Title: Fast Data Attribution for Text-to-Image Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: Data Attribution

Influence Estimation

Text-to-Image models

Image Retrieval
Abstract: Data attribution for text-to-image models aims to identify the training images that most significantly influenced a generated output. Existing attribution methods involve considerable computational resources for each query, making them impractical for real-world applications.  We propose a novel approach for scalable and efficient data attribution. Our key idea is to distill a slow, unlearning-based attribution method to a feature embedding space for efficient retrieval of highly influential training images. During deployment, combined with efficient indexing and search methods, our method successfully finds highly influential images without running expensive attribution algorithms. 
We show extensive results on both medium-scale models trained on MSCOCO and large-scale Stable Diffusion models trained on LAION, demonstrating that our method can achieve better or competitive performance in a few seconds, faster than existing methods by 2,500x - 400,000x.  Our work represents a meaningful step towards the large-scale application of data attribution methods on real-world models such as Stable Diffusion.

Fast Data Attribution for Text-to-Image Models

Abstract

Data attribution for text-to-image models aims to identify the training images that most significantly influenced a generated output. Existing attribution methods involve considerable computational resources for each query, making them impractical for real-world applications. We propose a novel approach for scalable and efficient data attribution. Our key idea is to distill a slow, unlearning-based attribution method to a feature embedding space for efficient retrieval of highly influential training images. During deployment, combined with efficient indexing and search methods, our method successfully finds highly influential images without running expensive attribution algorithms. We show extensive results on both medium-scale models trained on MSCOCO and large-scale Stable Diffusion models trained on LAION, demonstrating that our method can achieve better or competitive performance in a few seconds, faster than existing methods by 2,500 × ∼ 400,000 × . Our work represents a meaningful step towards the large-scale application of data attribution methods on real-world models such as Stable Diffusion.

1 Introduction

Data attribution for text-to-image models [3, 4, 5, 6, 7, 8, 9, 10] aims to identify training images that most significantly influence generated images. For a given output, influence is defined counterfactually: if influential images were removed from the training dataset, the model would lose its ability to generate the output . However, directly identifying influential images by retraining models with all possible training subsets is computationally infeasible.

To make attribution more tractable, researchers have proposed several approaches to approximate this process. One common strategy is to estimate individual influence scores by examining the effect of removing single training images. Gradient-based methods, such as influence functions [11, 12, 13], approximate this removal process by calculating the inner product of the training and test images' model gradients, reweighted by the inverse Hessian. Unfortu-

~2,500x  speedup

~437,000x  speedup

nately, computing and storing these gradients per training image is costly. Projecting gradients into lower dimensions reduces accuracy further [14], while still being computationally prohibitive for models with billions of parameters [15]. Another approach approximates influence by directly 'unlearning' individual images, which improves accuracy but significantly increases runtime [1, 16, 17]. None of the above methods is practical for applications requiring fast test time performance. Furthermore,

while text-to-image platforms typically charge users 5-10 cents per image [18, 19], existing attribution methods could cost way more per query due to heavier computation needs. This significant cost disparity, costing orders of magnitude more than generation revenue, prevents real-world deployment.

In this work, we propose a new method to overcome the tradeoff between computational complexity and attribution performance. Our key idea is to distill a slow, unlearning-based attribution method to a feature embedding space that enables efficient retrieval of highly influential training images. More concretely, leveraging off-the-shelf text and image encoders, we learn our embedding model's weights via learning-to-rank, supervised by the unlearning-based attribution method [1]. During deployment, our method scales to hundreds of millions of training images at a low cost.

However, this approach introduces several technical challenges. First, data curation is computationally intensive - collecting influence scores across large datasets (e.g., LAION-400M [20]) consumes significant GPU hours. To address this, we adopt a two-stage retrieval strategy: rapidly indexing [21] to select top candidates and then reranking them with more precise attribution methods. We show that our embedding function can be effectively learned from these reranked subsets. Second, designing objective functions to accurately predict dense rankings is non-trivial, as most learning-to-rank literature focuses on ranking fewer items. After evaluating several learning-to-rank objectives, we develop a simplified yet effective loss function.

We validate our method through extensive experiments. First, we benchmark our approach on MSCOCO using counterfactual evaluation, showing that our learned feature embeddings achieve strong attribution performance. We further compare runtime and storage costs against recent methods, as shown in Figure 1, demonstrating the best tradeoff between computational complexity and performance. Finally, we apply our approach to Stable Diffusion, achieving superior predictive performance on held-out test data. Our code, models, and datasets are at: https://peterwang512. github.io/FastGDA .

In summary, our contributions are:

A new scalable approach to data attribution that employs a learning-to-rank method to learn features related to attribution tasks.

Asystematic study of key components that ensure efficient and effective learning-to-rank, including tailored objective functions and a two-stage data curation strategy.

Extensive benchmarking against existing methods, demonstrating superior performance in both computational efficiency and attribution accuracy.

The first successful application and evaluation of an attribution method on a large-scale model such as Stable Diffusion trained on LAION.

2 Related Works

Data Attribution. The interplay between training data and models has been extensively studied across various domains, including data selection [22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32], dataset mixing [33, 34, 35, 36, 37, 38], and data valuation [39, 40, 41, 42, 43, 44, 45, 46, 47]. In contrast, we focus on identifying training images influential to a given generated output in text-to-image models. For classification tasks, prior methods typically average contributions over models retrained on random subsets [48, 39, 40, 49, 46], inspired by Shapley values [50], or estimate gradient similarity across training checkpoints [51, 52].

Influence functions [12, 11] are also widely used, as they require no retraining or intermediate checkpoints. The main idea is to approximate changes in the model output loss after perturbing the training datapoint. This requires estimating gradients and an inverse Hessian matrix of the model parameters. To make evaluating the Hessian tractable, previous methods explore inverse Hessianvector products [12], Arnoldi iteration [13], and Gauss-Newton approximation [14, 53, 54, 2, 15, 55], which is most commonly adopted for text-to-image models for its efficiency.

Despite these advances, challenges persist from high storage costs for model gradients across the dataset. To address this, researchers proposed several solutions, including estimating influence at test time [54, 1, 55, 16, 17] without storing gradients at a cost of increased runtime, and gradient dimensionality reduction through random projections [14, 53, 2] or low-rank adaptors [56, 15]. These methods introduce a tradeoff between computational resources (storage, runtime) and attribution

performance, as heavier approximations worsen attribution [2, 1]. To address this, we propose learning a feature space specifically trained to predict attribution results obtained by computationally expensive but accurate methods.

