nWeakness\n1. 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.\n2. 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.", "was_revised": true, "final_justification": "The rebuttal adequately addressed the main concerns.\n\nReliance on paired images: The authors clarified that creating such pairs is feasible in practice and provided examples where text-based editing would fall short. While the method still cannot operate in unpaired settings, the clarification helps justify the relevance of their task setup.\n\nLimited backbones tested: The authors added results using SDXL, which demonstrates some degree of generalization. Although the performance is not as strong as with FLUX, the explanation is reasonable and the new results will be included in the revision.\n\nReal-world applicability: The authors provided concrete use cases (e.g., fine-grained facial edits), which support the practical motivation of exemplar-based editing.\n\nThat said, the scope of the method is still somewhat narrow, and the generalization beyond the studied setting remains limited. Given that, I am maintaining my score at 4.", "rebuttal": "We thank Reviewer ahKQ for the insightful feedback! We are encouraged that the reviewer finds our results to be good, and recognizes the strength of eliminating textual guidance and effective learning from limited data.\n\nBelow, we address the reviewer’s concerns point by point. \n\n> **W1: 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.** \n\nIndeed, we stated in the limitations section that our method relies on paired images. However, we would like to clarify that generating training image pairs is actually quite convenient in practice. We simply copy the target region coarsely into the source image. Even though the pasted target image often exhibits obvious artifacts around the boundary, we find that the model effectively learns the semantic edits while ignoring these artifacts.\n\nMoreover, although our method relies on paired images, this task setting is meaningful and applicable to real-world scenarios. For example, users may want to change the nose shape to a very specific target shape that cannot be precisely described using text alone. Our method is well-suited for such fine-grained, visually guided edits. In fact, the second row of Figure 3 (i.e., the example of changing the nose shape) illustrates a real case taken from a GitHub issue for Slider [1], submitted by a user seeking assistance with exemplar image pairs.\n\nNote that existing exemplar-based editing methods, such as Slider [1] and Edit Transfer [2], also rely on paired images. To the best of our knowledge, there are currently no methods capable of performing attribute-specific visual edits in an unpaired setting. Developing models capable of learning from unpaired data remains an open challenge in this domain, and it is one of the key directions we intend to pursue in future work.\n\n> **W2 & Q2: 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.**\n\nWe appreciate the reviewer’s suggestion. We built our model upon FLUX.1-dev because it is one of the current state-of-the-art open-source image generation models. Following the reviewer’s recommendation, we additionally tested our method with SDXL as the backbone. We found that the model remains capable of learning meaningful editing semantics. However, its performance is not as strong as with FLUX in terms of identity preservation and editing quality. We hypothesize that this performance gap stems from differences in the mathematical modeling of the backbone models—our approach is theoretically grounded in the noise formulation (Eq. 1) used in FLUX from a flow-matching perspective, whereas SDXL employs a DDPM noise schedule. As we cannot upload images in the rebuttal, we will include the results using SDXL in the revised version of the paper. Regarding the use of LoRA adapters, we adopted them due to their lightweight nature and fine-tuning efficiency. LoRA allows us to adapt the backbone with a relatively small number of trainable parameters, which is especially important when training on limited paired data. It also helps maintain the general generation capabilities of the pretrained model and is compatible with other learned LoRAs available in the community.\n\n\n> **Q1: What are the real-world application scenarios for exemplar-based image editing?**\n\nExemplar-based image editing is particularly valuable in real-world scenarios where users have a clear and specific visual goal but cannot accurately describe it using text. For example, users may want to modify specific facial attributes (e.g., nose shape or ear shape) to match a very specific target look. These edits are often difficult to describe precisely with text but can be easily conveyed through exemplar image pairs. In fact, the second row of Figure 3 in our paper demonstrates a real-world case from a GitHub issue of Slider [1], submitted by a user seeking assistance with exemplar image pairs—a case that could not be solved by Slider but is successfully handled by our method. In addition to facial editing, exemplar-based image editing may also apply to design-related scenarios, such as character and product design, where designers frequently need to adjust visual appearances to match very specific target looks.\n\n> **Q2: The effect of the proposed method on other backbones.**\n\nPlease refer to our detailed response to \"W2 & Q2\".\n\n\n\n**References**\n\n[1] Gandikota et. al. \"Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models\". ECCV, 2024.\n\n[2] Chen et. al. \"Edit Transfer: Learning Image Editing via Vision In-Context Relations\". arXiv:2503.13327, 2025."}, {"review_id": "rQTcub5d29", "reviewer": "Reviewer_1DPG", "summary": "This 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.", "questions": "1. 