Mind-the-Glitch: Visual Correspondence for Detecting Inconsistencies in Subject-Driven Generation
Authors: Abdelrahman Eldesokey
Aleksandar Cvejić
Bernard Ghanem
Peter Wonka
Venue: NeurIPS 2025 spotlight ()
Keywords: Diffusion Models
Text-to-Image Generation
Semantic Correspondence
Subject-driven Generation
Personalized Generation
In-Context Generation
Primary area: deep_learning
TL;DR: We propose a pipeline for detecting visual inconsistencies using visual correspondences based on disentangled diffusion model features

Abstract: We propose a novel approach for disentangling visual and semantic features from the backbones of pre-trained diffusion models, enabling visual correspondence in a manner analogous to the well-established semantic correspondence. 
While diffusion model backbones are known to encode semantically rich features, they must also contain visual features to support their image synthesis capabilities. 
However, isolating these visual features is challenging due to the absence of annotated datasets. 
To address this, we introduce an automated pipeline that constructs image pairs with annotated semantic and visual correspondences based on existing subject-driven image generation datasets, and design a contrastive architecture to separate the two feature types. 
Leveraging the disentangled representations, we propose a new metric, Visual Semantic Matching (VSM), that quantifies visual inconsistencies in subject-driven image generation. 
Empirical results show that our approach outperforms global feature-based metrics such as CLIP, DINO, and vision--language models in quantifying visual inconsistencies while also enabling spatial localization of inconsistent regions. 
To our knowledge, this is the first method that supports both quantification and localization of inconsistencies in subject-driven generation, offering a valuable tool for advancing this task.

Mind-the-Glitch:
Visual Correspondence for Detecting Inconsistencies in Subject-Driven Generation
AbdeIrahman Eldesokey
Aleksandar Cvejic Bernard Ghanem KAUST, Saudi Arabia
Peter Wonka
first. last@kaust edu.sa
Semantic Fostures
Sewawtio Cernesprraeee
Visual Connessowdevaa
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Figure Mind-the-Glitch the first pipeline that enables computing visual correspondences based on the backbone features of pre-trained diffusion models. The pipeline separates backbone features into semantic and visual components, allowing for visually matching keypoints across images, analogous to the well-established semantic correspondence task. This provides the first empirical framework for evaluating and localizing visual inconsistencies in subject-driven image generation.
Abstract
We propose novel approach for disentangling visual and semantic features from the backbones of pre-trained diffusion models, enabling visual correspondence in a manner analogous to the well-established semantic correspondence. While diffusion model backbones are known to encode semantically rich features, they must also contain visual features to support their image synthesis capabilities. However, isolating these visual features is challenging due to the absence of anno- tated datasets. To address this, we introduce an automated pipeline that constructs image pairs with annotated semantic and visual correspondences based existing subject-driven image generation datasets, and design a contrastive architecture to separate the two feature types. Leveraging the disentangled representations, propose a new metric, Visual Semantic Matching (VSM), that quantifies visual inconsistencies in subject-driven image generation. Empirical results show that our approach outperforms global feature-hased metrics such as CLIP, DINO, and vision-language models in quantifying visual inconsistencies while also enabling spatial localization of inconsistent regions. To our knowledge, this is the first method that supports both quantification and localization of inconsistencies in subject-driven generation, offering a valuable tool for advancing this task. Project Page: https://abdo-eldeeokkk. hitpl/eeooooegiiubioiooiimmmo sishuu.io/mind-thegliiiee
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Recent advances in diffusion models have transformed the landscape of image generation, delivering exceptional quality, fidelity, and diversity 32 36 27 BO). These capabilities have revolutionized several domains, including artistic creation, advertising, content production for video games, and storyboards for movies. In many of these applications, maintaining visual consistency of subject, whether a character or an object, across different generations is crucial. For example, in cinematic and advertising workflows, preserving the identity and appearance of a subject across multiple scenes is essential for narrative coherence and branding. However, most diffusion models operate in a latent space where semantic and visual concepts entangled, making it challenging control preserve specific content using text prompts alone.
To address these limitations, growing research direction has focused on subject-driver generation, which aims to guide diffusion models to produce consistent images of a given subject in different scenes while preserving fine-grained visual details [33 28 49 Nonetheless, a major bottleneck in this line of research has been the lack of reliable evaluation metrics for subject consistency. Since subjects may appear in varying poses and spatial configurations, traditional image similarity metrics such as LPIPS [53]. or structural similarity (SSIM) are not well-suited for this task. As a result, many works have relied on feature-based similarities using models like CLIP [29] or DINO [4] as approximations of overall appearance. However, these metrics capture global semantics and tend to overlook subtle visual inconsistencies in object details 26
Recent works 41 26 have explored the use of Vision-Language Models (VLMs), such as ChatGPT, to assess consistency between reference and generated image. While these approaches are promising, they remain limited to global assessments, and it is often unclear which visual cues or criteria the models rely on to judge consistency. Furthermore, VLM-based metries lack the ability to localize inconsistent regions within the subject. To enable robust evaluation of subject-driven image generation, it is essential to develop methods that can reliably match the visual appearance of a subject across images, irrespective of variations in pose, scale, or context, and accurately localize inconsistencies for potential correction through post-procsssing
This challenge bears similarities to the task of semantic correspondence, where corresponding points matched across image pairs of objects with varying poses, scales, and contexts. Diffusion models have been shown to encode semantically-rich features that have demonstrated great success in computing semantic correspondences across images (39 42 52 22) [T]. However, as illustrated in Figure [ these semantic features are typically insensitive to appearance- variations, as they focus primarily on structural categorical semantics rather than fine-gr aineigraneed visual details. Given that diffusion models are trained for image synthesis, it is reasonable to assume that their internal representations should encode both semantic and visual information, yet only the semantic component has been extensively leveraged so far.
Building on this hypothesis, we propose a novel framework for disentangling semantic and visual features from the backbone of pre-trained diffusion models. Due to the absence of datasets with annotated visually similar or dissimilar regions of a given subject, we introduce automated dataset generation pipeline that constructs image pairs with annotated semantic and visual correspondences, derived from existing subject-driven generation datasets. Using this dataset, we propose an archi- tecture that disentangles semantic and visual features in a contrastive manner. We then leverage the disentangled representations to derive a metric, the Visual Semantic Matching (VSM) metric, that quantifies the degree of visual inconsistency in subject-driven generation. Empirical results show that approach outperforms existing feature-based metries such as CLIP and DINO, as well as the LanguneLangguage Model (VLM) metric, in quantifying visual inconsistencies, while allowing for localizing the inconsistent regions well. We believe our framework offers valuable step forward in the evaluation and development of subject-driven image generation.
Our contributions can be summarized as follows:
We propose novel framework comprising an automated dataset generation pipeline and an architecture for disentangling semantic and visual features in diffusion models.
Based on the disentangled features, we present new metric for evaluating subject-driven generation methods that both quantifies and localizes visual inconsistencies.
We empirically demonstrate that the proposed metric outperforms existing feature-based metrics commonly used in subject-driven generation.

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2
Related Work
In this section, we first provide a brief overview of the semantic cortespondence task, which serves as key inspiration for our work. Then review existing subject-driven image generation approaches, and how consistency is currently evaluated in these methods.
2.1 Semantic Correspondence using Diffusion Models
Diffusion models have been shown to encode semantically rich features within their backbones, enabling computing semantic correspondence across instances of the same object class and even among semantically similar categories. Some approaches leveraged intermediate features from the decoder of diffusion models 422 and others combine them with features from DINO 4 to enhance semantic representation [52]] Several works [22] 51 proposed learnable aggregation networks that automatically select and fuse features from different layers. trained in supervised manner using semantic correspondence datasets such as SPair-7Ik [24]. CleanDIFT [39 further introduces
a distillation framework to compress diffusion features, allowing for efficient inference. These semantically rich features have been successfully applied to range of downstream tasks, including segmentation [45 23), controllable editing 255 2 7. and object manipulation [20]. Inspired by
the task of semantic correspondence, we aim to learn feature representations that capture visual appearance rather than semantics. These visual features enable matching regions based on appearance and are well-suited for detecting visually inconsistent regions in subject-driven image generation.
2.2 Subject-Driven Image Generation
Early personalized generation techniques using UNet-based diffusion models 32] 27 primarily
focused on encoding subject identity through full-model fine-tuning (3 I7 or low-rank adaptation methods [12] 100 37 [1]], and training-firee ffeii approaches (43) Other approaches [35 g 15
learned subject-specific embeddings, without modifying model weights, by associating subjects with new tokens an image text embedding space. To improve the trade-off between efficiency
and fidelity, adapter-basee based methods [48 13 28 50 introduced zero-shot personalization by conditioning reference image features through lightweight network modules. These methods showed that injecting image-driven signals or selectively updating parameters could effectively preserve subject identity and visual details, even with limited data.
