When Model Knowledge meets Diffusion Model: Diffusion-assisted Data-free Image Synthesis with Alignment of Domain and Class
Authors: Yujin Kim
Hyunsoo Kim
Hyunwoo J. Kim
Suhyun Kim
Venue: ICML 2025 poster (2025)
Keywords: Data-Free Image Synthesis
Diffusion
Data-Free Knowlege Distillation
Primary area: applications->computer_vision
TL;DR: We propose DDIS, a data-free image synthesis method using diffusion models to align with training data distribution, especially the domain and class

Abstract: Open-source pre-trained models hold great potential for diverse applications, but their utility declines when their training data is unavailable. Data-Free Image Synthesis (DFIS) aims to generate images that approximate the learned data distribution of a pre-trained model without accessing the original data. However, existing DFIS methods produce samples that deviate from the training data distribution due to the lack of prior knowledge about natural images. To overcome this limitation, we propose DDIS, the first Diffusion-assisted Data-free Image Synthesis method that leverages a text-to-image diffusion model as a powerful image prior, improving synthetic image quality. DDIS extracts knowledge about the learned distribution from the given model and uses it to guide the diffusion model, enabling the generation of images that accurately align with the training data distribution. To achieve this, we introduce Domain Alignment Guidance (DAG) that aligns the synthetic data domain with the training data domain during the diffusion sampling process. Furthermore, we optimize a single Class Alignment Token (CAT) embedding to effectively capture class-specific attributes in the training dataset. Experiments on PACS and ImageNet demonstrate that DDIS outperforms prior DFIS methods by generating samples that better reflect the training data distribution, achieving SOTA performance in data-free applications.

When Model Knowledge meets Diffusion Model: Diffusion-assisted Data-free Image Synthesis with Alignment of Domain and Class
Yujin Kim 12
Hyunsoo Kim 12 Hyunwoo J. Kim
Suhyun Kim
Abstract
1. Introduction
The widespread availability of open-souree pre-trained mod- els have significantly advanced the field of deep learning (Ridnik et al., 2021; Singh et al., 2023; Goldblum et al., 2024). Recently, platforms such as Hugging Face (Wolf, 2019) have further enhanced accessibility of the pre-trained model, enabling researchers to easily apply these models to various tasks like knowledge distillation and model pruning (Tran al., 2024; Wang et al., 2024; Yin et al., 2020). How- ever, common requirement for utilizing these models in such applications is access to their training data, either in full or in part, for training student networks or fine-tuning pruned models. Unfortunately, these training datasets are often inaccessible due to various reasons, including data privacy and copyright issues. Consequently, the lack of access to training data presents a challenge in leveraging the potential of pre-trained models across various applications.
Open-source pre-trained models hold great po- tential for diverse applications, but their utility declines when their training data is unavailable. Data-Free Image Synthesis (DFIS) aims to gener- ate images that approximate the learned data dis- tribution of a pre-trained model without accessing the original data. However, existing DFIS meth- ods produce samples that deviate from the training data distribution due the lack of prior knowl- edge about natural images. To overcome this limitation, we propose DDIS, the first Diffusion- assisted Data-free Image Synthesis method that leverages a text-to-image diffusion model as a powerful image prior, improving synthetic im- age quality. DDIS extracts knowledge about the learned distribution from the given model and uses it to guide the diffusion model, enabling the generation of images that accurately align with the training data distribution. To achieve this, we introduce Domain Alignment Guidance (DAG) that aligns the synthetic data domain with the training data domain during the diffusion sam- pling process. Furthermore, we optimize single Class Alignment Token (CAT) embedding to ef- fectively capture class-specific attributes in the training dataset. Experiments on PACS and Ima- geNet demonstrate that DDIS outperforms prior DFIS methods by generating samples that better reflect the training data distribution, achieving SOTA performance in data-free applications.
To address the above issue, Data-Free Image Synthesis (DFIS) has been proposed as solution. DFIS aims to synthesize images that approximate the training data dis- tribution by extracting the model's Iss internal understanding of its training data. This enables the application of these models to tasks like data free knowledge distillation or prun- ing, enhancing their utility (Yin al., 2020; Mordvintsev et al., 2015; Kim et al., 2022; Ghiasi et al., 2022). How- ever, since existing DFIS methods generate images without prior knowledge of natural images, the image search space becomes huge. Consequently, they produce images with unnatural or artificial patterns that deviate from the training data distribution, ultimately limiting the model's utility.
In this paper, we introduce novel DFIS paradigm that leverages the inherent natural image understanding of an off the-shelf Text-to-Image Image (T2I) diffusion model. Recent breakthroughs in T2I diffusion models trained on large-scale open-world datasets (Rombach et al., 2022; Saharia et al., 2022) provide powerful image priors capable of significantly narrowing the search space on DFIS. However, naively re- placing the original training data with images generated by the T2I diffusion model is insufficient. Due to the in- accessibility of information regarding the training dataset, encompassing its domain, class-specific attributes, the input prompts of the T2I diffusion model are necessarily impre-
'Equal contribution KKorea University "Korea Institute of Sci- ence and Technology KKorea Advanced Institute of Science and Technology 'Kyung Hee University. Correspondence to: Suhyun Kim <dr.suhyun.king kim gmail.com>.