Learning to Rank (LTR) aims to predict item rankings [57] and are broadly categorized into pointwise, pairwise, and listwise approaches. Pointwise methods independently score each item, applying methods such as multiclass classification [58], ordinal loss [59], or regression [60]. Pairwise methods are trained to predict relative orderings between pairs, leveraging boosting [61], SVM [62], and neural networks with cross-entropy [63] or rank-based losses [64]. Listwise methods optimize the entire list directly [65, 66, 67, 68], aiming to improve ranking metrics such as NDCG [69], mAP, Precision@k, and Recall@k [70]. LTR benchmarks typically involve ranking fewer than 1,000 items [71, 72]. In contrast, our task involves ranking significantly larger lists (e.g., 10,000 items). For simplicity, we focus on a pointwise method and propose a simple, effective variant that outperforms strong baselines in our application.

Representation Learning. The representations learned in deep networks [73, 74, 75] often translate across tasks [76, 77, 78]. For example, a representation learned from an image classification dataset [79] can be efficiently repurposed for tasks, from detecting objects [76] to modeling human perception [80, 81]. Unsupervised [82, 83] or self-supervised learning [84, 85, 86, 87, 88] methods aim to learn image representations without text labels. A particularly effective training objective is the contrastive loss [89, 90, 91, 92, 93], where co-occurring data or two views of the same datapoint (such as text and image in CLIP [94]), are associated together, in contrast to other unrelated data. Previous work by Wang et al. [95] evaluate and tune a representation towards attribution in the customization setting. In our work, we leverage counterfactually predictive unlearning methods and learn representations for general-purpose attribution.

3 Method

Our goal is to learn an attribution-specific feature using a collection of attribution examples, generated from an accurate but slow attribution method. Section 3.1 overviews the attribution algorithm we adopt and explains our data collection process. In Section 3.2, we introduce our learning to rank objective for training attribution-specific features.

3.1 Collecting Attribution Data

Following the convention from Wang et al. [1], a text-to-image model θ 0 = A ( D ) is trained with learning algorithm A on dataset D = ( x i , c i ) N i =1 , where x and c denote an image and its conditioning text, respectively. The model θ 0 generates a synthesized image ˆ x conditioned on caption c . To simplify notation, we denote a synthesized pair as ˆ z = (ˆ x , c ) and a training pair as z i = ( x i , c i ) .

Data attribution aims to attribute the generation ˆ z to its influential training data. We define a data attribution algorithm τ , which assigns an attribution score τ (ˆ z , z i ) to each training datapoint z i , with higher score indicating higher influence. We assume that τ has access to all training data, parameters, and the loss function, but omit them for notational brevity.

Attribution by Unlearning. We collect attribution examples using Attribution by Unlearning (AbU) [1], one of the leading attribution methods for text-to-image models, shown in the top of Figure 2. The method runs machine unlearning [96, 97, 98, 99] on the synthesized image and evaluates which of the training images are forgotten by proxy. The authors observe that the forgotten training images are more likely to be influential. Intuitively, this reverses the question: which training points must the model forget to unlearn the synthesized image?

Formally, to unlearn the synthesized image and evaluate which training images are 'forgotten', we start with the pre-trained model θ 0 , and apply certified unlearning [96] on the synthesized sample ˆ z :

this creates unlearned model θ -ˆ z , where α is the step size, N is the size of the training set, and F is the Fisher information matrix. L is the training loss of text-to-image diffusion model, as defined in Appendix A.1. This constitutes a one-step Newton update to maximize the loss of the synthesized

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sample through gradient ascent, regularized by elastic weight consolidation (EWC) loss [100] to retain other knowledge.

After unlearning, the attribution score is assigned to each training sample z by evaluating the training loss change, before and after unlearning:

The loss change in τ (ˆ z , z ) measures how much the unlearned model loses its capability to represent each training image. The method is effective but computationally expensive, as it requires evaluating the unlearned model on every training image at runtime.

Attribution by Unlearning+. The Fisher information matrix of a deep network is difficult to compute and store, as its size grows quadratically with the number of model parameters. In AbU, Wang et al. [1] simply approximate this using a diagonal approximation. We find that replacing the diagonal approximation with the Eigenvalue-corrected Kronecker Factorization (EK-FAC) approximation [101] yields better performance and refer to this as AbU+ .

While this method is accurate, it takes a significant amount of runtime. For example, it takes 2 hours to process a single query for a training dataset with 100K images. We aim to distill this slow but accurate method into a fast embedding next.

Two-stage data collection. Recall that our goal is to collect a dataset of attribution scores between synthesized and training examples. However, the expensive runtime prohibits collecting data across all training samples. We resolve this using a two-stage data collection process to improve efficiency. We note that most training samples do not meaningfully contribute to a synthesized example, so evaluating all training samples D for a given query is unnecessary. To narrow the search space, we first obtain the K nearest neighbors of the synthesized sample using off-the-shelf features D ˆ z ⊂ D . We then collect attribution scores for them S ˆ z = { τ (ˆ z , z k ) } z k ∈D ˆ z . As long as the subset contains the most influential images, the data is suitable for learning to identify them. Guo et al. [102] also explored a similar two-stage approach, but to reduce runtime cost for attribution, whereas we apply it to speed up data collection.

Our final dataset contains query images, corresponding training subset, and collected attribution scores: ˆ z , D ˆ z , S ˆ z .

3.2 Learning to Rank From Attribution Data

Given query ˆ z and attribution scores S ˆ z in dataset D ˆ z , we obtain normalized ranked scores π i ˆ z ∈ [ 1 K , 2 K , ..., 1] for each training sample z i , where the most influential sample is assigned 1 K , and the least assigned 1 .

Predicting attribution rank. We then learn a function r ψ (ˆ z , z i ) to efficiently predict the true rank π i ˆ z of the sample, as shown in the bottom branch of Figure 2. We parameterize r ψ (ˆ z , z i ) = cos ( f ψ (ˆ z ) , f ψ ( z i )) to compare cosine similarity across feature embeddings. This enables one to store embeddings f ( z i ) and perform simple feature similarity search at attribution time.

We parameterize feature embedding f ψ = g ψ ◦ ϕ , where ϕ is a pretrained network and g ψ is a learned MLP that maps pretrained features towards an embedding focused on attribution.

Learning to rank by cross-entropy. We adopt the pointwise learning-to-rank approach [58, 59, 60] and apply cross-entropy loss to optimize each rank prediction individually. We then learn r ψ through binary cross-entropy loss ℓ BCE:

where we randomly sample synthesized images from a collection ˆ Z and training images D ˆ z , and function σ α,β ( x ) = 1 1+ e -( αx + β ) is a logit with a learned affine scaling. We also explored other alternatives. Direct regression [60, 103] performs poorly, whereas Ordinal regression loss from Crammer and Singer [59] delivers competitive ranking performance. However, implementing the loss requires changes in our rank function designs, such that it no longer supports fast feature similarity search at inference time. A full derivation of the ordinal loss, along with more details of the alternative ranking losses, could be found in the supplementary material.

Sampling strategies. To further improve accuracy and reduce attribution-score computation costs, we modify our sampling of training examples in two ways. First, sampling only from topK neighbors focuses the model on fine-grained distinctions but hurts its ability to recognize unrelated images. To counteract this, with probability 0.1, we draw a random example z outside the neighbor set and assign it the worst rank: π i ˆ z = 1 . This encourages the model to rank non-neighbor images last.