The emergence diffusion Transformers (DiT')
introduced a new class of architectures that replace UNets with Vision Transformers the denoising backbone, offering greater scalability and enhanced contextual understanding. These models support more ficxible conditioning mechanisms and exhibit strong in-context learning capabilities, enabling them to generate diverse images of subject without explicit fine-tuning [38 [8]. To further improve fidelity and identity preservation,
several works have applied LoRA-based fine-tuning DiTs architectures [4] 54 14 3), achieving
more precise control over subject appearance and consistency. More recently, visual autoregressive models (VARs) have been adopted for subject-driven generation due to their inherently strong
conditional modeling capabilities [40 47 6). For an exhaustive overview of subject-driven image
generation approaches, we refer the readers [46]
2.3 Evaluating Subject-Drivee Image Generation
Visual similarity is often measured using traditional metries such Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and LPIPS (53), which assume that the object is spatially aligned across images. However, in subject-driven generation, this assumption does not hold, as the subject may appear in different poses, positions, and contexts. As a result, it has become common use global feature-based metrics, such as CLIP-Image 29] and DINO (4, which compare the similarity of image-level embeddings extracted from different models. While these methods are robust to spatial changes, they are inherently global and often fail to capture fine -mne-grained appearance details of the subject. A recent trend involves leveraging Vision- LaiiunL Models (VLMs) to evaluate subject-driven generation by prompting them to score specific criteria of the generated images [1 26 However, it remains unclear how VLMs form their judgments, and they lack the ability to localize the source of inconsistency within the image. In this work, we aim to bridge this gap by leveraging visual features extracted from the backbones of diffusion models to assess
3

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A1'
4
R;
SAM
Inpaint
Combrelled Irspaulaneaay
Comespandence
Prompits
R2
Figure 2: Automated Dataset generation pipeline for producing controlled visual inconsistencies. Access to such pairs of images enables separating visual features from pre-trained backbones in contrastive manner.
the appearance similarity across images in subject-driven generation. Our approach enables both quantification and spatial localization of visual inconsistencies. By providing reliable metric that offers fine-ge tne-grained insight into subject consistency, we take a step toward more robust evaluation and improvement of subject-driven generation.
3 Method
In subject-driven image generation, assessing whether different parts of a subject are visually consis- tent across two images requires matching visual representations that are robust to changes in pose, scale, and environment. This challenge is similar to the semantic correspondence task, where several approaches 39
were trained on SPair-71k [24], which includes annotated semantic points between image pairs of given subjects. As demonstrated in Figure] | semantic features are insensitive to appearance changes, making them unsuitable for matching visual appearance. To the best of our knowledge, there exists dataset with annotated visual correspondences between visually similar or dissimilar regions, similar to SPair-7Ik.
22
51
52 42
To bridge this gap, we first introduce a novel automated pipeline for constructing a dataset with visual correspondences, leveraging existing datasets of subject-driven image generation such as Subjects200k [41] (Section 3.1. Using the data generated from this pipeline, we then propose an architecture for disentang ling semantic and visual representations from the internal features of diffusion models (Section 3.2) Lastly, we introduce metric that leverages the disentangled features
for empirically evaluating visual consistency between image pairs, which quantifies the degree of consistency and localizes inconsistent regions (Section 3.3
3.1 Data Generation Pipeline with Visual Correspondence
We aim to generate a dataset of image pairs with visual correspondences between visually similar and dissimilar regions inspired by the SPair-71k dataset. Given a consistent image pair Jı and 12 from subject-driven dataset, we begin by segmenting the subject in each image using Grounded-SAM 31. This isolates the subject from the background and makes the computation of correspondences more reliable. Next, we compute semantic correspondences between the two images within the segmented subject regions using the semantic correspondence method ClcanDIFT [39]. Specifically, we extract features from the sixth decoder layer of the diffusion model UNet 4; which has been shown to contain semantically rich information 42 39
resulting in F = {b(1) and F ((2)).
We then compute the pairwise similarity between the two feature maps to form a similarity matrix
D = F& 1-5" Corresponding points are obtained by selecting locations with the highest similarity scores using arg max D, to obtain the following correspondence mapping:
C = ((z). v))... (a uf
C₂ = ((x2, v2). ..... (xs) .
C.. E R (I)
where N denotes the number of correspondences.
Based on these correspondences, we aim to match semantically similar regions across both images and then alter them visually to generate pair of images with known, localized visual inconsistencies. To

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D[4]
4
12
Djk]
Skewness-1.6
Skewness=-0.5
Figure 3: Examples illustrating how the skewness of the matching scores distribution correlates with matching ambiguity. High skewness implies a distinct match, while low skewness indicates diffuse or ambiguous correspondences.
achieve this, we sample a point k from Ci that has high similarity score and use the corresponding
point pair (æf, uf) and (x/), v/2 as prompts to the SAM model (16), which segments a localized region in each image. This produces region masks defined as R, SAM(1,, (a y/)). We configure SAM to return multiple candidate masks and select the one with the smallest area, as it is more likely to correspond to an isolated semantic part rather than the entire object.
Handling Ambiguous Matches: When segmenting regions, a common challenge arises with flat- textured subjects, such whiteboard, where the selected point & tends to match broadly across the object (see Figure leading SAM to segment the entire subject rather than a localized part. To address this, we propose using the skewness of the similarity distribution D[k) to identify ambiguous matches. The skewness computed as:
a
DiK] M
3
Skewness(D(k)) (n 1)(n - 2)
where n = [D[k]]. μ = mean (Dean(D(k) [D())), and o = STD(D(k)). We observe that high skewness corre- sponds to matches in textured regions, characterized by long-tailed distribution and low ambiguity. In contrast, low skewness indicates more uniform similarity scores, suggesting ambiguous matches typically associated with flat surfaces illustrated in Figure
We apply additional heuristics to alleviate segmentation failures and to ensure that the selected regions in both images correspond to the same semantic part, including constraints on aspect ratio and relative size with respect to the full object. For regions that pass all checks, we perform inpainting using diffusion-based inpainting model (we use SDXL [27] for efficiency), ) to obtain two inconsistent images I and I/. Specifically, we crop a patch around the region with padding and feed it to the inpainting model ensure that the model does not observe the rest of the object, promoting the generation visually distinct content. To confirm that the inpainted region differs from the original, we compute the LPIPS score [53] between the original and inpainted regions in T and K. We discard samples with low LPIPS scores to ensure meaningful variation. Further details on the filtering strategy provided in the supplementary material. We provide an illustration for the data generation pipeline in Figure 2 Eventually, each dataset sample comprises consistent image pair (I,, I2), inconsistent pair (K, (4) generated with our data generation pipeline, subject masks (O,, O2), inpainted region masks (Ri, R2), subject prompt and target prompt p.
Validating Matched Regions:
3.2 Learning Disentangled Semantic and Visual Representations
Given an inconsistent image pair I and 42 which were generated with our automated pipeline in Section 3.1 where the inconsistent regions are defined by the masks RS and R2, we aim to disentangle semantic and visual features of the diffusion model backbone. We start by extracting multi-layer features for both images using the diffusion model backbone Q yielding feature maps Fi ((1)) and F3 & (72) that include features from multiple decoder layers E L. To aggregate features from the different layers, we follow the approach of (22), but instead of using single aggregation network, we employ two separate networks, V and V #. to aggregate semantic and visual representations separately. Each aggregation network encompasses a ResNet [ block per decoder layer that are
5

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Inconsistent Image Pair
Disentangled Features
V
Semantic Similarity
P2
Semantic
Aggregation
S₁
D12
CE
Ls
$2
pout
poue
CE
Lg
Vi
012
Aggregation
V2
Visual Similarity
CE
P1
P2
Residual blocks
Dot blocks
Indexing
Negation
CE
Pross-Entropy
Figure 4: Overview of the proposed architecture for disentangling semantic and visual features from frozen diffusion backbone $. Inconsistent regions between the imput image pairs are indicated by binary masks (left). The semantic branch (blue) encourages the features corresponding semantic points in both images, P1 and P2. to align. In the visual branch (yellow), we bring visually consistent points outside the inpainted regions, Pl and P closer together, while pushing apart features at inconsistent points inside the inpainted regions, Pin and Pir
combined using trainable scalar weights w':
S S.= = Σ wt V'.(F.)
V Σ w V (F.)
