Title: Mind-the-Glitch: Visual Correspondence for Detecting Inconsistencies in Subject-Driven Generation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Diffusion Models

Text-to-Image Generation

Semantic Correspondence

Subject-driven Generation

Personalized Generation

In-Context Generation
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

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. Project Page: https://abdo-eldesokey.github.io/mind-the-glitch/

1 Introduction

Recent advances in diffusion models have transformed the landscape of image generation, delivering exceptional quality, fidelity, and diversity [8, 5, 32, 36, 27, 30]. 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 a 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 are entangled, making it challenging to control or preserve specific content using text prompts alone.

To address these limitations, a growing research direction has focused on subject-driven generation, which aims to guide diffusion models to produce consistent images of a given subject in different scenes while preserving fine-grained visual details [13, 33, 28, 3, 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 a reference and a 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 metrics 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-processing.

This challenge bears similarities to the task of semantic correspondence , where corresponding points are 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, 51]. However, as illustrated in Figure 1, these semantic features are typically insensitive to appearance-level variations, as they focus primarily on structural or categorical semantics rather than fine-grained 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 an 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 architecture 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 our approach outperforms existing feature-based metrics such as CLIP and DINO, as well as the Vision-Language Model (VLM) metric, in quantifying visual inconsistencies, while allowing for localizing the inconsistent regions as well. We believe our framework offers a valuable step forward in the evaluation and development of subject-driven image generation.

Our contributions can be summarized as follows:

We propose a 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 a 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.

2 Related Work

In this section, we first provide a brief overview of the semantic correspondence task, which serves as a key inspiration for our work. Then we 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 [42], 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 a supervised manner using semantic correspondence datasets such as SPair-71k [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 a range of downstream tasks, including segmentation [45, 23], controllable editing [25, 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 [34, 17] or low-rank adaptation methods [12, 10, 37, 19], and even training-free approaches [43] Other approaches [35, 9, 15, 1] learned subject-specific embeddings, without modifying model weights, by associating subjects with new tokens in an image or text embedding space. To improve the trade-off between efficiency and fidelity, adapter-based methods [48, 44, 13, 28, 50] introduced zero-shot personalization by conditioning on 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 of diffusion Transformers (DiTs) [8, 5] introduced a new class of architectures that replace UNets with Vision Transformers as the denoising backbone, offering greater scalability and enhanced contextual understanding. These models support more flexible conditioning mechanisms and exhibit strong in-context learning capabilities, enabling them to generate diverse images of a subject without explicit fine-tuning [38, 18]. To further improve fidelity and identity preservation, several works have applied LoRA-based fine-tuning to DiTs architectures [41, 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 to [46].

2.3 Evaluating Subject-Driven Image Generation

Visual similarity is often measured using traditional metrics such as 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 to 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-grained appearance details of the subject. A recent trend involves leveraging Vision-Language Models (VLMs) to evaluate subject-driven generation by prompting them to score specific criteria of the generated images [41, 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

Segment

Object

'motorcycle'

Semantic

Correspondence

SAM

Inpaint

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𝑅1

𝐼

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Controlled

Inconsistency

𝐼

1

Sample a

Match

(𝑥 1

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, 𝑦

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(𝑥 2

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2 ′

the appearance similarity across images in subject-driven generation. Our approach enables both quantification and spatial localization of visual inconsistencies. By providing a reliable metric that offers fine-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 consistent 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, 22, 51, 52, 42] were trained on SPair-71k [24], which includes annotated semantic points between image pairs of given subjects. As demonstrated in Figure 1, semantic features are insensitive to appearance changes, making them unsuitable for matching visual appearance. To the best of our knowledge, there exists no dataset with annotated visual correspondences between visually similar or dissimilar regions, similar to SPair-71k.

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 disentangling semantic and visual representations from the internal features of diffusion models (Section 3.2). Lastly, we 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 (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 I 1 and I 2 from a 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 CleanDIFT [39]. Specifically, we extract features from the sixth decoder layer of the diffusion model UNet Φ , which has been shown to contain semantically rich information [42, 39], resulting in F 6 1 = Φ( I 1 ) and F 6 2 = Φ( I 2 ) . We then compute the pairwise similarity between the two feature maps to form a similarity matrix D = F 6 1 F 6 2 T . Corresponding points are obtained by selecting locations with the highest similarity scores using arg max D , to obtain the following correspondence mapping:

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 a pair of images with known, localized visual inconsistencies. To

𝒟[𝑘]