Proceedings of the ndnd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Title Suppressed Due to Excessive Size
cise (e.g.. A dog). This vagueness leads the generation of countless images that do not align with the training set distribution. Consequently, without guiding mechanism based on the pre- treinra model's 's knowledge of its training data distribution, the generated images are highly suscepti- ble to deviating from the underlying distribution, ultimately limiting the model's utility in various applications.
art results in data-free knowledge distillation and pruning, verifying the benefit of our synthesized data in enhancing the given model's utility, Our key contributions are:
We propose a DDIS, a novel Data-Free Image Synthesis (DFIS) method that pioneers the use of a Text-to-Image (T2I) diffusion model as a strong image prior to address the issue of existing DFIS methods producing images distant from the training set distribution due to an vast image search space.
Therefore, we propose a Diffitsion-assien asssisted Data-free Image Synthesis (DDIS), which guides the T2I diffusion model to generate images that are closely aligned with the training set distribution. Our approach tackles the misalignment prob- lem that arises when directly substituting the training set with images synthesized by a T2I diffusion model. Specif- ically, our method focuses on achieving alignment in two key aspects: 1) domain and 2) class-specific detail.
To ensure alignment at both the domain and class levels, we introduce Domain Alignment Guidance (DAG) and Class Alignment Token (CAT), DAG guides diffusion sampling to align the statistics of generated images with the internal statisties of the pre-trained model and CAT embedding encodes class-specific details.
Firstly, to capture the domain of the training data, we in- troduce a Domain Alignment Guidance (DAG) that guides the image latent during the diffusion sampling process. We are inspired by the understanding that Batch Normalization (BN) layers encode the domain knowledge of the training set (Wu et al., 2024; Wang et al., 2020). Specifically, the running statisties in BN layers, derived from the mean and variance of all training batches, capture the distribution of the entire training set, including its domain knowledge. Consequently, DAG guides the diffusion model in aligning the internal statistics of the synthesized samples with the running statistics within the given model. This alignment effectively induces and strengthens the domain-specific fea- tures of the training dataset the synthesized images.
Experimental results show that DDIS outperforms prior DFIS methods by generating samples closely aligned with the original training set, leading to stte-of-the-aa performance in various data-free applications.
2. Related Works
2.1. Data-Free Image Synthesis (DFIS)
DFIS aims to approximate the distribution p(z) learned by given classifier by solving an optimization problem in the input space without accessing the training data (Kim et al., 2022). DeepDream (Mordvintsev et al., 2015) visualizes the patterns learned by the model for specific classes by iter- atively optimizing noise to maximize the output probability for the desired class. DeepInversion (DI) (Yin et al., 2020) tackles the limited image prior in traditional DFIS methods and introduces a regularizer that ensures the statistics of syn- thetic images follow the running statistics within the model's s Batch Normalization (BN) layers (loffe & Szegedy, 2015). Plug-In- Inveeverioon (PII) (Ghiasi et al., 2022) addresses the issue of low diversity in generated images by combining various data augmentations during optimization. Despite these successes, finding an optimal & that closely approxi- mates p(x) within a vast search space remains a significant challenge without access to the training data. Consequently, the generated images are distant from the training samples.
Secondly, we propose to find new embedding vector corre- sponding to a pseudo-word, Class Alignment Token (CAT), that represents specific concepts of the target class. CAT is a token designed to capture the class details of training data that are not revealed on the class label name itself. For example, while a class label might be 'dog,' the specific dog breeds within the training set are not specified. We aim to find the embedding vector of CAT, ensuring that the synthetic images accurately align with the target class the model was trained on. Interestingly, CAT can not only capture detailed class features but also resolve the lexical ambiguity of class labels. We observe that CAT generates images precisely associated with the intended class when a class label is homonym or a compound noun.
Conclusively, proposed method, leveraging T2I dif- fusion model as strong image prior, synthesizes samples closely aligned with the training set distribution. DDIS enables replacing inaccessible original training data, ulti- mately enhancing the pre-trained model's utility. DDIS out- performs existing DFIS methods in capturing the training data distribution across various domains, such as art, car- toons, and manga, well as across the various 1000 classes within ImageNet- Ik. Moreover, DDIS achieves ofate-of-the-
2.2. Difffusion Models
Diffusion models (Ho et al., 2020; Sohl-Dickstein et al., 2015; Song et al., 2020) have garnered considerable atten- tion due to their exceptional sampling quality. Initiating the process with a random noise sample Xy ~ (O 7), the diffusion model iteratively predicts a denoised sample x, at each time step t. This is known as the reverse process:
x = Mt-1 + Oie, c~ N(OO
(1)
2

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Title Suppressed Due to Excessive Size
Domain Alignment Guidance (DAG)
Class Alignment Token (CAT)
Pronge: A photo of Sc Diger cat
mutipal
2=
Pprefix
Vestas
DAG
"photo"
-5c
bijer
DAG
t=0
CAT embedding
Outppt
optimisation Iteration
Sampling
All gummenttt
DAG Diffusion Sampling Process
Allgument:
Iterattonn
Goste
DAG: N
DAG
fi
Cauo
fo
5c
Vete
cat Tigee
lacklokk
Classifer fo
1-1
CAT
Bordhhoorm
Hate BatchNormaa
Seracion
Caustar
f6
Running Statisties ())
(1-11.1-11
Viena
55%
Tigercat Tiper
Figure 1. Overall framework of DDIS. The goal of DDIS is generate images approximating the training set distribution learned by using a Text-to-Image diffusion model. Firstly, we construct prompts y with Class Alignment Token (CAT) and the class label c provided with the model. (e.g.. y ="A/Aa An {Sa} {class label).) Secondly. Domain Alignment Guidance (DAG), aligning image features with the BN layer statistics within a model fā Lastly, we forward the final image from the guided image latent the fö and optimize the CAT embedding using Cross-Entropy loss encode features specific to the target class. (As in the figure above, we can successfully synthesize the "tiger cat" class in the "art" domain via DDIS.)
provide domain guidance noise latent at each time step via
where is noise scale at time t and Pt-1 is given by
Pr (y)x),, which is not feasible in DFIS due to the lack of access to labeled datasets.