S, Vi E Rd
where S₁, are the semantic and visual features respectively, [1,2), d w denotes the flattened spatial dimensions, and is the feature dimensionality. This architecture allows for flexible selection of features that capture either semantic content or visual appearance. An overview of the architecture is provided in Figure
Next, we compute cyclic similarity matrices between the aggregated semantic and visual features of the two images using dot products:
Dig S1ST Di2 ViVV'"
D S2
Dig, D Rddd
(4)
D = V2V
Diz, Dh Radd
(5)
Our objective is to encourage semantic features to be similar across all point correspondences, while visual features should similar only outside the inconsistent regions R, and dissimilar within them. To achieve this, adopt contrastive learning framework that brings similar features closer and pushes dissimilar ones apart. As a first step, we partition the set of pre-computed correspondences C, from Section 3.1 into two subsets: points that fall inside and outside the inconsistent regions:
pin
(oc v!)
(f E C. (. =
(6)
P
(o/ v!)
(of vt) E C. R, v.) = 0)
(7)
Then, inspired by the contrastive objective used in CLIP 29 define semantic correspondence loss as follows:
C. = CrossEntropy(Di,,(). Pa). P; P.n U P
(8)
This semantic loss encourages matched points, whether inside or outside the inconsistent region, to exhibit similar semantic feature representations.
To disentangle appearance-specifie features, define a visual loss that explicitly separates consistent from inconsistent regions. For inconsistent regions, we use a negative similarity objective:
c CrossEntropy (T P
(9)

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This term penalizes similarity in appearance features for points known to be visually inconsistent. For consistent regions, we retain the standard contrastive loss encourage matching:
L CrossEntropy (Diz(P P2
(10)
Additionally, visually corresponding points inside the inpainted regions of the original consistent pairs 1 and 1a can be incorporated into Equation (10) to provide additional supervision: however, we omit the formal definition of this part for simplicity. All loss terms are also computed in the reverse
direction as well using 21 and D2 and each loss term is averaged over both directions. The final training objective combines semantic and visual losses as follows:
L L. a( + _
(11)
where a is a scaling factor used to prioritize the visual branch, since the semantic branch can already be extracted reliably even without any aggregation, shown in 42 39
3.3 A Metric for Evaluating Subject-Drive Image Generation
Having disentangled visual and semantic features from the backbone of pre-trained diffusion models in the previous section, we now aim to leverage these features to estimate and localize visual consistency in subject-driven image generation. Given two test images 71 and 12 generated by subject-driven generation method, our goal is to evaluate the visual consistency of the subject across the two images.
We begin by passing both images through our architecture to extract semantic and visual feature maps, denoted F and PY, respectively, following the aggregation described in Equation (3). As in the previous section, we compute pairwise similarities between features to obtain semantic and visual similarity matrices, denoted by De and D", respectively. We then take the maximum similarity score for each point to obtain D max(D") and D max(D"). representing the best per-point match in the semantic and visual domains.
Semantic correspondences predefined threshold Ts, Le., D > Ts, resulting in set of confident semantic matches indexed by J.. This filtering ensures that we only consider regions that are both visible and semantically coherent. We set T 0.7 in all experiments.
identified by selecting points whose semantic similarity exceeds
To assess visual consistency at these semantically matched locations, we use the same index set Jg to retrieve the corresponding visual similarity scores from D", Given a visual similarity threshold Te. define the Visual Semantic Match (VSM) metric
VSM(T.)
1
Σ JEJ.
8
DJ DT>TT To
(12)
T.
where 61:] is the indicator function, equal to if the condition holds and 0 otherwise.
This metric captures the proportion of semantically aligned regions that are also visually consistent, providing a quantitative measure of consistency between the two generated images. Inconsistent regions can be identified by subtracting visually matched locations from the semantically matched ones, by examining regions in Dr with low visual similarity scores.
4 Experiments
In this section, we evaluate the effectiveness of our approach and compare it against commonly used feature-based metrics such as CLIP [29] and DINO [4]. as well as VLM-based approaches (e.g. ChatGPT-4o). Then, we provide an ablation study to analyze the impact of key architectural design choices and provide further insights into the behavior of our model.
4.1 Implementation Details
We use the data generation pipeline described in Section 3.1 to construct the dataset used to train our architecture. The pipeline takes consistent image pairs from the Subjects200k dataset 41 as input, although any subject-driven generation dataset can be used.
Dataset:
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Hark
Fentunee
Commerodeecee
Fesuras
Connecontinnt
Scores
Increndeentt Cerrentto
BANNNTT Sujject
Supject
Figure 5: Qualitative examples of semantic and visual features, along with their correspondences, produced by our architecture. Regions that fall within the inpainting mask exhibit visually dissimilar features, enabling the detection of visual inconsistencies based feature similarity. Dark Red is most consistent and Yellow least consistent.
During generation, we apply several filtering steps to ensure high-quality training data. We discard samples with matching skewness below 1.3 (see Figure 3), and we enforce that the inconsistent region occupies between 5% and 60% of the subject area. Additionally, we filter out samples where the inpainted region has an LPIPS score below 0.15 to ensure sufficient visual inconsistency. The final dataset consists 5,000 image pairs for training and 500 image pairs for validation.
Testset: We generate testset of 100 samples using our dataset, and we manually revise it to ensure the reliability of evaluation.
Hyperparameters: We use the backbone of Stable Diffusion 2.1 [32 for all experiments following CleanDIFT 339. We set the spatial resolution of the aggregated features in Equation (3) to h
48, resulting in d = 48 48 2304, following DIFT 39. The projected feature dimensionality is set to 9 384, as in [22] We empirically found that setting
10 in Equation (IT) yields the best performance. We train the model for 30 epochs using the Adam W optimizer [21] with learning rate of 1e-3 that is divided by 10 every 10 epochs. The training is done A100 GPU (40GB) and takes 12 hours. The source code for the dataset generation pipeline and the proposed architecture are publicly available.
4.2 Evaluating the VSM Metric
To validate our proposed Visual-Semanica Match ( (VSM) metric, described in Section 3 we first perform controlled evaluation where define oracle based on the inpainted regions for each image pair in the test set. This oracle is defined based on the ratio of the inconsistent region R₁ to the total object mask O (see Figure 2) computed
R ΣON
Oracle 1
(13)
This oracle reflects the ground-truie truth degree of visual consistency between the image pair. An effective metric should produce estimates that strongly correlate with the oracle. We evaluate against commonly
http://ghthb..omm/bbbbbllllkyyiy

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Controlled Inconsistency
Subject-Dree Driven Generation
CLIP
DINO
VLM*
VSM (Ours)
CLIP
DINO
VLM
VSM (Ours)
Pearson
0.053
0.087
0.072
0.448
0.156
0.164
0.079
0.405
Spearman -0.005
0.120
0.091
0.582
0.112
0.146
0.073
0.369
Table 1: Average correlation scores across methods. Our VSM achieves significantly higher correla- tion with the oracle than other metrics both on controlled and realistic settings. *(ChaGPPT-4))
Reference Subject
DED (CV003325)
EasyCoo BOOV 2005E
2012 Bractech 901
EL.P
E1.P 10 U00
CLIP 18 BE3
3raclei8.07 603
Bractedl 563
731 CLEB
CL.B
10 850
CLIP
08 063
KUR
18
trackor8 905
Draclese
dhociee
CLER
01.09
19-072
CLIP 10
AIP
Sracke:9.900
Dracleco.com
Figure 6: Qualitative examples of evaluating subject-driven image generation approaches using our proposed VSM metric and other existing approaches. Our VSM metric can accurately quantify and localize inconsistency and is more consistent with the oracle. Dark Red is most consistent and Yellow is least consistent.
used metrics in the literature for comparing image pairs: CLIP and DINO image-to Image-to-imaae tomimag similarity, well ChatGPT-4o as representative Vision-Language Model (VLM). For VLM, adopt the prompt used in (26). but modify it to produce a numerical score in the range of O to 100.
We report Pearson and Spearman correlations between each evaluated metric and the oracle in Table 1 (left). Our VSM metric shows significantly higher correlation compared both feature-based simi- larity metrics and VLM judgments, indicating its greater reliability for measuring visual consistency. Qualitative results in Figure 5 iillustreee semantic and visual features with their correspondences. While semantic features align with semantically similar regions regardless of appearance, visual features differ notably within inpainted areas and do not match across image pairs. This is evident in the score heatmaps shown in the rightmost column.
4.3 Evaluating Subject-Drive -rrivnn Image Generation
To evaluate the effectiveness of our VSM metric in a realistic subject-driven image generation setting, we evaluate three recent methods: Diptych [38], DSD-Diffusion (3], and EasyControl [54]. Using the reference image 1 and target prompt p from our test set, generate 100 images per method and evaluate them using VSM and other metrics as before. To compute the oracle, we manually annotate the generated images to mark visually inconsistent regions and calculate the oracle score as defined in Equation
13)
Table shows that our VSM metric consistently outperforms all other metrics, demonstrating strong generalization to real-world subject-driven generation. We also present qualitative examples in Figure illustrating that VSM aligns most closely with the oracle. This is further supported by the KDE plots in Figure 7 where VSM shows the highest agreement with the oracle, while VLM tends to score most image pairs between 75-95 regardless of visual discrepancies.