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𝐼 1

𝐼 2

9.3

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93

02

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Skewness=1.6

Skewness=-0.5

achieve this, we sample a point k from C 1 that has a high similarity score and use the corresponding point pair ( x k 1 , y k 1 ) and ( x k 2 , y k 2 ) as prompts to the SAM model [16], which segments a localized region in each image. This produces region masks defined as R i = SAM ( I i , ( x k i , y k i )) . 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 flattextured subjects, such as a whiteboard, where the selected point k tends to match broadly across the object (see Figure 3), 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 is computed as:

where n = |D [ k ] | , µ = mean ( D [ k ]) , and σ = STD ( D [ k ]) . We observe that high skewness corresponds to matches in textured regions, characterized by a long-tailed distribution and low ambiguity. In contrast, low skewness indicates more uniform similarity scores, suggesting ambiguous matches typically associated with flat surfaces as illustrated in Figure 3.

Validating Matched Regions: 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 a diffusion-based inpainting model (we use SDXL [27] for efficiency), to obtain two inconsistent images I ′ 1 and I ′ 2 . Specifically, we crop a patch around the region with padding and feed it to the inpainting model to ensure that the model does not observe the rest of the object, promoting the generation of 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 I i and I ′ i . We discard samples with low LPIPS scores to ensure meaningful variation. Further details on the filtering strategy are provided in the supplementary material. We provide an illustration for the data generation pipeline in Figure 2. Eventually, each dataset sample comprises a consistent image pair ( I 1 , I 2 ) , an inconsistent pair ( I ′ 1 , I ′ 2 ) generated with our data generation pipeline, subject masks ( O 1 , O 2 ) , inpainted region masks ( R 1 , R 2 ) , subject prompt s and target prompt p .

3.2 Learning Disentangled Semantic and Visual Representations

Given an inconsistent image pair I ′ 1 and I ′ 2 , which were generated with our automated pipeline in Section 3.1, where the inconsistent regions are defined by the masks R 1 and R 2 , 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 Φ , yielding feature maps F 1 = Φ( I ′ 1 ) and F 2 = Φ( I ′ 2 ) that include features from multiple decoder layers l ∈ L . To aggregate features from the different layers, we follow the approach of [22], but instead of using a single aggregation network, we employ two separate networks, Ψ l s and Ψ l v , to aggregate semantic and visual representations separately. Each aggregation network encompasses a ResNet [11] block per decoder layer that are

Inconsistent

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combined using trainable scalar weights w l :

where S i , V i are the semantic and visual features respectively, i ∈ { 1 , 2 } , d = w × h denotes the flattened spatial dimensions, and q 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 4.

Next, we compute cyclic similarity matrices between the aggregated semantic and visual features of the two images using dot products:

Our objective is to encourage semantic features to be similar across all point correspondences, while visual features should be similar only outside the inconsistent regions R i and dissimilar within them. To achieve this, we adopt a 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 i from Section 3.1 into two subsets: points that fall inside and outside the inconsistent regions:

Then, inspired by the contrastive objective used in CLIP [29], we define a semantic correspondence loss as follows:

This semantic loss encourages matched points, whether inside or outside the inconsistent region, to exhibit similar semantic feature representations.

To disentangle appearance-specific features, we define a visual loss that explicitly separates consistent from inconsistent regions. For inconsistent regions, we use a negative similarity objective:

This term penalizes similarity in appearance features for points known to be visually inconsistent. For consistent regions, we retain the standard contrastive loss to encourage matching:

Additionally, visually corresponding points inside the inpainted regions of the original consistent pairs I 1 and I 2 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 D s 21 and D v 21 , and each loss term is averaged over both directions. The final training objective combines semantic and visual losses as follows:

where α is a scaling factor used to prioritize the visual branch, since the semantic branch can already be extracted reliably even without any aggregation, as shown in [42, 39].

3.3 A Metric for Evaluating Subject-Driven 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 I 1 and I 2 generated by a 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 as F s i and F v i , 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 D s and D v , respectively. We then take the maximum similarity score for each point to obtain ˆ D s = max( D s ) and ˆ D v = max( D v ) , representing the best per-point match in the semantic and visual domains.

Semantic correspondences are identified by selecting points whose semantic similarity exceeds a predefined threshold T s , i.e. , ˆ D s > T s , resulting in a set of confident semantic matches indexed by J s . This filtering ensures that we only consider regions that are both visible and semantically coherent. We set T s = 0 . 7 in all experiments.

To assess visual consistency at these semantically matched locations, we use the same index set J s to retrieve the corresponding visual similarity scores from ˆ D v . Given a visual similarity threshold T v , we define the Visual Semantic Match (VSM) metric as:

where δ [ · ] is the indicator function, equal to 1 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, or by examining regions in ˆ D v 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

Dataset: 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.