VI VI- Vat
Pt-1 Vot
)xo(x)), (2)
Classifier-Free Guidance (CFG) (Ho Salimans, 2022) proposes method for achieving the effects classifier guidance without the need for an additional classifier by in- terpolating between unconditional and conditional outputs.
where ex(x) is the noise predicted by the U-Net (Ho et al., 2020).
co (x,, y) + (E,, co (xx))
(4)
Sampling Guidance for Diffusion Models. Classifier- assisted guidance methods contribute to the advancement of diffusion models by allowing the adjustment of outputs to generate desired images. From score function perspective (Song et al., 2020),
Unfortunately, despite this efficiency, CFG cannot provide guidance on the domain of the training set learned by the classifier, making it insufficient for standalone use in DFIS.
modify the unconditional score function Vx, log p(x)) to Vx, log pxx,ly) controlling im- age generation to produce results that align with a given condition such as a text prompt or class label.
3. Method
First, Section 3.1 introduces the preliminaries of image synthesis and diffusion models. Section 3.2 explains the concept of iiffusion-assistar Data-free Image Synthesis (DDIS). Then, Section 3.3 details Domain Alignment Guid- ance (DAG), which directs the image latent during diffusion sampling, and Class Alignment Token (CAT) in Section 3.4, which captures class details for accurate class alignment.
Classifier Guidance (CG) (Dhariwal Nichol, 2021) gener- ates class-conditional images by factorizing Vx, log p(xx Ly) the unconditional score Vx, log pxx;) and the gradient derived from the classifier V x. log logp(y) xx)) by Bayes' rule p(x)ly) p(y (xxx). The classifier guidance be formulated as the following with guidance scale > 0:
3.1. Preliminary
é e (x so, Xx, log p(y(x:)).
(3)
Data-Frea Image Synthesis. The goal of Data-Free Im- age Synthesis (DFIS) to find the optimal point R BxCC x within the sigh-dimensional input space that
CG requires generating training data yx, at each time step and training time-dependent external classifier
3

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Title Suppressed Due to Excessive Size
Algorithm 1 Domain Alignment Guidance Algorithm
subsequentyy obtain the denoised latent and finally gener- ate the clean image Xp by passing the 2o the decoder D : Rd + R Expressly, since the text conditioned LDMs focus on generating images guided by the given text prompt encoded by the text encoder To as a condition, and the predicted noise is Eg(Z;; To ty),
Require: Mode 1 (Pre-trained unconditional score estimator co(t.), text encoder Tæ, image decoder D. prompt y. classi- fier [(; pre-trained on unknown dataset
Output: Latent Zo
1: zr (0, 1)
2: for t T to do
3: < N((O I)
Guidance for Text-to- -mmae Diffusion Models. As men- tioned in Section 2.2, Classifier- Free Guidance (CFG), al- lows the class label or text to be used condition in the diffusion process can be represented in the latent space by modifying Equation 4 as follows:
4: z: 4 pt 04416441 {obtain jrr from Equation 2}
5: (BN(D(Z))) Σ (1)m)())) milla + (8+) 12)
[=1
6: āo s n. - Vv LaN (a())) (Apply DAG]
7: E cn ((1) + s (een(Z);
(Z:; (y)) () cs (Z); 23, ())
8: 6:-1 vaiii
2s + (1- ac)or)
/I Oc-1 o7c:
é en (2;; Ø, () + s(ea(Zi; T (y). eo (z:; 0,()), (6)
vor
deCe
9: end for
where 2 means a null condition (unconditioned) and 2₁ is latent variable in time step given by
10: Return 20
Zt + (1 - a,)c's
Algorithm 2 DDIS Algorithm
Zi-1 = vār-1
t u--1 offttore
Require: Pre-trained T2I diffusion model Model, classifier f(:00) pre-trained on unknown dataset
va
Parameter: Class Alignment token embedding Us
Output: Sampling images Xo
3.2. Diffusion-Assistee Data-free Image Synthesis
1: E, D, To, Eg +
2: for number of total classes C do
In this section, we introduce iiffusion-assister Data-free Image Synthesis (DDIS), which is pioneer of the use of a Text-to-lmage (T2I) diffusion model as a strong image prior to address the issuc of existing DFIS methods producing images distant from the training set distribution due a vast image search space. We utilize the Stable Diffusion (SD) (Rombach et al., 2022), which has achieved remarkable success in T2I diffusion models. By leveraging the com- prehensive knowledge of natural images embedded in this pre-trained T2I diffusion model, DDIS can produce samples that better approximate the learned distribution than current DFIS methods. However, since the images generated by T2I diffusion models still differ from the exact training set distribution, additional guidance is necessary to ensure they align properly with the training set.