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Compariion Score Distribtions Compared cracle
Oracie VSM (7,m0.5)
Semantic wei ghts
(7,m0.6)
DINO
0.4
Visual weights
CLIP
0.2
layers
layer7
layera
layers
layer10 10
Score
Figure 7: Score Distribution of different metrics compared to the Oracle.
Figure 8: Aggregation weights.
T. 0.5
0.7
Skewness> 1.0
Skewness>>
VSM (Ours)
Pearson
0.465
0,352
0.118
0.232
0.224
0.448
Spearman
0.454
0.496
0.104
0.250
0.225
0.582
Table 2: Ablation analysis of different hyperparameters.
4.4 Ablation Analysis
We visualize the learned aggregation weights for the semantic and visual branches in Figure Visual features are primarily derived from decoder layers 8 and 9, while semantic features draw from layers and 10, indicating that certain layers (c.g., layer 8) contribute to both representations. Notably, our learned semantic weights differ from those in [22], likely due to differences in supervision: their model is trained on sparse keypoints, whereas ours uses dense correspondences, favoring later layers with higher spatial resolution.
Table presents an ablation study of key byperparameters. For the VSM similarity threshold Te- both low (0.5) and high (0.7) values reduce correlation with the oracle, with T, = 0.6 yielding the best performance. Setting a in the training loss significantly degrades performance by reducing emphasis visual features in favor of semantic features, which are easier to learn and already present in diffusion backbones without additional training [42 39.. In the dataset pipeline, high skewness thresholds overly restrict sample diversity, while low thresholds allow ambiguous matches-bont leading to performance drops.
Limitations and Future Work
Our feature disentanglement is inherently partial, visual features may still carry semantic infor- mation. Achieving full disentanglement, enabling visual matching across semantically different objects, remains promising future direction. The quality of the learned features also depends the reference dataset used in the automated pipeline. The Subjects200k dataset [41], while large-scale, was validated automatically and may include noisy pairs. Manual curation or improved filtering could enhance supervision quality. Additionally, detecting fine-grained inconsistencies is further limited by the spatial resolution of the diffusion features. We plan address this via multi-scale aggregation and higher-resolution extraction. Lastly, our current framework targets structural and appearance-level inconsistencies. Extending handle broader variations, such as artistic style or color, may require further decomposing. We include failure cases in the supplementary to guide future work.
6 Conclusion
We proposed a framework for disentangling diffusion model features into semantic and visual com- ponents. Using automated dataset pipeline with controlled visual inconsistencies, trained contrastive architecture to separate the two. This enables visual correspondence across images, com- plementing semantic matching, and supports fine-gra fine-grained analysis. Leveraging this, we introduced a metric that quantifies and localizes inconsistencies in subject-driven generation. Our approach outperforms existing metrics, offering more reliable and interpretable evaluation framework.
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Acknowledgment
The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST)- Center of Excellence for Generative AI, under award number 5940.
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Reviews:
[{"review_id": "M8hWVDHenS", "reviewer": "Reviewer_Ke5D", "summary": "A major bottleneck in the subject-driven generation research line is the lack of reliable evaluation metrics for subject consistency. The traditional similarity metrics such as LPIPS or structural similarity (SSIM) are not well suited for this task as subjects may appear in varying poses and spatial configurations. Feature-based similarities CLIP or DINO score does not work well either as they overlook subtle visual inconsistencies in object details. This work propose a novel approach for disentangling visual and semantic features, enabling the detection of visually inconsistent regions in subject-driven image generation. To achieve such goal, they first introduce an automated pipleine for constructing a dataset with visual correspondences, leveraging existing datasets of subject-driven image generation such as Subjects200k. Then they propose an architecture for disentangling semantic and visual representations from the internal features of diffusion models. Lastly, they introduce a metric that leverages the disentangled features for empirically evaluating visual consistency between image pairs, which quantifies the degree of consistency and localizes inconsistent regions. Empirical results show that their methods achieves SOTA correlation score with oracle compared to other feature-based similarity metrics and VLM judgement.", "questions": "- Can you offer more results with large pose variations and non-rigid deformations? \n- During the inpainting process, do you see any artifects such as the clear boundary of the inpainting region? If yes, is it an obvious image cue for the network to capture during the training process and hack the final test score?\n- Is there a multiple visual inconsistence setup in the experiment? In the text, it only says the smallest region for each SAM segmentation to create the inpainting. Since in reality, there always are multiple inconsistencies in the results, how to evaluate such senarios?", "limitations": "N/A", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- It is the first approach that tackles both the semantic and visual inconsistency in subject-driven image generation. The proposal includes the dataset creation, baseline method built-up, and evalution metics, so it is a complete work.  \n- The design details of data pipeline is clever. Fexible utilization of grounded-sam, sam, skewness score, inpainting model and LPIPS score guarantees the quality of the dataset. \n- The proposed method shows significant superiority against the competitors on their own dataset, creating a good baseline solution of this topic. \n\nWeakness:\n- The novelty of the solution in learning disentangled semantic and visual representation is low.  The features are computed from a frozen diffusion network in a dual-stream design, and the learning process is to determine how to aggregate the importance of those features from different layers of diffusion model. And the work only investigates SD 2.1 structure and no other popular DiT based networks. \n- The variations of objects, poses, illumination, background, etc. in the dataset is limited due to the only data source from Subjects 200K. For example, in the qualitative exmaples offered in figure 6, we didn't see much pose variations or any other non-rigid deformations of objects. Whether the network can maintain a good semantic similarity under large variations is a question, though it can captures small visual diffierences.", "was_revised": true, "final_justification": "As authors have well addressed my major concerns, and I have read all the responses to other reviewers, I would like to lift the final rating of the proposal.", "rebuttal": "We thank the reviewer for the detailed and thoughtful feedback, and for positively highlighting key aspects of our work,  including that it is **“the first approach that tackles both the semantic and visual inconsistency,”** that our **“data pipeline is clever,”** and that our method shows **“significant superiority against the competitors.”** We greatly appreciate the recognition of the novelty and completeness of our pipeline.\n\nWe address the reviewer’s concerns and questions one by one below:\n\n---\n\n> **“The novelty of the solution in learning disentangled semantic and visual representation is low. The features are computed from a frozen diffusion network in a dual-stream design, and the learning process is to determine how to aggregate the importance of those features from different layers of diffusion model.”**\n\n&emsp; While our architecture indeed builds on the principle of feature aggregation, a widely used strategy in analyzing high-dimensional networks such as diffusion models and DINO [1,2], our contribution lies not in the aggregation mechanism, but in developing a **comprehensive and first-of-its-kind framework** for disentangling **semantic and visual representations** from diffusion model backbones.\n\nAs the reviewer rightly points out, our work is the first to explicitly address the challenge of **visual correspondence** within diffusion models, complementing the well-studied semantic correspondence task, with the aim of detecting visual inconsistencies in subject-driven generation. To tackle this novel task, we introduce several key contributions:\n\n- A **carefully designed dataset generation pipeline**, which combines established tools (e.g., SAM, LPIPS) with a novel **skewness-based metric** to ensure high-quality correspondences.\n\n- A **dual-branch aggregation architecture**, trained with a contrastive objective to disentangle visual and semantic features; a setup that was not investigated before.\n\n- A new **Visual-Semantic Match (VSM)** metric that enables **both quantification and spatial localization** of inconsistencies, capabilities absent in current evaluation protocols.\n\nIn summary, while the individual building blocks (e.g., aggregation, feature alignment) are familiar, our work makes a novel and cohesive **methodological contribution** to a previously unexplored problem setting, which we believe is a necessary foundation for future research in visual consistency evaluation.\n\n---\n\n\n\n\n\n\n\n> **“The work only investigates SD 2.1 structure and no other popular DiT based networks.”**\n\n&emsp; Despite the increasing prominence of DiT-based diffusion models in image generation, efficient feature extraction from their backbones remains an active area of research, particularly when it comes to identifying which layers yield robust semantic or visual features [3].\n\nMoreover, DiT architectures are computationally expensive, which makes dense feature extraction and aggregation significantly slower than UNet-based models. In contrast, UNet-based diffusion models benefit from well-studied internal representations and recent advancements such as **CleanDIFT** [4], which distills clean features from intermediate layers and enables *near* real-time inference for correspondence tasks.\n\nThat said, we would like to acknowledge that we found a recent preprint [5] addressing the use of DiT-based diffusion backbones for semantic correspondence. This paper was made publicly available on arXiv **after the NeurIPS submission deadline**, and we were therefore unable to consider it during the development and writing of our submission. Once the **code and implementation details become available**, we are very interested in integrating DiT features into our pipeline and evaluating their impact on visual correspondence tasks.