Second, computing attribution scores on all K neighbors at every step still incurs O ( K ) cost. We therefore subsample M < K candidates uniformly at each iteration, compute true ranks only on that subset, and train using those M examples. In practice, we find that choosing M ≈ 0 . 1 K (or even smaller) preserves ranking performance, while greatly reducing per-query curation time. We provide a detailed study on how each strategy affects accuracy and efficiency in Section 4.1.

4 Experiments

We address two questions in this section: (Q1) Rank prediction : Can our tuned feature improve attribution-rank accuracy? (Q2) Counterfactual forgetting : Does better rank prediction translate into stronger counterfactual prediction, the 'gold standard' to evaluate attribution? This counterfactual prediction verifies whether the model forgets about the synthesized sample when its influential data are removed. We evaluate Q1 on both MSCOCO and Stable Diffusion models, and Q2 on MSCOCO only, since repeated retraining on Stable Diffusion is computationally prohibitive.

4.1 MS-COCO models

We follow previous testbeds, using the same latent diffusion model [4] trained on the 100k MSCOCO dataset [104]. The model is the same as used in AbU [1] and Georgiev et al. [53], and we use the test set from AbU, which contains 110 synthesized queries.

Rank prediction metrics (Q1). We collect ground-truth ranks with AbU+ on the 110 queries, where we rank every training data point. To train our model, we generate 5000 images, using other prompts in MSCOCO. To build our dataset, for each query, we select the top 10k nearest neighbor candidates in the DINO feature space and rank candidates with AbU+, totaling 50M attribution ranks. We take 4900 queries for training and 100 for validation. We measure rank-prediction accuracy on the 110-query test set by mean average precision (mAP) in a binary-retrieval setup: the top L

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training points from the ground truth rank are labeled positive. We denote this as mAP ( L ) for L ∈ { 500 , 1000 , 4000 } . We note that this metric is different from conventional mAP@ K , where AP is evaluated for the first K data points only. Instead, we use L only to define positives while evaluating over the full training set. We also report other ranking metrics in Appendix C.2.

Counterfactual forgetting metrics (Q2). Following AbU, for each test query, we remove the top k influential images ( k =500 , 1000 , 4000 ), retrain the model from scratch , and measure how much the retrained model forgets via:

Loss change ∆ L (ˆ z , θ ) : the increase in query ˆ z loss under the retrained model relative to the original pretrained model.

Generation deviation ∆ G θ ( ϵ, c ) : the difference between the original and re-generated image (using the same noise seed ϵ ), where larger deviation indicates stronger forgetting [53]. As in AbU, we report the difference in mean square error (MSE) and CLIP similarity.

Note that this test is expensive, as it involves retraining a model for each combination of synthesized image and attribution method, but is a gold-standard counterfactual test for attribution. We select and study various design choices of our ranking model using the inexpensive rank-prediction metrics, then apply the much costly counterfactual forgetting evaluation to confirm that the tuned feature space indeed improves attribution.

Which feature space to tune? Figure 3 (left) reports the effect of tuning different feature spaces. Before tuning, using a text embedding (CLIP-text) beats image-only embeddings (DINO or CLIP). However, after tuning, the image-only embeddings achieve higher performance than text-only. The best results come from concatenating DINO and CLIP-Text: after tuning this combined feature, we achieve the highest prediction accuracy, harnessing both visual and text signals.

Learning-to-rank objectives. Figure 3 (right) compares three objective functions. We first implement a simple mean-squared-error (MSE) loss to regress ground-truth ranks, as used in previous work [60, 103]. However, this formulation fails to converge and yields poor retrieval accuracy.

Next, we find that ordinal loss [59] delivers competitive ranking performance. However, as mentioned in Section 3.2, this loss function is incompatible with fast feature search at inference time. Finally, we adopt a cross-entropy loss on rank labels (normalized by K ), which preserves the efficient cosinesimilarity search, while matching the ordinal loss in mAP ( L ) across all thresholds L . Please see additional comparisons in Appendix C.2.

Data scaling. Figure 4 (left) shows how rank-prediction accuracy improves as we increase the number of training samples. We see steep gains when moving from small to moderate dataset sizes, followed by diminishing returns as size grows further. This 'elbow' behavior suggests that a few thousand attribution query examples suffice to capture the bulk of the ranking signal.

Sampling images outside the neighbor set. Figure 4 (right) explores the impact of injecting random 'negative' samples-images not among the topK neighbors-during training. A modest 10% sampling rate of non-neighbors raises accuracy from 0.709 to 0.724, but higher rates steadily degrade performance. This pattern suggests that occasional negatives help the model maintain global

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distances (assigning truly unrelated images worse ranks), yet over-sampling them distracts from the core task of fine-grained ranking among the most relevant candidates.

Random sampling of nearest-neighbor candidates. Figure 5 (left) fixes the number of queries and varies the fraction of topK candidates used for training. We observe that rank-prediction accuracy drops mildly until the candidate subset falls below 20%, indicating that reliable relative rankings can be learned from a small fraction of neighbors. Figure 5 (right) explores a fixed compute-budget regime (constant total queries × selected candidates) of 2.45M query-training attribution ranks. (Recall our complete dataset has 50M). In this setting, one can reduce the number of training images sampled per query and increase the number of distinct queries. We find that this boosts accuracy. This trade-off suggests that under budget constraints, it is better to sample fewer candidates per query to evaluate more queries. Accordingly, we adopt a 20% neighbor-sampling rate for collecting attribution examples in our Stable Diffusion experiments (Section 4.2).

Runtime vs. counterfactual performance. In Table 1, we compare performance and run-time against several baselines. Runtime and storage are shown on the left. We gather runtime values for each method by warmstarting 10 queries (if applicable) and then averaging over 20 queries. We run on a single Nvidia A100 80GB for benchmarking. The right columns of Table 1 show the counterfactual performance - retraining a model from scratch without a subset of identified images from an attribution method - and seeing if the targeted synthesized image is damaged in the subsequent model (Loss deviation) or generation (Image deviation).

The influence and unlearning-based methods offer tradeoffs between latency, performance, and storage. Unlearning-based AbU, and our improved AbU+ variant achieve the highest attribution performance. However, the method requires hours to run, due to repeated function evaluations on each training image. While the influence function-based methods are faster, they are lower performing, with D-TRAK performing relatively well at fast inference times (46.7 sec). However, note that this is still longer than the time to generate an image (21.5 sec for 50-DDIM steps), and the method requires storage of the preconditioned gradients on the training set (30GB), which is larger than the images in the training set itself (19 GB).