3: Define the prompt y
4: (e.g y = "A/ An (S.) (cen class label)")
5: Add S. to vocabulary and get the ve from (S.)
6: for number of iterations do
7: Za DoonainligmmenCCCdaaaacoCooooo
8:
No t D(žo)
9:
te - LOE (f 0²), c) (Optimize the )
10: end for
11: Save the csh optimal Class Alignment token embedding o.
12:
end for
Return &o E (R) x3 ... >5*}
elicits a maximum response from the classifier for desired class p(y(ě) under data-free conditions. This process in- volves iteratively updating a random noise into a visually natural image by optimizing the following loss function:
3.3. Domain Alignment Guidance
min L(ū,v) v) + R()),
(5)
We propose effectively applicable guidance method for the diffusion model, called Domain Alignment Guidance (DAG), which provides guidance on the domain learned by the classifier during the diffusion sampling process. In- spired by studies suggesting that Batch Normalization (BN) layers encode domain knowledge (Lim et al., 2023; Mirza et al., 2022), Domain Alignment Guidance (DAG) guides the statisties synthesized images to align with the training set statisties stored in the model's BN layers. Specifically, we can obtain the channel-wise running mean l and run- ning variance o² of the entire training set from all BN layers in the given model f(::0). ). To guide the image latent so that the statistics of the generated images follow the run- ning statisties, we modify the unconditional score of the
where L(-) is Cross Cross-Entropy loss, and R(-)) is an image prior regularizer. Despite the potential of DFIS, finding an optimal point in a vast search space requires huge computa- tional costs under data-free settings. Although DeepDream (Mordvintsev et al., 2015) explores a total variation regu- larizer to generate visually plausible outputs, the resulting images lack the naturalness and fidelity of the real samples.
Text Conditioned Latent Diffusion Models. Text Con- ditioned Latent Diffusion models (LDM) (Rombach et al., 2022) operate in the latent space by projecting the image Xy to 2 = E(x)) through the auto-encoder & R + Rdd In the diffusion sampling process, starting from 2r, we
4

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image latent Vz. a. ((,) to Vz, pog |u, o²) from the perspective of SIDE (Song et al., 2020). Using Bayes' rule, the conditional score can be factorized as below two terms:
22 log.lzzll...l o²) = 2z, log p(zz)+sssV... log p(u, of 1z);
(8)
where s is a parameter controlling the guidance strength. We can interpret the second term as the gradient with respect to the latent obtained from an external loss function, which minimizes the difference between the running statisties and those of the synthetic image:
Otigina Cola
DL
SD 2.1
Das
(&() = ((Imm(()) /ull2 - (x,) -
-of)))olll)) ofll)), (9)
Figure 2. Qualitative comparison with various DFIS methods on the PACS (Art Painting) dataset. (a)-(c) correspond to the classes dog, guitar and horse, respectively.
1=1
where (Xee) and (xx) represent the mean and variance of the feature map at the I-th layer of the generated image x, In this process, we project the image latent to the pixel space at each time step t, by & = D(z)). to compute the gradient. We replace Vz log p( (Z))) with the obtained gradient V2,CBN (D:z)) w.r.t the latent variable, thereby providing guidance to the image latent in the direction of the training set distribution as below:
bN
2 = z: - LBN (D(z:)),
(10)
where y is the scaling factor for the gradient influence. Fi- nally, the DAG is integrated with the CFG to guide the latent toward the target distribution, enabling the T2I diffu- sion model to sample images faithful the prompt while incorporating domain knowledge within the classifier.
Originel data
DE
P
SD
Ouas
Figure 3. Qualitative comparison with various DFIS methods on the PACS (Cartoon) dataset. The classes match those Figure
E = 6n ( () + ((eo (y))))
(y): () eo (Rx; ()) (11)
encoder, which we expect to capture the semantic features of the desired class effectively, Inspired by (Gal et al., 2022; Huang et al., 2024; Kim al., 2025), which encodes specific concepts into pseudo-word token embeddings, we define S. as CAT, precisely represent the desired class Then, construct simple prompt y using the given class label as ofectorc y =AA/An {Sc} {class label)". We expand the vocabulary (12) The overall algorithm of DAG is described in Algorithm 1.
In conclusion, starting from noise ZT ~ (O, I). we esti- mate the noise & based on guided latent 2n via the DAG. This process iteratively updates the latent to a cleaner one, ultimately sampling images Xo that is aligned with the target domain by modifying Equation 7.
(*
Ze + (1 az)Ec
2 vār-1
+ ā,-1
Vol
by adding the CAT and only optimize its token embedding v, of the {S.} using Cross-Entropy (C.E.) loss. This iter- ative process aims to find the optimal ve that captures the class-specific information learned by the model, as follows:
3.4. Class Alignment Token
N LCE(f(x0:0)) = - log (a(f (13) j=1
To capture the accurate class's property, we encode the semantic information about class details into the word em- bedding of the proposed Class Aligmment Token (CAT), In T2I diffusion model, words or wordw from the input prompt are converted into tokens from predefined vocab- ulary, and a pre-trained text encoder Ts maps each token to a unique word embedding. We chose this embedding space for optimization in DDIS because this space encodes rich semantic representations learned by the pre-trained text
where f(it;; 6*) indicates output logits given the pre- trained model [(; (=:00) and &o- = D(zo). Specifically, we compute the C.E. loss only for the final image Xo obtained through the DAG-based diffusion sampling process up to the final time step, as this image closely resembles the distribution observed by the classifier. (i.e. p(x) 2 p(xo)). Thus,
5

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Title Suppressed Due to Excessive Size
d
Oligiial dasl
Orgonlde del
Clas
Figure 5. Qualitative comparison with prior methods on the ImageNet-11 1 dataset. We also include comparisons with StableDif- fusion 2.1 (SD2.1), which serves as the baseline model in our work. (a-d) denote classes "tiger cat", "beach wagon". "mail bag" and "kite", respectively. It is noteworthy that our method can address the issue of lexical overlapping.