\n\nIn summary, while DiTs show strong generative potential, their internal representations are still being mapped and benchmarked. Our work builds on well-established UNet backbones for practical and reproducibility reasons, but is structured to **adopt DiT backbones as the field matures**.\n\n---\n\n> **“The variations of objects, poses, illumination, background, etc. in the dataset is limited due to the only data source from Subjects 200K … Whether the network can maintain a good semantic similarity under large variations is a question, though it can captures small visual diffierences”**\n\n&emsp; We agree with the reviewer’s concern that relying solely on a single dataset can impose limitations in terms of object pose diversity, illumination conditions, and background variations. However, at the time of submission, **Subjects200K was the only publicly available curated dataset** for subject-driven generation.\n\nThat said, the **qualitative results provided in Section D and Figure 3 of the supplementary material**, where we evaluate multiple subject-driven generation methods using our proposed metric, **demonstrate that our approach is effective even under large pose changes** in the generated subjects.\n\nFinally, we emphasize that our **dataset generation pipeline is highly flexible** and can be applied to future subject-driven generation datasets as they emerge. Since it only requires **image pairs of the same subject**, it imposes minimal assumptions, ensuring **broad applicability beyond the current dataset** and paving the way for seamless extension to more diverse settings.\n\n---\n\n\n\n> **“Can you offer more results with large pose variations and non-rigid deformations?”**\n\nWe have updated **Figure 6** to include additional examples that exhibit **large pose variations and non-rigid deformations**. However, due to the recent changes in NeurIPS rebuttal policy, we are unable to upload revised figures or supplementary content during the rebuttal phase.\n\nThese updated examples will be **included in the revised version of the paper** to better demonstrate the robustness of our method under more challenging transformations.\n\n---\n\n> **“During the inpainting process, do you see any artifacts such as the clear boundary of the inpainting region? If yes, is it an obvious image cue for the network to capture during the training process and hack the final test score?”**\n\n&emsp; This is an important concern and one we explicitly considered when designing our inpainting pipeline. To minimize the risk of the model learning to detect the inpainting artifact, we carefully tuned the inpainting pipeline to minimize introducing visible seams or hard edges. Specifically, we **crop a localized patch around the region to be inpainted with padding**, which promotes smoother blending and reduces the likelihood of visible boundaries.\n\nMoreover, since our method operates on **point-based correspondences rather than region-level supervision**, the majority of training points lie **within the inpainted region itself**, away from boundaries where artifacts might appear.\n\nTo further rule out the possibility that the model is simply learning to detect inpainting artifacts, we evaluated it on **real subject-driven generation outputs** in **Section D and Figure 3 of the supplementary material**, where no synthetic inpainting is involved. In these real-world scenarios, our method continues to **successfully localize visual inconsistencies**, while competing metrics such as CLIP, DINO, and VLM struggle to differentiate outputs.\n\nThese results strongly suggest that our learned features do **not rely on artifact cues**, and instead generalize to meaningful appearance differences across naturally generated image pairs.\n\n---\n\n> **“Is there a multiple visual inconsistence setup in the experiment? In the text, it only says the smallest region for each SAM segmentation to create the inpainting. Since in reality, there always are multiple inconsistencies in the results, how to evaluate such scenarios?”**\n\n&emsp; This is a valid concern and one we considered in the design of our dataset generation pipeline.\n\nWhile we select the **smallest valid region** from SAM segmentation during inpainting to ensure precision and control, it is important to note that **our training pipeline operates at the level of point correspondences, not regions**. \nAs a result, the learning process is inherently **agnostic to the number of inconsistent regions**, whether one or many, since the contrastive objective is applied over **individual point correspondences**.\n\nIn practice, as shown in **Figure 3 of the supplementary material**, our approach is capable of successfully localizing **multiple** inconsistent regions in the generated subject, for example, the front and rear wheels of the toy tricycle, or the handle and blade of the rolling cutter. \n\nThese results confirm that, even though each training sample may contain only a single manipulated region, the model generalizes well to real-world cases involving multiple inconsistent regions, which are common in subject-driven generation outputs.\n\n---\n\n**References**  \n[1] Luo, Grace, et al. \"Diffusion hyperfeatures: Searching through time and space for semantic correspondence.\" *NeurIPS* (2023)  \n\n[2] Zhang, Junyi, et al. \"Telling left from right: Identifying geometry-aware semantic correspondence.\" *CVPR* (2024)\n\n[3] Avrahami, Omri, et al. \"Stable flow: Vital layers for training-free image editing.\" CVPR (2025)\n\n[4] Stracke, Nick, et al. \"Cleandift: Diffusion features without noise.\" CVPR (2025)\n\n[5] Gan, Chaofan, et al. \"Unleashing Diffusion Transformers for Visual Correspondence by Modulating Massive Activations.\" arXiv preprint arXiv:2505.18584 (2025)."}, {"review_id": "Ls4f9VNkvw", "reviewer": "Reviewer_oXtL", "summary": "This paper proposes a method to evaluate visual consistency in subject-driven image generation by disentangling semantic and visual features from diffusion model backbones. The approach includes an automated dataset generation pipeline that creates image pairs with visual inconsistencies, training aggregation networks with a contrastive loss to separate semantic from visual features, and a metric based on the trained model for quantifying consistency. The metric shows improved correlation with ground-truth consistency compared to existing metrics like CLIP and DINO, while also providing spatial localization of inconsistent regions.", "questions": "Have you considered fine-tuning an open source VLM with this data? Given the detailed information about inconsistency locations, this could be effective even with limited training steps.\nWhy do you use features from diffusion UNets rather than Transformer models - is this to leverage previous studies on UNet features or for other reasons?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths: \n\n(1) Well-motivated approach: The work addresses two relevant gaps: (a) A metric for subject consistency is needed, as existing approaches like CLIP and DINO focus on global semantics and may miss visual details, and (b) as noted in the paper, diffusion features have been used extensively for semantic correspondence and other tasks, but less so for subject consistency evaluation. \n(2) Useful data generation pipeline: The automated pipeline for creating image pairs with controlled visual inconsistencies could be valuable regardless of the downstream evaluation method. Even if VLM-based approaches prove better than pretrained diffusion features for consistency evaluation, this synthetic data could be used to fine-tune VLMs.\n\n\nWeaknesses: \n\n(1) VLM-based metrics argument: The critique of VLM-based approaches may be overstated. Models like Gemini have shown reasonable capability in localizing visual inconsistencies, and the limitations noted could potentially be addressed through improved prompting strategies. It's also unclear why a single consistency score is preferable to region-based analysis that VLMs could provide.\n(2) ”Disentanglement\" terminology: The term \"disentanglement\" seems overstated given that the primary goal is subject consistency evaluation rather than true feature disentanglement. The learned visual features likely still contain semantic information - for instance, the method probably wouldn't identify two images with identical texture but completely different semantic content as the same subject. The work might be better framed around \"subject-driven features\" or similar terminology that better reflects the actual objective.\n(3) Missing citations: The related work appears to miss relevant approaches in training-free consistent generation, including [1] ConsiStory: Training-Free Consistent Text-to-Image Generation [2] The Chosen One: Consistent Characters in Text-to-Image Diffusion Models (both appeared in SIGGRPAH 2024)", "was_revised": true, "final_justification": "Most of my concerns were address in the rebuttal, and having read the discussions with other reviewers, I maintain my original positive leaning score.", "rebuttal": "We thank the reviewer for the constructive feedback and thoughtful suggestions, as well as for recognizing the strengths of our approach, including that it presents a **“well-motivated approach,”** **“addresses two relevant gaps,”** and offers a **“useful data generation pipeline”** whose **“synthetic data could be used to fine-tune VLMs.”**\n\nWe address the reviewer’s questions and concerns point by point below.\n\n---\n\n> **(1) “VLM-based metrics argument: The critique of VLM-based approaches may be overstated. Models like Gemini have shown reasonable capability in localizing visual inconsistencies, and the limitations noted could potentially be addressed through improved prompting strategies. It's also unclear why a single consistency score is preferable to region-based analysis that VLMs could provide.”**\n\n&emsp; We agree that VLM-based approaches can provide valuable insights for evaluating subject-driven generation, particularly in delivering high-level judgments of visual consistency, as demonstrated in DreamBench++.\n\nHowever, as shown in **Figure 3 of the supplementary material**, VLMs, including ChatGPT-4o, often produce **overly optimistic or indistinguishable scores across different generation methods**, even in the presence of clear visual inconsistencies. This highlights the limitations of relying solely on language-conditioned scoring for detecting fine-grained visual differences.