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On the other hand, embedding-based methods offer low-storage (100s of megabytes), depending on the size of the embedding, and fast run-times (tens of milliseconds). As such, these methods can be tractable, as they are cheap to create relative to the generation itself. As the models we test are learning a mapping from text to image, we explore both text and image-based descriptors. As a baseline, we test if a text descriptor is sufficient for attribution, using the CLIP text encoder. While it performs better than random, text-only is not sufficient, and the visual content is indicative of which training images were used. While one can use an off-the-shelf visual embedding and achieve visually similar images from the training set, they are not guaranteed to be counterfactually predictive and appropriate for attribution either. Previous work [1] found DINO to be a strong starting point.

From the ranking evaluation above, for our method, we select the best-performing model, which is tuned DINO+CLIP Text feature with 0.1 probability sampling images outside the neighbor set, using all the data within the neighbor set. In Figure 1, we plot the attribution performance (loss change with k = 500 ) vs. throughput (reciprocal of run-time). Our method achieves strong attribution performance overall, comparable to influence-function-based methods, at orders of magnitude higher speed. We show qualitative results in Figure 7 (left) and provide more results in Appendix C.2.

4.2 Stable Diffusion

We use Stable Diffusion v1.4 [4, 105] for our experiments. We collect generated images and captions from DiffusionDB [106] as attribution queries. Since retraining is prohibitive for such large models, we only report rank prediction metrics in this setting. In Figure 7 (right), we show a qualitative example of DINO + CLIP Text feature before and after tuning.

Rank prediction metrics (Q1). Same as the setup in MSCOCO (Section 4.1), we collect ground truth queries and collect attribution examples of them using AbU+, where the queries are split for training, validation, and testing. Different from MSCOCO, it is prohibitively costly to run AbU+ over the entire dataset (LAION-400M [20]). Hence, for each test query, we retrieve 100k nearest neighbor candidates from LAION-400M through CLIP index [21] and obtain ground truth attribution ranks for those candidates. For training and validation, following studies in Section 4.1, for each query, we retrieve 100k nearest neighbors. To collect more diverse queries within a fixed budget, we only rank a 20% random selection of them for supervision. We collect 5000 queries for training and 50 queries for validation, for a total of 101M query-training attribution ranks. We report mAP ( L )

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for L ∈ { 500 , 1000 , 4000 } , where we use the tuned features to rank the 100k candidates and check whether this prediction aligns with the ground truth.

Which feature space to use? Figure 6 (left) reports performance of different features before and after tuning. Similar to the findings in MSCOCO models, tuning feature spaces consistently improves the prediction results. However, in contrast to MSCOCO models, where image feature is an important factor for accurate rank prediction, we observe the opposite in Stable Diffusion. In fact, the results indicate that text features are strictly necessary to yield good tuning performance. Using DINO and CLIP-Image features significantly underperforms the ones with text features. This indicates that AbU+ tends to assign attribution scores that are more correlated with text feature similarities. We discuss this more in Appendix C.2

Data scaling. Figure 6 (right) reports performance improvements with respect to dataset size. Similar to the findings in MSCOCO models, there are steep gains from small to moderate dataset sizes, and the rate of gain decreases as size grows further. However, we note that even with the full dataset at 5000 queries (100M data points), the performance increase has not saturated. With more compute, one could collect more data to improve ranking performance.

5 Discussion, Broader Impacts, and Limitations

Data attribution is a quest to understand model behavior from a data-centric perspective. It can potentially aid practical applications such as compensation models, which could help address the timely issue surrounding the authorship of generative content [107, 108, 109, 110]. Our method reduces the runtime and storage cost of the data attribution algorithm, which is a step towards making data attribution a feasible solution for a compensation model.

While our work demonstrates a good tradeoff between compute resources and attribution performance, there are additional avenues for future work. First, the learning to rank approach does not distill the raw attribution score, just the relative ranking in the training set, so the degree of influence and how diffuse or concentrated the influence may be lost as well. Further exploring and characterizing the degree of influence is an area of future work. Secondly, as our method is distilled from a teacher method, failure modes will also be inherited. However, in this work, we have shown that future broader improvements in attribution methods can benefit a faster method through distillation. Besides developing a fast

attribution method for diffusion models, there are opportunities for applying attribution to other widely used models (e.g., flow matching [111, 112], one-step models [113, 114, 115, 116]) and making attribution more explainable to the end users.

Acknowledgments. We thank Simon Niklaus for the help on the LAION image retrieval. We thank Ruihan Gao, Maxwell Jones, and Gaurav Parmar for helpful discussions and feedback on drafts. Sheng-Yu Wang is supported by the Google PhD Fellowship. The project was partly supported by Adobe Inc., the Packard Fellowship, the IITP grant funded by the Korean Government (MSIT) (No. RS-2024-00457882, National AI Research Lab Project), NSF IIS-2239076, and NSF ISS-2403303.

References

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A Formulations

Here we expand the main paper's formulations with more details.

A.1 Diffusion Models.

Section 3 of the main paper describes our formulation, which uses the loss L of the generative model being used. Our experiments are on diffusion models, and we describe the loss term here. Diffusion models [117, 118, 119] learnsto reverse a data noising process, defined as x 0 , . . . , x T , where a clean image x 0 is gradually diffused to a pure Gaussian noise x T over T timesteps. At timestep t ∈ [0 , T ] , noise ϵ ∼ N (0 , I ) is blended into the clean image x 0 to form a noisy image x t = √ ¯ α t x 0 + √ 1 -¯ α t ϵ , where ¯ α t defines the noise schedule. The training loss of the diffusion models is to encourage accurate denoising of x t by predicting the noise:

where w t is a weighting term typically set to 1 [117], ϵ θ is the denoising model, and c is the text condition. At inference, the denosing model ϵ θ takes in random Gaussian noise and gradually denoises it to the learned data distribution.

Evaluating the diffusion loss in Equation 4 requires a Monte-Carlo estimate, and we estimate it by taking averages over different noise samples and timesteps. Section B provides implementation details of the Monte-Carlo estimate.

A.2 Influence function.

In the main text, we discussed that influence function methods suffer from a tradeoff between computation cost and attribution performance. To understand this, we briefly recap the influence function from Koh and Liang [12]. Let

where R is the full training loss, { z i } are the training examples, and H is the Hessian of the training loss. We estimate the effect of removing a single training point z via a small perturbation ϵ :

where θ -z ,ϵ is the optimal model under the perturbed loss. θ -z ,ϵ statistfies the stationary condition:

From Equation 7, taking a Taylor approximation around θ 0 and assuming convergence in model training, Koh and Liang show that the change in model weight ∆ θ -z = θ -z ,ϵ -θ 0 would be:

Influence function is then defined by the rate of change of loss on the testing point (or synthesized image) ˆ z with respect to the perturbation ϵ on the training point z . By the chain rule:

To make the computation of Equation 9 tractable, recent works [14, 2, 54, 53, 15, 55] estimate the Hessian by Generalized Gauss-Newton (GGN) approximation, which essentially replaces the Hessian with the Fisher information matrix.