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?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "The rebuttal satisfactorily addressed the key issues I raised. Although some fundamental limitation exists due to the pipeline of the method, the paper makes a valuable contribution. I’m keeping my original score.", "rebuttal": "We thank Reviewer 1DPG for the insightful feedback! We are encouraged that the reviewer finds our method to be both mathematically grounded and intuitively appealing. We are pleased that the reviewer recognizes the strength of eliminating textual prompts and the high data efficiency of our method.\n\nBelow, we address the reviewer’s concerns point by point. \n\n> **W1 & Q1: Each semantic variation requires its own LoRA to be trained from scratch. Is it possible to transfer knowledge across similar edits? Is it possible to support multi-attribute learning without retraining separate LoRAs?**\n\nThank you for the suggestion. We tested transferring a learned LoRA (e.g., glasses) to another edit (e.g., sunglasses or even eye size). It successfully learns the new edits and reduces optimization steps from 500 to 50. This demonstrates that the knowledge of the learned LoRAs can be effectively reused to accelerate the learning of related edits, showing strong transferability and generalization capabilities. We appreciate the reviewer’s valuable suggestion and will definitely include these results. As we cannot upload images in the rebuttal, we will include these new results in the revised paper.\n\nRegarding multi-attribute learning, one approach is to use image pairs with multi-attribute variations, allowing our model to simultaneously learn these variations. Another approach is to apply the suggested transfer learning method to accelerate the multi-attribute learning process. Additionally, as described in L242 and Figure 6, our method supports multi-attribute edits by merging the learned LoRAs.\n\n\n> **W2 & Q2: The training image pairs are manually curated or generated through semi-automatic pipelines. This limits the automation and reproducibility of the method. Could the authors clarify how scalable this pairing process is in practice? Would the model work effectively with noisy or weakly aligned pairs, or is high-quality alignment essential?**\n\nWe acknowledge that the current training image pairs rely on semi-automatic pipelines. However, we would like to clarify that generating training image pairs is actually quite convenient in practice. We simply copy the target region coarsely into the source image. Even though the pasted target images often have obvious artifacts around the boundaries, we find that the model is able to effectively learn the semantic edits while ignoring these inconsistencies.\n\nFor weakly aligned pairs, our model can still learn semantic variations. However, it may learn additional unintended semantics, as it does not explicitly know which semantics are targeted given that only 1–3 pairs are used for training. High-quality image pairs result in improved quality of generations, particularly regarding identity preservation. As we cannot upload images in the rebuttal, we will include results using weakly aligned pairs in the revised paper.\n\nMoreover, although our method relies on paired images, we would like to clarify that this task setting is meaningful and applicable in real-world scenarios where users have a clear and specific visual goal but cannot accurately describe it using text. For example, users may want to modify specific facial attributes (e.g., nose shape or ear shape) to match a very specific target look. These edits are often difficult to describe precisely with text but can be easily conveyed through exemplar image pairs. In fact, the second row of Figure 3 in our paper demonstrates a real-world case from a GitHub issue of Slider [1], submitted by a user seeking assistance with exemplar image pairs—a case that could not be solved by Slider but is successfully handled by our method.\n\n\n> **W3 & Q3: While PairEdit supports continuous control of semantic strength via LoRA scaling, the paper lacks a thorough analysis of how sensitive the editing behavior is to the choice of LoRA 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. Have the authors evaluated how scaling affects visual quality and identity preservation across a wider range of γ values?**\n\nThank you for pointing this out. Indeed, over- or under-scaling factors may result in negligible or distorted edits. In particular, under-scaling factors lead to negligible editing effect, while over-scaling factors may introduce artifacts or degrade image quality. According to our observations, setting the LoRA scaling factor between 0.3 and 1.0 consistently yields high-quality editing results with varying levels of editing strength. For different types of semantic editing, we typically set the scaling factor to 0.6, which empirically achieves an optimal balance between editing quality and identity preservation.\n\n**References**\n\n[1] Gandikota et. al. \"Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models\". ECCV, 2024."}]

Author rebuttal:

Meta-review: This paper addresses exemplar-based image editing: it takes multiple image pairs demonstrating the same edit as exemplars and applies that edit to new images. Using test-time finetuning, it employs two learnable parameter sets—Content LoRA (to preserve image identity) and Semantic LoRA (to capture edit semantics). The method introduces a Classifier-Free Guidance (CFG) strategy tailored to these LoRAs, reformulates the CFG formula for rectified flow settings, and resets temporal scheduling to enhance content preservation.

All the reviewers hold positive reviews and most concerns are solved. The authors need to add the rebuttal experiments into the final draft.
Decision: Accept (poster)