Semantic

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Features

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Inpainting

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Inconsistent Generation

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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 of 5,000 image pairs for training and 500 image pairs for validation.

Testset: We generate a 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 [39]. We set the spatial resolution of the aggregated features in Equation (3) to h = w = 48 , resulting in d = 48 × 48 = 2304 , following CleanDIFT [39]. The projected feature dimensionality is set to q = 384 , as in [22]. We empirically found that setting α = 10 in Equation (11) yields the best performance. We train the model for 30 epochs using the AdamW optimizer [21] with a learning rate of 1e-3 that is divided by 10 every 10 epochs. The training is done on 1 A100 GPU (40GB) and takes 12 hours. The source code for the dataset generation pipeline and the proposed architecture are publicly available. 1

4.2 Evaluating the VSM Metric

To validate our proposed Visual-Semantic Match (VSM) metric, described in Section 3.3, we first perform a controlled evaluation where we define an 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 R 1 to the total object mask O 1 (see Figure 2), computed as:

This oracle reflects the ground-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

Reference Subject

Diptych [CVPR2025]

DSD [CVPR2025]

EasyControl [ICCV 2025]

VSM

:0.965

VSM

:0.975

VSM

:0.609

CLIP

:0.892

CLIP

:0.888

CLIP

:0.861

DINO

:0.745

DINO

:0.767

DINO

:0.615

VLM

:0.900

VLM

:0.850

VLM

:0.850

Oracle:0.948

0racle:0.972

0racle:0.567

VSM

:0.900

VSM

:0.886

VSM

:0.967

CLIP

:0.731

CLIP

:0.857

CLIP

:0.862

DINO

:0.820

DINO

:0.825

DINO

:0.864

VLM

:0.900

VLM

:0.850

VLM

:0.850

0racle:0.935

0racle:0.930

0racle:0.969

VSM

:0.974

VSM

:0.978

VSM

:0.712

CLIP

:0.811

CLIP

:0.872

CLIP

:0.790

DINO

:0.818

DINO

:0.780

DINO

:0.766

VLM

:0.850

VLM

:0.850

VLM

:0.850

0racle:0.949

0racle:0.990

0racle:0.749

used metrics in the literature for comparing image pairs: CLIP and DINO image-to-image similarity, as well as ChatGPT-4o as a representative Vision-Language Model (VLM). For VLM, we adopt the prompt used in [26], but modify it to produce a numerical score in the range of 0 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 to both feature-based similarity metrics and VLM judgments, indicating its greater reliability for measuring visual consistency. Qualitative results in Figure 5 illustrate 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-Driven 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 I 1 and target prompt p from our test set, we 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 1 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 6, 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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Comparison of Score Distributions Compared to Oracle

Oracle

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Tv

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layer6

layer8

layer9

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4.4 Ablation Analysis

We visualize the learned aggregation weights for the semantic and visual branches in Figure 8. Visual features are primarily derived from decoder layers 8 and 9, while semantic features draw from layers 8 and 10, indicating that certain layers (e.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 2 presents an ablation study of key hyperparameters. For the VSM similarity threshold T v , both low ( 0 . 5 ) and high ( 0 . 7 ) values reduce correlation with the oracle, with T v = 0 . 6 yielding the best performance. Setting α = 1 in the training loss significantly degrades performance by reducing emphasis on 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-both leading to performance drops.

5 Limitations and Future Work

Our feature disentanglement is inherently partial, as visual features may still carry semantic information. Achieving full disentanglement, enabling visual matching across semantically different objects, remains a promising future direction. The quality of the learned features also depends on 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 to address this via multi-scale aggregation and higher-resolution extraction. Lastly, our current framework targets structural and appearance-level inconsistencies. Extending it to 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 components. Using an automated dataset pipeline with controlled visual inconsistencies, we trained a contrastive architecture to separate the two. This enables visual correspondence across images, complementing semantic matching, and supports fine-grained analysis. Leveraging this, we introduced a metric that quantifies and localizes inconsistencies in subject-driven generation. Our approach outperforms existing metrics, offering a more reliable and interpretable evaluation framework.

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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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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\n\nQUESTIONS:\nPlease see weaknesses.\n\nLIMITATIONS:\nPlease see weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-10"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nFollowing 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.\n\nLIMITATIONS:\nLimitations have been discussed. Potential negative societal impact has not been discussed.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-21"}

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

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nA 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-08"}