However, even with this approximation, the tradeoff between computation cost and attribution performance remains. The size of the gradient (e.g., ∇ θ L ( z , θ 0 ) ) is too big to store. One could either sacrifice runtime by computing training point gradients on the fly upon each query [54, 55, 1], or projecting gradients to a much smaller dimension that sacrifices performance [14, 2, 15].

A.3 Eigenvalue-Corrected Kronecker-factored Approximate Curvature (EKFAC)

We collect attribution data using AbU+, an improved variant of AbU [1], by replacing the diagonal Fisher-information approximation with EKFAC [101]. Below is a brief overview of EKFAC. We first discuss KFAC, the basis of EKFAC.

KFAC (Kronecker-factored Approximate Curvature) consists of two core ideas to reduce the space complexity of FIM: (1) approximating the Fisher information matrix (FIM) blockwise, where each layer corresponds to a block, and (2) reducing each layer's FIM block to two smaller covariances. To illustrate the idea, for a linear layer (no bias) at layer i :

where φ i is an element-wise nonlinearity (e.g., ReLU [120]), a i -1 ∈ R d in , and W i ∈ R d out × d in . Writing g i = ∇ s i L , the weight gradient is

where vec ( · ) flattens a 2D matrix column-wise into an vector. We use the fact that a flattened outer product of two vectors can be written as a Kronecker product of the two vectors. The Fisher block becomes

The last step in the Equation 12 leverages properties of the Kronecker product. The final approximation reduces space complexity from O ( d 2 in d 2 out ) to O ( d 2 in + d 2 out ) , since the only requirement is to compute the two smaller covariance matrices.

EKFAC refines KFAC by correcting the eigenvalues without increasing space complexity. We first take the eigen decompositions of the two covariance matrices:

Then the FIM block becomes:

EKFAC fixes the shared eigenbasis U A ⊗ U B but re-estimates the diagonal eigenvalue matrix S by projecting empirical gradients into this basis:

Since U A ∈ R d in × d in , U B ∈ R d out × d out , and S is diagonal of size d in d out , the O ( d 2 in + d 2 out ) cost is preserved while reducing approximation error [101].

In practice, we sample training images, noise vectors, and timesteps to estimate A , B , and the corrected eigenvalues S . Implementation details, including convolutional extensions, appear in Section B.

A.4 Learning-to-Rank Objectives

In Section 4 of the main text, we compare our cross-entropy objective with two other alternatives: MSE loss and ordinal loss. For the two losses, we follow the conventions from Pobrotyn et al. [103].

Using the notations from Section 3 of the main text, we want to predict the normalized ranked scores π i ˆ z ∈ [ 1 K , 2 K , ..., 1] for each training sample z i , where the most influential sample is assigned 1 K , and the least assigned 1 .

MSE loss. This simply regresses the normalized rank:

where r ψ and the affine-scaled sigmoid σ α,β ( x ) = 1 / ( 1 + e -( αx + β ) ) are as defined in Section 3.2. As reported in Section 4.1, training with this loss fails to converge and yields poor retrieval accuracy. We conjecture that regressing dense ranks over 10 4 candidates (vs. the usual 5 -100 ) [60, 103] makes the MSE formulation ill-suited to our setting.

Ordinal loss. In the ordinal-regression framework [59], each ground-truth (unnormalized) rank r ∈ { 1 , . . . , K } is converted into a binary vector of length K -1 via

Our ranker r ψ now outputs K -1 logits ℓ 1 , . . . , ℓ K , which we transform into probabilities

The ordinal loss is the sum of binary cross-entropies over thresholds:

At inference, we recover a scalar rank via

Directly using K ≈ 10 4 would require 10 4 binary heads and thresholds, which is memory-prohibitive. Instead, we coarsen the rank range into B = 10 equal-width bins and apply ordinal loss over the B -1 thresholds, reducing the number of binary classifiers to 9, while preserving most of the ordinal structure.

In contrast to our setup in Section 3 of the main text, the ordinal approach requires the network to emit multiple feature heads (one per threshold), compute a separate cosine similarity, affine transform, and sigmoid for each, and then sum all resulting probabilities to recover a scalar rank. This multi-step, multi-head pipeline increases parameters and computation and slows inference. Since our crossentropy-based method matches ordinal loss in accuracy while only requiring one feature vector with no extra summation, we adopt it as our main method due to its simplicity and efficiency.

B Implementation Details

B.1 MSCOCO Models

Collecting attribution data. We follow the AbU+ procedure from Wang et al. [1], performing a single unlearning step that updates only the cross-attention key/value layers. When using EKFAC in place of a diagonal Fisher approximation, we set the Newton step-size to 0 . 01 /N , with N = 118287 (the size of the MSCOCO training set).

Within that step, we estimate the diffusion loss via Monte Carlo by sampling and averaging over 50,000 independent (noise, timestep) pairs. Attribution scores are then defined as the difference in loss between the unlearned and original models. To compute each loss, we evaluate at 20 equally spaced timesteps; at each timestep, we average the five losses obtained by combining the image with each of its five corresponding captions (using different random noise for each caption).

Training rank models. Our rank model is a 3-layer MLP with hidden and output dimensions of 768. We optimize using AdamW (learning rate 10 -3 , default betas 0 . 9 , 0 . 999 , weight decay 0.01) for 10 epochs on the training set, without any additional learning-rate scheduling.

B.2 Stable Diffusion

Collecting attribution data. Similar to MSCOCO (Section B.1, we run AbU+ by performing a single unlearning step that updates only the cross-attention key/value layers. We set the Newton step-size to 0.002, and N = 400 , 000 , 000 (the size of LAION-400M [20]).

Within the unlearning step, we estimate the diffusion loss via Monte Carlo by sampling and averaging over 4,000 independent (noise, timestep) pairs. To compute the loss for attribution scores, we evaluate at 10 equally spaced timesteps, and at each time step, we sample 1 random noise and use the corresponding caption to assess the loss value.

Training rank models. We follow the exact same training recipe as the rank model for MSCOCO, which is described in Section B.1.

B.3 Baselines

We describe the baselines used in our experiments. Most baselines follow those reported in AbU [1].

Pixel space. Following JourneyTRAK's implementation [53], we flatten the pixel intensities and use cosine similarity for attribution.

CLIP image and text features. We use the official ViT-B/32 model for image and text features.

DINO [92]. We use the official ViT-B/16 model for image features.

DINOv2 [121] We use the official ViT-L14 model with registers for image features.

CLIP (AbC) and DINO (AbC) [95]. We use the official models trained on the combination of object-centric and style-centric customized images. CLIP (AbC) and DINO (AbC) are selected because they are the best-performing choices of features.

TRAK [14] and Journey TRAK [53]. We adopt the official implementation of TRAK and JourneyTRAK and use a random projection dimension of 16384, the same as what they use for MSCOCO experiments.

D-TRAK [2]. We follow the best-performing hyperparameter reported in D-TRAK, using a random projection dimension of 32768 and lambda of 500. We use a single model to compute the influence score.