Orginel
Figure 4. Qualitative comparison with baseline methods on the StyleAligned dataset. We conduct experiments on two domains, Manga (a.b) Caricature (c.d). (a-d) correspond to classes hero, villain, politician, and chef, respectively.
4.2. Main Results
Image Synthesis with Various Domain. DDIS is the first to succeed in generating samples from various domains, not just the photo domain, in the DFIS. Figures 2 to 4 compare images generated with the models pre-trained on diverse domain datasets with baselines. Since we do not know the knowledge of the dataset used for training the classifier, ex- isting DFIS methods must explore an extremely large image search space. This leads to generated images with artifacts that fail to accurately capture the training dataset's domain properties. As result, they fail to capture the domain prop- erties of the training dataset and usually generate images with severe artifact effects. Although Stable Diffusion can generate class-faithful images given prompts such as 'A dog', it lacks guidance on the domain of the training data, preventing it from capturing the dataset's domain-specific information. In contrast, the proposed DDIS leverages DAG in the diffusion sampling process and optimizes CAT em- beddings to synthesize images that accurately reflect the training dataset's domain and class attributes, producing images that closely resemble the original data.
even without access to the training set or additional training processes, we can capture the dlass-specffie attribute by optimizing only the CAT embedding. Furthermore, once the optimal CAT embedding is found, we can quickly generate images of the desired class through a sampling process. Consequently, DDIS can generate images that approximate the training set via DAG in the sampling process and CAT optimization in the T2I diffusion model, ensuring significant improvements in image quality compared to existing DFIS methods. The overall framework is described in Figure 1, and the algorithm can be found in Algorithm 2.
4. Experiments
4.1. Experimental Settings
Baselines. We mainly compare our method with existing data-free image synthesis (DFIS) methods such as Deep- Inversion (DI) (Yin et al., 2020) and PiugInInversion (PII) (Ghiasi al., 2022), which deal with the large-scale datasets synthesis inverting the CNNs. We do not compare the per- formance the Naturallnversion (NI) (Kim et al., 2022) since NI only considers small-scale datasets.
Image Synthesis Faithful the Target Class. We demonstrate how CAT embedding optimization enhances the capture of target class attributes. Figure 5 presents im- ages generated with a ResNet-34 pre-trained on ImageNet- lk. Surprisingly, DDIS achieves precise class mappings, even resolving the lexical overlap problem, where gener- ators misinterpret class labels. For instance, suppose the classifier has learned the tiger cat, as shown in Figure (a) (though we do not know the training set). However, Stable Diffusion (SD) incorrectly generates tiger due to word similarity. This issue becomes even more pronounced with compound nouns, such as 'beach wagon' or 'mailbag' where ambiguity leads to incorrect images. Even for single-word labels (Figure (d)). SD struggles with homonyms, reduc- ing accuracy. In contrast, DDIS overcomes these challenges
Datasets. We utilize ResNets (He et al., 2016) trained on five domains to generate images. For the photo domain, ImageNet- 1k (Russakovsky et al., 2015). For the art painting and cartoon domain, we use the PACS dataset (Li et al., 2017). Additionally, we collect artificial datasets via Style Aligned (Hertz et al., 2024) for manga and cari- cature domains. Each dataset includes 7 classes. Detailed experimental settings and class labels are the appendix.
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Table 1. Comparison of generated image quality with baselines. We evaluate how closely generated images approximate the training set distribution and further measure the fidelity and diversity of images using Precision and Recall metrics.
DDIS (Ours)
DeepInversion
PlugInImersion
Dataset
Domain
18(t>
FID(J)
Procision(")
Recalkt))
18(1)
FIDX.)
Precision(1)
Recall(t)
1S(1)
FID())
Procision(")
Recalk(1)
ImageNet-Ik
Photo
15.92
30.31
0,8028
0.7731
9.52
187.63
0.5038
0.0907
3.51
220.62
0.2966
0.0528
Art painting
4.12
133,37
0,7742
0,3213
4.00
188.53
0.4033
0.0004
2.53
208.73
0.3442
0.0014
PACS
Cartoon
4.04
85,41
0.7541
0.3104
148.94
0.3704
0.0038
275.86
0.0004
0.0001
Stylo-Aligned
Caricature
3.94
139.75
0.3552
0.5672
3.58
195.25
0.1027
0.0618
2.51
293.58
0.0001
0.0004
Manga
3,87
145.82
0.2036
0.4396
3.32
206.57
0.0834
0.0019
2.36
295.14
0.0001
0.0003
tion Score (IS) (Reed et al., 2016) and Frechet Inception Distance (FID) (Heusel et al., 2017), and (2) Precision and Recall (P&R) (Sajjadi et al., 2018), to measure the fidelity and diversity of synthetic images strictly. We evaluate the image quality of 10,000 synthetic ImageNet- Ik samples, 2,800 synthetic PACS samples, and 1,400 synthetic Style-
Data-Free Knowledge Distillation (DFKD) performance on PACS and ImageNet-Ik Ik with synthesized images from various DFIS methods. 'T.' and 'S." denote the teacher and student net- works, respectively. 'Original'' refers the baseline accuracy of
Table 2.
student network trained on the original training set.