\n\nRegarding the use of Gemini, we share the reviewer’s view that it has strong potential, especially given its native grounding and multimodal segmentation capabilities. However, to the best of our knowledge, there is currently **no published work** evaluating Gemini’s ability to localize inconsistencies in subject-driven generation. \n\nTo explore this direction, we conducted **preliminary experiments** in which we concatenated the reference image and the generated image side-by-side and prompted Gemini to produce segmentation masks highlighting inconsistent regions. Unfortunately, we were **unable to obtain valid outputs via the API**, suggesting either prompt misalignment or unsupported task formulation.\n\nWe still believe Gemini may be a promising direction, but several open design questions remain:\n- Should images be presented independently or concatenated?\n- Can the model return pixel-level consistency scores or only segmentation masks?\n- What prompt formulations are required to elicit meaningful pairwise comparisons?\n\nWithout a systematic investigation into these questions or supporting literature, we believe it is **premature to draw conclusions** about Gemini's applicability to this task. That said, we welcome any references or suggestions from the reviewer regarding published work in this area.\n\nBy contrast, our method already provides a unified and transparent solution offering:\n- A **global consistency score** via the VSM metric\n- **Localized, pixel-level consistency maps**, as demonstrated in **Figure 3** in the supplementary.\n\nThis combination of **quantitative scoring and spatial interpretability** remains **unmatched by current VLM-based metrics**, and we believe it fills a critical gap in the evaluation of subject-driven generation.\n\n\n---\n\n> **(2) “Disentanglement\" terminology: The term \"disentanglement\" seems overstated given that the primary goal is subject consistency evaluation rather than true feature disentanglement. The learned visual features likely still contain semantic information — for instance, the method probably wouldn't identify two images with identical texture but completely different semantic content as the same subject. The work might be better framed around \"subject-driven features\" or similar terminology that better reflects the actual objective.**\n\n&emsp; We appreciate this insightful observation and agree with the reviewer that the learned visual features likely retain some degree of semantic information, particularly because the training image pairs consist of the **same object**, even after visual perturbation.\n\nAchieving complete disentanglement, where visual features are entirely free of semantic content, would ideally require a dataset that provides **visual and semantic correspondences across different objects**, a setup that is not straightforward to construct in an automated way and would require huge manual annotation effort.\n\nTherefore, we leave this to future work, and we will revise the manuscript to clarify this point and **acknowledge that our notion of \"disentanglement\" is partial and task-driven**. \nWe will also incorporate this discussion in the **limitations and future work sections** of the revised paper.\n\n---\n\n> **(3) “Missing citations:” The related work appears to miss relevant approaches in training-free consistent generation, including [1] ConsiStory: Training-Free Consistent Text-to-Image Generation [2] The Chosen One: Consistent Characters in Text-to-Image Diffusion Models (both appeared in SIGGRPAH 2024)**\n\n&emsp; We thank the reviewer for pointing that out. We will include the missing references in the revised manuscript.\n\n---\n\n> **“Have you considered fine-tuning an open source VLM with this data? Given the detailed information about inconsistency locations, this could be effective even with limited training steps.”**\n\n&emsp; We appreciate this excellent suggestion. Our automated dataset generation pipeline indeed provides detailed annotations of inconsistent regions, which could be valuable for fine-tuning vision-language models to improve their ability to detect visual inconsistencies.\n\nWhile promising, such an approach would require addressing several design challenges, including how to represent the annotated regions as input to the VLM (e.g., through bounding boxes, masks, or spatial tokens), and what **output modality** the VLM should be trained to produce (e.g., region-wise judgments or pixel-level scores).\n\nWe view this as a compelling direction and will include this idea as part of the **future work** in the revised manuscript.\n\n---\n\n\n> **“Why do you use features from diffusion UNets rather than Transformer models — is this to leverage previous studies on UNet features or for other reasons?”**\n\nYes, our choice to use **UNet-based diffusion backbones** was driven by both practical and methodological considerations. UNet backbones have been extensively studied in prior work, and with the emergence of **CleanDIFT**, it became possible to extract clean features with *near real-time efficiency*. This enables fast and scalable computation of dense correspondences, which our VSM metric relies on, with minimal overhead.\n\nThat said, we found a promising new work [1] that explores using **DiT-based backbones** for semantic correspondence tasks. This work appeared **after the NeurIPS submission deadline**, and we were therefore unable to consider it in our method or comparisons. Once the code and model checkpoints become available, we are keen to explore integrating DiT-based features into our framework.\n\nImportantly, our pipeline is **modular and backbone-agnostic by design**, and can readily incorporate new backbones, including DiT, as the field matures and extraction tools become more accessible.\n\n[1] Gan, Chaofan, et al. \"Unleashing Diffusion Transformers for Visual Correspondence by Modulating Massive Activations.\" arXiv preprint arXiv:2505.18584 (2025)."}, {"review_id": "Wu7EZrlByc", "reviewer": "Reviewer_bVqH", "summary": "This paper proposes a novel metric for evaluating subject-driven image generation models. The proposed method disentangles semantic features and visual features from a frozen diffusion backbone to compute semantic similarity and visual similarity, and the final quality metric, visual-semantic match (VSM), is given by an IoU of the semantically and visually matched regions. A data generation pipeline is proposed, by altering the semantically corresponding regions using an inpainting model. Inconsistent regions can be localized.", "questions": "Following up from the Weaknesses above:\n1. It would be good to show visualizations of semantic and visual correspondences on samples with large view/pose change and lighting difference. Is the proposed metric robust to such variations? What if the object gets heavily occluded after the subject-driven generation? Would this result in a lower metric value?\n2. A more objective way to evaluate the metrics would be to actually generate results using subject-driven generation models and have human annotators manually label the inconsistent regions against the reference object images, even on just a small number of samples. The oracle metric can be computed on such human annotations to evaluate the metrics.\n\nThis is a great paper proposing an evaluation metric for subject-driven generation, with promises to release the code and model. I'm more closer to Accept and willing to raise the score based on the responses to #1 and #2.", "limitations": "Limitations have been discussed. Potential negative societal impact has not been discussed.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n* It has long been a challenge in subject-driven image generation that there are no reliable quality metrics to evaluate models. This paper addresses this well and can potentially have a big impact in the domain.\n* The whole pipeline is sound, from data generation using a diffusion-based inpainting model and training the model via localized contrastive learning.\n* Visualizations of inconsistent regions look accurate. The proposed metric (VSM) works much better than CLIP, DINO or VLM, which are metrics that are currently being used in the domain.\n* Various ablation studies are provided in the main paper and the supplementary material, allowing readers to gain a deeper understanding of the proposed approach.\n* The authors promise to publicly release the source code and the proposed model.\n* The paper is well-written. It is clear and easy to read with motivations explained well throughout.\n\nWeaknesses:\n* My main concern is that the visualized samples in Fig 1 (main), Fig 6 (main) and Fig 2 (supp) mostly show cases where there is almost no view change of the reference object. For evaluating subject-driven generation results, I think it's important that the metric is invariant to view and lighting changes of the object and solely focuses on the identity. \n* Metrics are evaluated using the proposed data generation pipeline in Table 1, in which case, the proposed model could be at an advantage regardless of the specific images being held out. Metrics should be evaluated on real results by subject-driven generation models for a more objective and accurate evaluation. A suggestion is given in Questions #2 below.", "was_revised": true, "final_justification": "Great discussion with the authors during the discussion period. The authors were able to clarify all my questions and concerns the best they can given that no visual results could be provided. I trust the authors on the updated visual results and to make appropriate revisions as promised. The additional results provided during the discussion should go in the main paper or the supplementary material as they seem fit. Happy to raise my score and recommend Accept for this paper. I believe the proposed metric will be able to serve as a reliable quality metric for evaluating subject-driven generation models, where such metric was highly needed.", "rebuttal": "&emsp; We thank the reviewer for the constructive and thoughtful feedback, and for highlighting several strengths of our work, including that **“this is a great paper proposing an evaluation metric for subject-driven generation,”** that it **“can potentially have a big impact in the domain,”** that **“the whole pipeline is sound,”** and that **“visualizations of inconsistent regions look accurate.”** \nWe also appreciate the recognition of the paper’s clarity and the effectiveness of the proposed VSM metric compared to existing alternatives such as CLIP, DINO, and VLM.\n\nWe address the reviewer’s concerns and suggestions below:\n\n---\n\n> **“My main concern is that the visualized samples in Fig 1 (main), Fig 6 (main) and Fig 2 (supp) mostly show cases where there is almost no view change of the reference object. For evaluating subject-driven generation results, I think it's important that the metric is invariant to view and lighting changes of the object and solely focuses on the identity.”**\n\n&emsp;  We agree with the reviewer that the qualitative examples in Figures 1 and 6 of the main paper do not include cases with significant pose or lighting variation.