AbU [1] and AbU+. For AbU, we follow the default hyperparameter reported in the paper. For AbU+, we follow the same hyperparameters as the ones used for data curation, which is described in Section B.1.

Licenses. Below we list the licenses of code, data, and models we used for this project.

MSCOCO source model: collected from Georgiev et al. [53], which is under the MIT License.

MSCOCO dataset: Creative Commons Attribution 4.0 License.

MSCOCO synthesized images testset: collected from AbU [1], which is under CC BY-NC-SA 4.0.

Stable Diffusion: CreativeML Open RAIL++-M License.

DiffusionDB images: MIT License.

CLIP model: MIT License.

DINO model: Apache 2.0.

DINOv2 model: Apache 2.0.

AbC model: CC BY-NC-SA 4.0.

TRAK code: MIT License.

EKFAC code: Taken from the Kronfluence codebase, which is under Apache 2.0 License.

C Additional Analysis

C.1 Compute Cost

Our experiments are all done by NVIDIA A100 GPUs. Below, we describe the runtime cost of the components of our project.

Data curation. On MSCOCO, attributing 100k candidates for 110 test queries at 2 hours/query took 220 GPU-hours, while curating 10k candidates for 5,000 train/val queries at 0.25 hour/query took 1,250 GPU-hours. For Stable Diffusion, attributing 20k candidates for 5,050 train/val queries at 3 hours/query required 15,150 GPU-hours, and 140 test queries at 15 hours/query consumed 2,100 GPU-hours in total.

Training time for LTR models. Training one rank model on MSCOCO for 10 epochs takes approximately 1 GPU-hour, while training the Stable Diffusion rank model for the same number of epochs requires about 6 GPU-hours.

C.2 Additional Results

Effectiveness of K-NN retrieval using off-the-shelf features. We study the effect of sampling from topK neighbors for data collection, described in Section 3.2. As in Table 1, we report in Table 2 counterfactual metrics for AbU+ ran on (1) the full training set ( AbU+ ) and (2) top neighbors after K-

Table 2: Counterfactual leaveK -out evaluations. We report loss deviation and image deviation metrics at K ∈{ 500 , 1000 , 4000 } . Values are scaled as indicated in the headers; gray shows the standard error.

NN( AbU+(K-NN) ). We also copied numbers from D-TRAK (2nd best teacher) and random baselines as reference. We find that applying K-NN retrieval does not introduce a significant performance drop.

Predicting absolute attribution scores directly. We explore directly predicting the absolute attribution instead of ranks, where the MLP regresses the absolute attribution scores (normalized by mean and standard deviation). This regression leads to worse ranking performance (0.009 mAP (1000) ) than our learning-to-rank method (0.724 mAP (1000) ).

Rank prediction evaluation. In Section 4, we only include mAP ( L ) with one L value. Here, we include mAP (500) and mAP (4000) for MSCOCO experiments in Figure 10,11,12,13,14,15. We include mAP (500) and mAP (1000) for Stable Diffusion experiments in Figure 16,17. All trends are similar to the ones reported in the main text.

MSCOCOqualitative results. Figure 8 presents additional MSCOCO examples. Our rank model can retrieve influential training images as AbU+. Their influence is confirmed-removing those images, retraining, and regenerating the query leads to large deviations.

Stable Diffusion qualitative results. Figure 9 shows additional examples on Stable Diffusion. Our model's top attributions follow those of AbU+, emphasizing prompt content over pure visual similarity. Since full counterfactual retraining is infeasible at this scale, we cannot definitively verify that these attributions reflect true influence. However, as shown in Section 4.2, our method reliably predicts AbU+'s ranks, indicating a consistent attribution signal.

Establishing the ground-truth validity of this signal is left to future work. Prior studies suggest that influence-based methods may weaken on very large models (e.g., LLMs) [122, 123]. However, developing more robust attribution algorithms for large-scale models-and distilling a rank model from stronger teacher methods using our method-are all promising directions ahead.

Generated Sample

Ground Truth

DINO +

CLIP Text

'The dining

table near the

kitchen has a

bowl of fruit

on it.'

Ours

Top 8

Attributed Training Sample

Remove Top 500

Retrain & Regenerate

Generated Sample

Ground Truth

DINO +

CLIP Text

'a flock of

goats and

some men

watching

them'

Ours

Generated Sample

Ground Truth

DINO +

CLIP Text

'A person in a

helmet is

riding a

skateboard.'

Ours

Generated Sample

Ground Truth

DINO +

CLIP Text

'a living room

that has some

couches and

tables in it'

Ours

Generated Sample

'thunder

warrior helmet,

unreal engine,

8 k, ultra

realistic, ultra

detail'

DINO +

CLIP Text

Ours

Ground Truth

Generated Sample

'A lunar sage

with his

magical owls

looking out

into the

reflections

[…]'

DINO +

CLIP Text

Ours

Ground Truth

'BFV Arras

Head'

'Sallet Helm

15e eeuws'

'Sallet Helm

15e eeuws'

'Ancient

Greek helmet

1 head helmet

0002.jpg'

'Adult Deluxe

Knight

Helmet'

Knight

Helmet'

Knight

Helmet'

'Character

Workshop -

Female

Knight'

'3d model of

medieval

knight

helmet'

'Viking

helmet 3D

model

realtime PBR'

'future soldier

armour 3d

model

turbosquid'

'armor suit

3d model

turbosquid'

'armor suit

3d model

turbosquid'

'3D armor

helmet model'

'[…]The Knight

is almost

entirely modeled

in Max […]'

'For Honor

- Knight 3D

Print 20765'

'football

helmet'

'Warrior

Platemail Armor

M dyed head

back.jpg'

'ww2 russian

helmet, Sniper

Damage

Helmets'

'Spartan

warrior wearing

helmet and red

cloak'

'[…] Helmet

Cosplay

Costume Hard

PVC Mask […]'

'Roman

Trooper

Helmet'

'Carbon fiber

stormtrooper

helmet, one of

a kind'

'Snappers

Advanced Facial

Rig for Maya

[...]'

'Dancing

Witches'

'Enchanted

dark forest

Wallpaper

Mural'

'Lake night

scene with

grass

silhouette'

'Painting -

Midnight

Green by

'Paint Nite:

Bonzai Moon'

'Follow Author

Holly Black On

Her Maleficent

Journey […]'

'Magic Moon

Tree Photo

Print'

'Beanna Cornibus

Canvas Print by

Canvas Artwork'

'surreal moon

owl collage by

moi'

Barn Owl Tree

Leaves Moon

Stars […]'

'NEW ITEM:

"Night Owl" -

11x14 Original

Acrylic […]'

'Sleepy Owl -

Fine Art

Print'

'Wise One

(Owl in the

Woods) - Fine

Art Original'

'[…] Blue Winter,

Mixed media, tree

with owl and

moon, hare'

'Owl And

Digital Art -

Owl […]'

'surreal

mixed media

Owl Art'

'We seek the

moon but find

only its

reflection […]'

'[…] Digital

Watching The

'Fantasy

landscape with

grass and a big

'[…] Titled

Waiting for the

romantic'

'[…] owl

painting, owl

wall art, owl

love […]'

'Colorful

Pictures Of

Owls Colorful

Owl Paintings'

'christian-

schloe-mirror'

'owl in the

night sky.'