T.
8.
Original
PU
SD
ResNet-34
RssNet-18
32,60
17.49
12.27
21.93
28.46
A Aligned samples. As shown in Table 1, DDIS outperforms the baselines across all metrics. Our proposed method gen-
ResNei-34
VGG-11
26.03
12.65
9.23
18.12
21,89
PACS-art
ResNet-S0
ResNet-50
ResNet-34
Ro
28.46
37.98
16.63
18:48
13:36
15.32
31.67
19.52
24.65
35.87
erates images that most closely approximate the training set distribution, effectively producing images that align with both the class and domain of the training set.
VGG-16
VGG.11
32.89
17.37
12.47
22.61
VGG-16
ShuffleNew2
54.25
30.32
23.14
41.74
48,56
ResNei-34
ResNet-18
51.35
28 65
17:23
38.17
47.066
34
VGG-11
49.95
26.92
15.62
38.94
44.92
PACS-cartoon
55
ResNet-18
58.15
31.48
20.32
44.67
51.45
ResNet-50
Re
17.34
24.05
15.66
36.73
42.93
4.3. Data-Free Applications
VGG-16
VGG-11
48.82
25. 25
16.04
38. 15
44.81
VGG-16
ShufBeNew2
51.24
29.91
19.32
43.63
DDIS aims enhance the utility of given model by gen- erating samples that approximate the distribution of the training data. Accordingly, we conduct experiments on Knowledge Distillation (KD) and Pruning using synthetic images without direct access to the training data. Specifi- cally, we synthesize 2,800 images from the PACS dataset and 100k images from ImageNet- Ik for these experiments.
BesNet:34
ResNet-18
13.30
4.67
2.01
33.02
41.68
ResNoi-34
VGG-11
34.91
2.67
1.34
27.41
ImageNer- 1k
50
ResNet188
43.09
6.31
1,98
35.43
40.77
ResNoi-50
ResNet-34
44.57
7.29
3,08
33.15
42.87
VGG-16
VGG.11
34.70
2.55
1.23
26.93
32.14
VGG-16
ShuffleNev22
28.13
1.37
1.02
18.52
24.61
Restieb-34 anininng
ImageNet-1k
Data-Free Knowledge Distillation. In this section, we en- sure that we can transfer information from a teacher network to a student network without original data and outperform existing data-free knowledge distillation approaches. The experimental setup for knowledge distillation is based on the protocol outlined in (Li et al., 2023). Our method is compared against previous DFIS approaches. including DI and PII, as well as the use of images generated by Stable Diffusion (SD).
Oure
VGG-16 PACS paiiting
VGG 16 PACS cartoon
VGG 16 ImageNet-1k
Truning Ratlo
Truning Rakto 154
Trasing Raza (S0
demonstrates the superior performance of the proposed approach. Our method consistently achieves results closer to the baseline across all datasets compared to prior studies. Notably, it better approximates the training set distribu- tion than directly using SD outputs as training data. This suggests that our method generates samples that are more aligned with the domain and class distributions of the origi- nal training data.
Figure 6. Finetuning the pruned R4 and VGG-16 with syn- thetic PACS and ImageNet-Ik Ik samples under different pruning ratios. We compare DDIS performance with prior DFIS methods.
by optimizing only the CAT embedding, ensuring that the generated images accurately align with the intended class.
Quantitative Results. We compare the image quality with existing DFIS studies to evaluate whether the generated im- ages approximate the distribution of the training set used to train the model. For this evaluation, we use (1) Incep-
Data-Free Free Pruning. We demonstrate that DDIS enhances pruned model accuracy without using real data. We apply -l-norm pruning (Liu et al., 2017) to ResNet-34 and VGG-
7

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Title Suppressed Due to Excessive Size
W 1.0
10.0
25.0
30.0
BAG
BAE CNTO
IMA 0
BAGOCATT
Crigina PAGS
Figure 7. Sample visualization of the re-weighting of the optimal Class Alignment Token embedding. We visualize synthetic sam- ples shown as the scaling parameter w of the CAT embedding vector progressively increases from 1.0 to 30.0.
Figure 8. Ablation results on PACS (Art painting) dataset. We assess the impact of the proposed DAG and CAT on synthetic images. Note that the case where neither DAG nor CAT is used equivalent to using the standard SD 2.1.
16 on PACS art painting, cartoon and ImageNet-1K 1k with pruning ratios from 50% to 90%. Following (Liu et al., 2018) settings, we locally prune the least important channels in each layer at the specified ratios. Similar to DFKD results, DDIS synthesizes samples closely matching the underlying distribution, enhancing model utility, as shown in Figure 6.
Table 3. Evaluation of image quality in PACS (Art Painting) under different elements in our method. We assess the impact of each component on the quality of generated images.