\nWe updated Figure 6 to include examples with large pose and view variations. \nHowever, due to recent changes in the NeurIPS rebuttal policy, we are unable to include additional visual examples at this stage. \n **Therefore, we will include the updated figure in the revised version of the paper**.\n\nIn the meantime, we would like to draw the reviewer’s attention to **Figure 3 in the supplementary material**, which includes examples with **notable changes in pose and illumination** from real subject-driven generation outputs. \nThese examples demonstrate that our method **is effective at localizing inconsistent regions**, even under changes of pose and viewpoints.\n\n---\n\n> **“Metrics are evaluated using the proposed data generation pipeline in Table 1, in which case, the proposed model could be at an advantage regardless of the specific images being held out. Metrics should be evaluated on real results by subject-driven generation models for a more objective and accurate evaluation. A suggestion is given in Questions #2 below.”**\n\n&emsp; We agree with the reviewer that evaluating metrics solely on data generated by our own pipeline may not fully reflect real-world performance. To address this, we conducted an additional evaluation on **real subject-driven generation outputs**, as described in **Section D of the supplementary material**.\n\nIn this evaluation, we applied our VSM metric to outputs from three recent subject-driven generation models: **Diptych Prompting**, **DSD Diffusion**, and **EasyControl**. These examples exhibit natural visual inconsistencies and do not involve any synthetic inpainting. Results show that our method effectively localizes inconsistent regions and assigns **more reasonable and differentiated scores** to each method. In contrast, existing metrics such as CLIP, DINO, and VLM often produce **similar or inaccurate scores**, even when inconsistencies are clearly visible.\n\nThis real-world evaluation highlights the **robustness and practical utility** of our metric beyond synthetic settings.\n\nWe address the follow-up suggestion in **Question #2** separately below.\n\n---\n\n> **Q1 “It would be good to show visualizations of semantic and visual correspondences on samples with large view/pose change and lighting difference. Is the proposed metric robust to such variations? What if the object gets heavily occluded after the subject-driven generation? Would this result in a lower metric value?”**\n\n&emsp; As discussed above, we will include additional visualizations of semantic and visual correspondences under large pose and lighting variations in the revised version of the paper.\n\nRegarding the robustness of the metric in the presence of occlusion, the VSM metric compares visual correspondences against semantic ones, using an IoU-style formulation over matched point correspondences. \nIf a region becomes heavily occluded in one of the images, it is **unlikely to be selected as a semantic match**.\nAs a result, such regions would typically be excluded from the semantic match set, and thus **do not contribute to the VSM score**. \nThis behavior aligns with human judgment: VSM focuses on regions that are both semantically and visually present in both views, and naturally disregards parts that are missing or occluded in one image.\n\nThis design makes the metric **robust to occlusion and partial visibility**, ensuring that the score reflects consistency only over **mutually visible parts** of the subject.\n\n---\n\n> **Q2 “A more objective way to evaluate the metrics would be to actually generate results using subject-driven generation models and have human annotators manually label the inconsistent regions against the reference object images, even on just a small number of samples. The oracle metric can be computed on such human annotations to evaluate the metrics.”**\n\nThis is an excellent suggestion, and we thank the reviewer for proposing it.\n\nTo implement this idea, we developed a lightweight web-based annotation tool where the **reference image** is displayed on the left and the **generated image** from a subject-driven model is shown on the right. Annotators are asked to draw masks over regions in the generated image that are **visually inconsistent** with the reference subject.\n\nTo compute the oracle score, we follow the protocol described in Section 4.2, calculating the **ratio between the annotated inconsistent region and the reference object mask**, providing a normalized inconsistency score per sample.\n\nWe asked professional annotators to label a subset of generations from our test set, resulting in 480 high-quality annotated samples. We then computed the correlation between each metric and this human-annotated oracle:\n\n| Metric         | Pearson Correlation | Spearman Correlation |\n|----------------|---------------------|-----------------------|\n| CLIP           | 0.223               | 0.172                 |\n| DINOv2         | 0.107               | 0.132                 |\n| VLM (GPT-4o)   | 0.298               | 0.147                 |\n| **VSM (Ours)** | **0.367**           | **0.253**             |\n\nOur **VSM metric consistently achieves the highest correlation** with human annotations, demonstrating its strong performance on real-world data. While VLM (GPT-4o) performs better in this setting than in the synthetic experiments reported in the main paper, our approach still maintains the **highest overall correlation**, confirming its robustness and reliability.\n\nThis experiment further validates that our approach:\n- Generalizes beyond the synthetic training setup,\n- Produces objective and interpretable consistency scores,\n- And uniquely supports **localized, pixel-level consistency maps**, a capability not provided by current metrics or VLMs.\n\nTogether, these strengths position VSM as a **practical and reliable evaluation tool** for subject-driven generation tasks."}, {"review_id": "F9KDvAqtBL", "reviewer": "Reviewer_SfLC", "summary": "The paper proposes learning semantic and visual features from diffusion features. This is done by tuning diffusion features via contrastive loss on generated data. The data generation pipeline consists of taking a (visually and semantically) consistent pair of images, and applying segmentation (to detect consistent regions between the images) and inpainting (to alter one of the consistent regions), and as a result constructing an inconsistent pair.", "questions": "Please see weaknesses.", "limitations": "Please see weaknesses.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n1.\tThe paper is clearly written.\n2.\tThe method and its motivation is well explained.\n3.\tThe data generation pipeline is interesting and novel, enabling the creating of inconsistent pairs of images for training.\n4.\tDetecting visual inconsistent regions is beneficial for subject-based image generation, and visual and semantic features can possibly be used to enhance the effectiveness of such applications.\n\n**Weaknesses**\n\n1.\tUnclear generalization abilities of the learned features: my major concern is that it is unclear whether the tuned features truly learn to detect visual inconsistencies, or rather to identify inpainted regions (and ignore them/assign some random features to them so there won’t be correspondences between the inconsistent pair in these regions). While the inpainted images may appear natural to the human eye (although I can see blurry and unrealistic examples of inpainted regions), a strong neural net can probably easily detect stitched inpainted regions and treat them differently.\n* The evaluation of the method relies on the same data generation pipeline, and thus it is unclear whether the learned features actually generalize and learn visual features. I believe it is crucial to create a creative test set of real, unaltered inconsistent pairs for evaluation. Examples for such pairs can be different images of a person with different clothing, images of a house with different decorations (e.g., holiday decorations), etc.\n* Another valuable experiment is to collect pairs of images, created by the data generated pipeline, where the inpainted region is very close (visually) to the original image. In this case, we would still expect to see visual correspondences between the “inconsistent pair”.\n* In some examples, it seems that there are no correspondences in the inpainted region between the images, even when some parts within the inpainted region still look visually similar between the images (e.g., the rings in the main paper).\n* Additionally, it will be helpful if the authors can also visualize the inpainting mask used to create the inconsistent pair in figure 6, for reference.\n2.\tLacking experiments on real tasks or benchmarks: The main motivation presented by the authors for detecting inconsistent regions is subject-driven image generation. However, another concern regarding the evaluation section is that the paper lacks experiments on a real task. It would be significantly more convincing if the authors evaluated the performance of their features on subject-driven generation methods or personalized-generation methods (e.g., textual inversion [1], dreambooth [2], RealFill [3]), aiming to generate images based on a set of reference images, and demonstrated the effectiveness of their visual and semantic features in actually detecting inconsistent regions.\n\n3. Limitations: The authors discuss limitations regarding the training data and inconsistencies between fine-details which the features may not detect. I think it would be helpful if the authors can include failure cases, and an analysis regarding the sensitivity of the learned features to real-world image degradations as motion blur, noise, photometric distortions, illumination changes.\n\n**References**\n\n[1] An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion, ICLR 2023.\n\n[2] DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation, CVPR 2023.\n\n[3] RealFill: Reference-Driven Generation for Authentic Image Completion, SIGGRAPH 2024", "was_revised": true, "final_justification": "Thanks to the authors for the response.\nWhile I still have some concerns about the sensitivity of the features (e.g., in the last two rows of figure 3, when the pose changes significantly, VSM score is much lower), the authors have addressed most of my concerns.\n\nThe additional experiments and user study show that the features learn beyond the inpainted regions, and the paper provides new dataset and metric which can benefit the community. \n\nTherefore, I am happy to increase my score. I highly suggest the authors to add an analysis about the sensitivity of VSM to pose, illumination, scale and image degradation, that would encourage other researchers to adopt the metric.", "rebuttal": "&emsp; We thank the reviewer for the thorough feedback. We appreciate the recognition of our **paper’s clarity**, **the novelty of the data generation pipeline**, and **the relevance of our approach to subject-driven image generation**. \n\nWe address the reviewer’s concerns and questions below.