Moonrise […]'

DINO

CLIP Image

CLIP T ext

DINO+CLIP Text

0.0

0.2

0.4

0.6

0.8

mAP (500)

0.369

0.256

0.250

0.372

0.607

0.608

0.392

0.663

mAP (500): Before vs. After Tuning

Before tuning

After tuning

DINO

CLIP Image

CLIP T ext

DINO+CLIP Text

0.0

0.2

0.4

0.6

0.8

mAP (4000)

0.494

0.388

0.408

0.497

0.664

0.670

0.611

0.704

mAP (4000): Before vs. After Tuning

Before tuning

After tuning

mAP (500)

0.8

0.6

0.4

0.2

0.0

Regression

Ordinal

Cross Entropy (Ours)

0.0

0.2

0.4

0.6

0.8

mAP (4000)

0.035

0.713

0.704

mAP (4000) Across Loss Functions

mAP (500) Across Loss Functions

0.670

0.005

Regression

Ordinal

0.663

Cross Entropy (Ours)

0

1000

2000

3000

4000

5000

Dataset Size (number of queries)

0.3

0.4

0.5

0.6

0.7

mAP (500)

0.296

0.411

0.512

0.582

0.614 0.627

0.643

0.650

0.663

mAP (500) vs. Dataset Size

0

1000

2000

3000

4000

5000

Dataset Size (number of queries)

0.45

0.50

0.55

0.60

0.65

0.70

mAP (4000)

0.456

0.564

0.619

0.659

0.677 0.684

0.692

0.696

0.704

mAP (4000) vs. Dataset Size

0.0

0.1

0.2

0.3

0.4

0.5

Probability of sampling non-candidates

0.58

0.60

0.62

0.64

0.66

0.68

mAP (500)

0.659

0.663

0.648

0.625

0.609

0.589

mAP (500) vs. Non-candidate Ratio

0.0

0.1

0.2

0.3

0.4

0.5

Probability of sampling non-candidates

0.600

0.625

0.650

0.675

0.700

0.725

mAP (4000)

0.600

0.704

0.689

0.672

0.662

0.648

mAP (4000) vs. Non-candidate Ratio

0

20

40

60

80

100

Random candidate selection ratio (%)

0.4

0.5

0.6

0.7

mAP (500)

0.663

0.650

0.625

0.595

0.560

0.501

0.439

0.367

Fixed number of queries

0

20

40

60

80

100

Random candidate selection ratio (%)

0.55

0.60

0.65

0.70

mAP (4000)

0.704

0.698

0.687

0.675

0.655

0.630

0.586

0.531

Fixed number of queries

20

40

60

80

100

Random candidate selection ratio (%)

0.44

0.46

0.48

0.50

0.52

0.54

mAP (500)

0.459

0.492

0.505

0.515

0.522

Fixed budget

20

40

60

80

100

Random candidate selection ratio (%)

0.58

0.60

0.62

0.64

0.66

mAP (4000)

0.587

0.612

0.630

0.631

0.638

Fixed budget

DINO

CLIP Image

CLIP T ext

DINO+CLIP Text

0.00

0.05

0.10

0.15

0.20

mAP (500)

0.007

0.011

0.035

0.007

0.014

0.019

0.044

0.041

mAP (500): Before vs. After Tuning

Before tuning

After tuning

DINO

CLIP Image

CLIP T ext

DINO+CLIP Text

0.00

0.05

0.10

0.15

0.20

mAP (1000)

0.013

0.019

0.053

0.013

0.025

0.032

0.067

0.064

mAP (1000): Before vs. After Tuning

Before tuning

After tuning

0

1000

2000

3000

4000

5000

Dataset Size (number of queries)

0.020

0.025

0.030

0.035

0.040

0.045

mAP (500)

0.023

0.029

0.034

0.038

0.039

0.040

0.041

mAP (500) vs. Dataset Size

0

1000

2000

3000

4000

5000

Dataset Size (number of queries)

0.04

0.05

0.06

mAP (1000)

0.038

0.047

0.053

0.058

0.061

0.062

0.064

mAP (1000) vs. Dataset Size

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

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

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

11. Safeguards

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

Answer: [NA]

Justification: We conduct experiments on existing generative models and data. We will release a data-attribution-specific feature encoder, which has a low risk for misuse.

Guidelines:

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

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

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

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

12. Licenses for existing assets

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

Answer: [Yes]

Justification: In the supplemental material.

Guidelines:

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

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

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

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

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

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

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

13. New assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer:[Yes]

Justification: We will release the code upon publication. No assets are provided for this submission.

Guidelines:

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

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

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

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

14. Crowdsourcing and research with human subjects

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

Answer: [NA]

Justification: This paper does not involve crowdsourcing nor research with human subjects. Guidelines:

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

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

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

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

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

Answer: [NA]

Justification: This paper does not involve crowdsourcing nor research with human subjects.

Guidelines:

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

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

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16. Declaration of LLM usage

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

Answer: [NA]

Justification: The core method development in this research does not involve LLMs as any important, original, or non-standard components.

Guidelines:

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

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

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel approach for scalable and efficient data attribution. The key idea is to distill a slow, unlearning-based attribution method to a feature embedding space for efficient retrieval of highly influential training images. Good experimental results have been reported. The authors claim \"our method can achieve better or competitive performance in a few seconds, faster than existing methods by 2,500× ∼ 400,000×.\"\nIf this is true, it's impressive. On the other hand, it's not possible to verify here. The author stated at the end of their abstract: \"Our code, models, and datasets will be made publicly available.\"\n\nSTRENGTHS AND WEAKNESSES:\nThe paper is okay up to the bottom of page 3. Then it becomes very vague to understand. \nFor example, in equation (1) at the bottom of page 3, the symbols in the equation are not adequately defined. Readers have to imagine what they are by following some traditions. However, it's the authors' responsibility to declare them. \nThe following content, from the top of page 4 to the end of Section 3, is presented in a handwaving style without providing a rigorous description. \nPerhaps the authors could improve by providing a clear and readable algorithm here. \nWithout knowing exactly what the method is, it is hard to appreciate its reported success. \n\nThis reviewer decides to recommend negatively to this submission, mainly because, based on the submitted work, it is hard to see what the proposed algorithm is. Additionally, since the work is not reproducible, it is impossible to validate the numerical success declared in the abstract, namely \"demonstrating that our method can achieve better or competitive performance in a few seconds, faster than existing methods by 2,500× ∼ 400,000×.\" Considering the paper's main contribution is the declared experimental success, which is not verifiable.\n\nQUESTIONS:\nThe paper reports good performance of their methods; however, the justification is not convincingly presented. The proposed method is not rigorously presented either. See early comments on this. Since the source codes are not made available, it is impossible to validate their results. If the authors want to make a convincing case, they may want to consider releasing their code and the code for their experiments, so that others can reproduce their results.\n\nLIMITATIONS:\nThere are some discussions under Section 5 Discussion, Broader Impacts, and Limitations", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a method for efficiently identifying the training images that most influenced a generated image from a text-to-image model. \nThe primary challenge with existing data attribution techniques is their high computational cost, which makes them impractical for large-scale use.\nThe authors' key contribution is an approach that significantly speeds up data attribution. \nThey achieve this by \"distilling\" the knowledge from a slow but relatively accurate attribution method, AbU+, into a lightweight feature embedding model. \nOnce trained, this new model can quickly find influential training images by performing a fast similarity search in its learned feature space, eliminating the need to run the expensive attribution algorithm for every new query.\nThe paper demonstrates through extensive experiments on both the MSCOCO and the large-scale LAION datasets that their method achieves attribution performance comparable to or better than existing methods, but is thousands of times faster.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n1. The paper addresses a highly relevant and timely problem. \nAs generative models become more widespread, methods for tracing generated content back to training data are crucial for addressing issues of copyright, artist compensation, and model transparency.\nExisting attribution methods are often too computationally expensive for practical deployment. \nThis work significantly contributes by dramatically reducing the runtime of attribution to a few milliseconds, making it faster. \n\n2. The authors validate their method on MSCOCO using counterfactual evaluation, which is the \"gold standard\" for attribution. \nThis involves removing the top-k identified training images, retraining the model, and measuring the degradation in its ability to produce the query image. \nThis is a costly but powerful way to demonstrate true influence. \nThe authors perform a systematic study of various design choices, providing clear justifications for their final model. This includes comparing different feature spaces to tune (e.g., DINO, CLIP Text, and their combination), evaluating multiple learning-to-rank objectives (MSE, Ordinal, Cross-Entropy), and analyzing the impact of data-scaling and sampling strategies. \n\n3. The successful application to Stable Diffusion/LAION demonstrates the method's scalability and its ability to handle modern, large-scale systems. \n\n4. The paper is well-written and easy to follow. The methodology is presented clearly, with helpful illustrations like Figure 2 that effectively communicate the overall pipeline. \n\nWeaknesses\n1. The core idea of distilling a slow data attribution method into a fast, feature-based model might not be novel. Previous papers have explored similar paradigms, and the authors did not discuss them [1, 2]. \n\n2. The LTR approach distills the relative ranking of influential examples but loses the raw, calibrated attribution scores produced by the teacher. This means that while the method can identify which images were most influential, it cannot quantify how much more influential one image is than another or determine if influence is concentrated in a few examples or diffuse among many. \n\n3. The authors did not adopt the widely used Linear Datamodeling Scores as on of their evaluation metrics, making it a bit difficult to compare with previous methods.\n\n[1] MATES: Model-Aware Data Selection for Efficient Pretraining with Data Influence Models. NeurIPS 2024\n\n[2] Data Selection via Optimal Control for Language Models. ICLR 2025\n\nQUESTIONS:\nSee weaknesses\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a scalable and efficient data attribution method for text-to-image generative models. The key idea is to distill an unlearning-based attribution method into a task-specific embedding space, enabling fast retrieval of influential training images via similarity search. The learned embedding is trained with a learning-to-rank objective over attribution scores generated by AbU+. The method is benchmarked on both MSCOCO and Stable Diffusion, demonstrating superior attribution accuracy while achieving high speedup over compared attribution methods.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengthens**\n1. The idea of learning an embedding space specifically optimized for data attribution is novel. It bridges the gap between accurate attribution and similarity-based methods.\n2. The proposed method achieves faster attribution compared to unlearning or influence-function-based methods, making it practical for deployment in real-world applications.\n3. The experiments are comprehensive. They include ablations on feature types, loss functions and sampling strategies.\n\n**Weaknesses**\n1. The proposed method is fully supervised by AbU+ scores. However, the paper does not analyze whether the teacher model itself is stable or accurate across modalities. Any failure case in AbU+ would be learned.\n2. While the method achieves fast inference, it sacrifices the ability to interpret absolute attribution scores (only rankings are learned). Furthermore, constructing the training set still requires significant computational cost.\n3. Both evaluation metrics (ranking prediction accuracy and counterfactual forgetting) are aligned with the design of this paper. It remains unclear whether these metrics correlate well with real attribution quality.\n\nQUESTIONS:\n1. How does the teacher model limit the overall performance?\n2. Why are these two metrics selected, and are others infeasible due to the problem setting?\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper addresses the computational bottleneck in data attribution for text-to-image models by proposing a learning-to-rank approach that distills slow but accurate attribution methods into fast feature embeddings. The authors leverage Attribution by Unlearning (AbU) as a teacher method and train a feature space that can identify influential training images through simple cosine similarity search. Their method achievessignificant speedup compared to existing approaches while maintaining competitive attribution performance.\n\nSTRENGTHS AND WEAKNESSES:\nstreghts first:\n- The paper tackles a critical challenge where existing attribution methods have oversized cost (compute/time), making real-world use infeasible. The proposed speedup makes attribution practical.\n- The two-stage data strategy (coarse retrieval followed by fine-grained ranking) balances computational efficiency with attribution accuracy. The learning-to-rank framework is appropriate for distilling ranking information from expensive teacher methods.\n- The evaluation is thorough, including both efficient rank prediction metrics and expensive counterfactual evaluation. The ablation studies investigate key design choices (feature spaces, loss functions, sampling strategies) with clear insights.\n- Finally the method achieves attribution performance comparable to influence-function and Unlearning-based methods while operating at millisecond latencies with minimal storage overhead.\n\nWeaknesses:\n- The approach is inherently limited by the teacher method's quality. Any biases, errors, or failure modes in AbU+ can be inherited, limiting the path to improve attribution beyond the teacher.\n- This is minor, nonetheless, from the perspective of training data creators, by learning only relative rankings rather than absolute attribution scores, the method loses potentially important information about influence magnitude and concentration. This could matter for applications like fair compensation where the degree of influence is critical. But that in itself would require massive scale and doesn't undermine the efficacy of this method or the results.\n- The initial k-NN retrieval using off-the-shelf features may miss influential images that are semantically related but visually dissimilar, creating an irrecoverable performance ceiling.\n\nQUESTIONS:\n- Have you explored modifications to preserve absolute attribution scores rather than just rankings? This could be achieved through regression with appropriate normalization or by learning a calibration function. This information could be valuable for understanding whether influence is concentrated in a few images or distributed across many.\n\n- Also wondering if ensembling multiple different teacher ()attribution) models may achieve better results?\n\nLIMITATIONS:\n- This method can only work if one has access to the pretrained T2I model\n- One additional limitation, is the computational cost of the data curation phase, which still requires running expensive attribution methods", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-02"}