Method
DAG
CAT
IS(")
FID())
Precision(t)
Recall(1)
SD SD w/o DAG SD w/o CAT
2.88
193.57
0.6429
0,2572
<
3.29
174.31
0.6995
0,3074
3,95
166.22
0.6871
0,2843
4.4. Further Analysis
Ours
4.12
133.37
0,7742
0.3213
Ablation Study. To understand the impact of each com- ponent of DDIS on image synthesis, we evaluate the perfor- mance of various component combinations on the PACS art painting dataset, as described in Figure 8 and Table 3. When directly generating images from vanilla SD, the resulting images lack the information of the training set and fail to capture the unique characteristics of each class and domain, leading to significant deviations from the training set. The DAG leads to synthetic images that better reflect the tar- get domain by following the inner statistics of the training set encapsulated in the model. The optimal embedding of the CAT captures class-specific features, generating images with high fidelity to the class label, but if not used with DAG, domain discrepancy issues arise. When all proposed components are used together, the resulting images align with both the domain and class of the training set.
Drngiiad
Figure 9. Synthesizing the sketch domain is challenging under data-free conditions due to its abstract depiction.
5. Limitations
While DDIS excelled across domains, it struggled in the Sketch domain, which has abstract representations of ob- jects and scenes that make image generation particularly challenging, as shown in Figure 9. However, DDIS cap- tures common Sketch characteristics, such as monochrome backgrounds and darker tones. Additionally, since our DAG relies on feature statistics from the BN layer, it can only be applied to models with BN layers. In future work, we plan domain alignment guidance.
Impact of Optimal CAT Embedding on Synthetic Image. We evaluate if the optimal Class Alignment Token (CAT) embedding captures the desired class's high-level semantics and fine details. Figure 7 shows the influence of the CAT embedding vector by scaling the cross- cross-attention map with parameter w from 1.0 to 30.0. As w increases, the CAT embedding accentuates class class-specins specific features, such as the "eye shape" in the tiger cat images, showing its ability to cap- ture detailed class attributes. Furthermore, strengthening the CAT embedding improves alignment with the target domain, showcasing its capability to represent precise visual details and maintain consistency with the training set distribution.
to explore
6. Conclusion
We introduce DDIS, the first Diffusion-assisss assissed Data-free fata-free Image Synthesis method, using T2I diffusion model as powerful image prior narrow the image search space. We introduce Domain Alignment Guidance (DAG) for do- main alignment during diffusion sampling and Class Align- ment Token (CAT) embedding optimization for desired class alignment. We are also the first to conduct experiments across various domains in DFIS, proving its effectiveness.
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Title Suppressed Due to Excessive Size
Impact Statement
A., Hoffman, J., et al. Battle of the backbones: A large- scale comparison of pretrained models across computer vision tasks. Advances in Neural Information Processing Systems, 36, 2024.
This work aims to enhance the utility of pre-trained models by generating synthetic data in scenarios where access to the original data is unavailable. We acknowledge that data-free image synthesis approaches, designed to recover data fol- lowing the training dataset's ' distribution, can raise concerns regarding privacy leakage and other ethical issues. However, our objective is not to reconstruct individual instances or facilitate unauthorized data access but rather to synthesize a surrogate dataset that can be effectively utilized for data- free applications such as data-free knowledge distillation or data-free pruning. Specifically, data generation process by leveraging the running statistics from classifiers, which represent the averaged information of the entire dataset. This design inherently prevents the inclusion of individual instance details in the generated im- ages, making it extremely difficult to recover any specific sample (see Figures 2 to 5 in the main paper). We hope that this work contributes the responsible development of data-free techniques for scenarios in which data sharing is limited or infeasible.
He, K., Zhang, X., Ren, S., and Sun, J. Deep residual learn- ing for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770-778, 2016.
Hertz, A., Voynov, A., Fruchter, S., and Cohen-Or, D. Style aligned image generation via shared attention. In Proceed- ings of the IEEE/CVE Conference on Computer Vision and Pattern Recognition, pp. 4775-4785 2024.
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Heusel, M., Ramsauer, H.,, Unterthiner, T., Nessler, B., and Hochreiter, S. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30, 2017.
Ho, and Salimans, T. Classifier-free diffusion guidance. arXiv preprint (eXXxx:2277115590 2022.
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Acknowledgements
This research was partly supported by the Ministyy of Science and ICT), Korea, under the [TRC(Infommaion Technology Research Center) support program(IITP-202 RS-2023-00258649, 50%) supervised by the IITP(Instituee for Information & Communications Technology Planning & Evaluation), partly supported by the National Research Foundation of KoR))(NRF) grant funded by the Korea gov- emment(MMSTT TRS-2025-00562433 40%), and partly sup- ported by Institute of Information communications Tech- nology Planning& & Evaluation (ITP) grant funded by the N0.RS-2002-0015599 Artifi
Huang, Z., Wu, T., Jiang, Y., Chan, K. C., and Liu, Z. Re- version: Diffusion-based relation inversion from images, In SIGGRAPH Asia 2024 Conference Papers, pp. 1-11, 2024.
loffe, S. and Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pp. 448- 456. pmlr, 2015.
Korea (oVI) cial Intelligence Convergence Innovation Human Resources Development (Kyung Hee University), 10%).
Kim, H., Kim, D., and Kim, S. Difference inversion: Inter- polate and isolate the difference with token consistency for image analogy generation. In Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 18250-18259, 2025.