\n\n--- \n\n> **“Unclear generalization abilities of the learned features: my major concern is that it is unclear whether the tuned features truly learn to detect visual inconsistencies, or rather to identify inpainted regions ...”**\n\n&emsp; We fully understand and appreciate this concern; it is one we explicitly considered in the design of our method.\n\nFirst, to minimize the risk of the network learning superficial cues such as **boundary artifacts from inpainting**, we carefully designed our inpainting pipeline to operate on **cropped patches with padding** rather than applying inpainting to the full image. This design significantly reduces visible seams or texture discontinuities at the boundaries. Secondly, since our objective is **point-based rather than patch-based**, most training points lie within the central region of the inpainted patch, away from any potential boundary artifacts.\n\nNevertheless, as pointed out by the reviewer, the networks may still learn to exploit subtle, invisible cues undetectable to human observers. To invalidate this hypothesis, we evaluated our network on **real subject-driven generation outputs** in **Section D and Figure 3 of the supplementary material**. These images are produced by three different subject-driven generation methods, EasyControl, DSD Diffusion, and Diptych Prompting, **without any synthetic perturbations or inpainting**.\n\nIn these settings, Figure 3 in the supplementary shows that our method **successfully localizes inconsistent regions**, whereas existing metrics often fail to distinguish between models. This demonstrates that our learned features are not overfitting to inpainting artifacts and instead **generalize effectively to natural inconsistencies** in real subject-driven generation scenarios.\n\n--- \n\n> **“The evaluation of the method relies on the same data generation pipeline, and thus it is unclear whether the learned features actually generalize and learn visual features. I believe it is crucial to create a creative test set of real, unaltered inconsistent pairs for evaluation ...”**\n\n&emsp; We agree that evaluating on real, unaltered, inconsistent image pairs is essential for assessing generalization beyond the synthetic training pipeline. As mentioned above, we include such an evaluation in **Section D and Figure 3 of the supplementary material**, where we apply our method to outputs from multiple **real subject-driven generation models**.\n\nThese examples are **not generated through our inpainting pipeline**, but instead reflect natural inconsistencies such as changes in pose, texture, and object details. As shown in Figure 3 in the supplementary, our method **successfully localizes inconsistent regions** in these real-world cases, whereas existing metrics such as CLIP, DINO, and VLM often assign similar or overly optimistic scores across models.\n\nWe believe this evaluation provides strong evidence that our learned features **generalize beyond synthetic artifacts** and are effective at capturing meaningful visual inconsistencies in real subject-driven generation scenarios.\n\n--- \n\n> **- “In some examples, it seems that there are no correspondences in the inpainted region between the images, even when some parts within the inpainted region still look visually similar between the images...”**\n> **- “Another valuable experiment is to collect pairs of images, created by the data generated pipeline, where the inpainted region is very close (visually) to the original image. In this case, we would still expect to see visual correspondences between the ‘inconsistent pair’.”**\n\nThese are excellent observations, and both behaviors are primarily governed by two key mechanisms in our pipeline:\n\n1. **Visual similarity threshold ($\\tau$)**:  \n   As described in Section 3.3 (line 232), when computing visual correspondences, we apply a similarity score threshold $\\tau$ to determine which matches are retained. A higher threshold results in stricter matching; even minor visual discrepancies may prevent a correspondence, while a lower threshold is more forgiving and allows matches between regions that are only approximately similar in appearance.\n\n   In our experiments, we found $\\tau = 0.6$ to strike a good trade-off between sensitivity and robustness, as supported by the ablation study in Section 4.3. However, this parameter is fully tunable and could be adapted depending on the application’s sensitivity requirements. A natural extension of our metric would be to report **multiple VSM scores at different $\\tau$ values**, such as $VSM_{\\tau=0.5}$, $VSM_{\\tau=0.6}$, and $VSM_{\\tau=0.7}$, to give users a more comprehensive picture of subject-driven generation performance across varying thresholds.\n\n2. **LPIPS-based filtering in training data**:  \n   To ensure that the inpainted regions used for training reflect meaningful changes, we compute the LPIPS distance between each inpainted patch and its corresponding original patch. Pairs with LPIPS scores below a set threshold are discarded. This ensures that the model focuses on learning **clear, perceptually relevant inconsistencies**, rather than becoming overly sensitive to minimal or imperceptible changes.\n\n   Based on the reviewer’s suggestion, we experimented with different LPIPS thresholds and report below their effect on correlation with the oracle as explained in Section 4.3 in the main paper:\n\n   | LPIPS Threshold | Pearson Correlation | Spearman Correlation |\n   |----------------|---------------------|-----------------------|\n   | 0.10           | 0.3523              | 0.3935                |\n   | 0.15           | **0.4750**          | **0.4410**            |\n   | 0.20           | 0.2330              | 0.1790                |\n\n   These results show that a **threshold of 0.10** admits inpainted regions that are too similar to the original, weakening the model's ability to learn discriminative features. Conversely, a **threshold of 0.20** removes too many moderately different regions, limiting variability. We selected **0.15** as it offered the best balance between preserving useful variation and excluding near-duplicates, yielding the highest correlation with the oracle.\n\nBoth $\\tau$ and the LPIPS threshold are **exposed and tunable**, offering users flexibility to tailor the system to their desired level of tolerance or strictness.\n\n--- \n\n> **“Additionally, it will be helpful if the authors can also visualize the inpainting mask used to create the inconsistent pair in figure 6, for reference.”**\n\nThat is a good suggestion and would definitely help interpret the figure better. We will incorporate this suggestion in the revised version of the paper.\n\n--- \n\n> **“Lacking experiments on real tasks or benchmarks: The main motivation presented by the authors for detecting inconsistent regions is subject-driven image generation. However, another concern regarding the evaluation section is that the paper lacks experiments on a real task. It would be significantly more convincing if the authors evaluated the performance of their features on subject-driven generation methods or personalized-generation methods…”**\n\n&emsp; We appreciate this suggestion and agree that evaluation on real subject-driven generation outputs is critical for validating the utility of our method. This experiment **was provided in Section D of the supplementary material** (“Benchmarking Subject-Driven Generation Approaches with Our Metric”), where we apply our approach to outputs from three recent subject-driven generation methods: **Diptych Prompting**, **DSD Diffusion**, and **EasyControl**.\n\nThese models generate subject-specific images based on reference inputs or identity prompts, without any artificial perturbations or inpainting. As shown in **Figure 3 (supplementary)**, our evaluation reveals that existing metrics, including CLIP, DINO, and VLM (ChatGPT-4o), often assign overly optimistic or indistinguishable scores across methods, despite clearly visible inconsistencies. In contrast, our **VSM metric provides more discriminative and spatially grounded scores**, aligning more closely with human perception of visual consistency.\n\nDue to space limits, we also refer the reviewer to our answer to **Question #2 of Reviewer bVqH** that shows that our metric achieves the highest correlation with human judgment in real subject-driven generation.\n\n--- \n\n> **“Limitations: The authors discuss limitations regarding the training data and inconsistencies between fine-details which the features may not detect. I think it would be helpful if the authors can include failure cases, and an analysis regarding the sensitivity of the learned features to real-world image degradations as motion blur, noise, photometric distortions, illumination changes.”**\n\n&emsp; We thank the reviewer for this suggestion. Due to the recent NeurIPS rebuttal policy changes, we are unable to include new visual examples or failure case analyses at this stage. However, we fully agree that examining the sensitivity of our learned features to real-world image degradations, such as motion blur, noise, and illumination changes, is an important direction.\n\nIn the revised version of the paper, we will include **qualitative failure cases** as well as an initial analysis of how the method responds to various photometric and geometric distortions. This will help characterize the **robustness and limitations** of the learned visual features in less controlled settings, and guide future extensions of the approach toward more degradation-resilient representations."}]

Author rebuttal:

Meta-review: The paper proposes a method for measuring visual consistency in subject-driven image generation by disentangling semantic and visual features from diffusion model backbones. Reviewers acknowledge several strengths of the paper, including addressing a critical gap where current metrics (CLIP, DINO) provide only global scores that miss fine-grained inconsistencies, while this approach can spatially localize where generated subjects differ from references. Additional strengths include a novel automated data generation pipeline and clear presentation. The main weaknesses noted by reviewers include limited pose/view variations in demonstrated examples and concerns about whether the method generalizes beyond its synthetic training data to real-world inconsistencies. In the rebuttal, the authors provided new experiments on subject-driven generations (DreamBooth) from real images, pointed to supplementary results demonstrating pose variations, and conducted a supportive human annotation study. All reviewers found the responses satisfactory and recommend acceptance.

The Area Chair agrees with the reviewers and recommends acceptance. The camera-ready version would benefit from including the promised additional experiments and discussing limitations more thoroughly.
Decision: Accept (spotlight)