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A. Experiment Settings
A.1. Hyper parameters
We generate a 512x512 resolution image using Stable Diffusion 2.1 (Rombach et al., 2022) with = 30 diffusion time steps. For applying Domain Alignment Guidance (DAG), we define the gradient scaling factor 17 as the product of λBN and the guidance scale 8g. Here, ABN is the scaling factor for LBN. and 8g denotes the DAG guidance scale. These two parameters play a crucial role in our image generation process, and their impact on synthetic images is discussed in the following subsection. DAG is incorporated with the Classifier-Free Guidance (CFG) with scale of 15.
To define the Class Alignment Token (CAT), we newly define single token S. with no inherent meaning and add it to the vocabulary, using its embedding as the initial value. We utilize the token "newels" as the CAT, but any arbitrary token without meaning can serve as the initial token. To optimize the embedding vector de of the CAT, we employ the Adam optimizer with a learning rate of 0.005. We train for up 30 epochs, each involving 20 gradient accumulation steps and generating images from latent noise initialized with different seeds. It is equivalent batch size of 20 and can be adjusted based on resource availability.
A.2. Detail of r in DAG
The main hyperparameter of our proposed Domain Alignment Guidance (DAG) in the diffusion sampling process is r). which is divided into the gradient flow scale ABN and the DAG guidance scale Sg and we can rewrite the Equation 10 as below.
2 = 2, (ABN 22 CNN (D(z))))
(14)
To determine the optimal values for the gradient flow scale(ABx) and guidance scale(sg). we conduct experiments across a wide range of values. Specifically, we test ABN between 0.0001 and I, while the Sq is explored within the range of 0.1 to 100. As a result, we find the optimal settings as a ABN of 0.01 and a Sy of 20. The experimental results are shown in the Figure 11. Users can adjust these two parameters as needed, with the selection of hyperparameters guided by the generated images' confidence score.
A.3. Training Strategy
Prompt Design. Due to the nature of Data-Free Image Synthesis (DFIS), where we have no information about the training set beyond class label information, we design prompts y to be very simple and ambiguous (e.g.. y ="A/An {S.} {class label).") "" To demonstrate that we can effectively approximate the desired distribution with minimal information, we refrain from using any prefixes like "A photo of ~.."
Gradient Skipping for CAT embedding optimization. Gradient backpropagation through all diffusion steps / demands substantial memory. In our experiments, limiting gradient propagation to just the final denoising step (i.e., step 1 = 30 (-final 7')) reduces the resource usage. Additionally, since the given model was trained on natural images with a similar distribution of images from the final denoising step (i.e., p(x) p(fu)), the gradient skipping technique produces an appropriate loss for representing the intended class. Although deeper backpropagation could lead to further improvements, we do not explore this approach due to memory constraints.
Early Stopping Strategy. We use a threshold of 0.7 for the proportion of correctly predicted samples in a batch to determine early stopping. During each epoch, the generated batch is evaluated by the classifier. If over 70% of the samples are accurately predicted as the target class, we apply early stopping for the CAT embedding optimization.
A.4. Computation Overhead
In our experiments using single RTX 4090 GPU, the optimal CAT embedding is found in approximately 7.5 minutes. Once determined, this embedding remains fixed, enabling rapid image generation via diffusion sampling process. Compared to existing Data-Free Image Synthesis (DFIS) methods, our DDIS enhances efficiency and cost-effectiveenss for large-scale ImageNet-IK Ik dataset generation while preserving high image quality. To illustrate this, we compare the total optimization iterations needed to synthesize 100,000 ImageNet- 1k images on a single RTX 4090 GPU, shown in 4.
DeepInversion (DI) (Yin et al., 2020) requires iterative optimization for each mini-batch. With an optimal batch size of 250,

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Table Comparison of time cost and iteration count for generating 100,000 ImageNet-1k 1k images with prior DFIS methods (DeepInversion, PlugInlaversion), and our method.
Cost
DeepInversion
Pluginlnversion
DDIS (Ours)
Total Iteration for 100k samples synthesis
8,000K
1,120K
30K
Times per iteration (sec)
0.83
15.17 0.79
Total training cost (hours)
18444
245
126
around 20,000 iterations per batch are needed. Generating 100,000 images necessitates repeating this 400 times, totaling 8,000,000 iterations (20,000 iterations per a mini-batch x 400 batches). PluginInversion (PII) (Ghiasi al., 2022) optimizes random input across seven progressive upsampling stages (7x7 to 224 x 224), with 400 iterations per stage. Generating 100,000 images with a batch size of 250 results in approximately 1, 1,120,000 total iterations (400 iterations per stage x 7 stages x 400 batches).
Our DDIS optimizes only class-wise CAT embedding vectors in a low-dimensional space (1x784). For ImageNet- Ik (1000 classes), we perform just 30 iterations per class, totaling 30,000 iterations (30 iterations/class 1000 classes). After finding the CAT embedding, generating 100,000 images involves simply sampling latent vectors without further training or optimization. While CAT' optimization has slightly longer per-iteration time than prior methods, the drastically reduced total iterations make the overall process more efficient.
B. Sample Visualization
B.1. ImageNet-Ik 1k Sample Synthesis
Since ImageNet-Ik Ik (Russakovsky et al., 2015) consists of 1,000 classes, to evaluate whether our DDIS can avoid lexical overlapping issues and accurately align with the correct class, we generate images by inverting the ResNet-34 al., 2015) pretrained on ImageNet-I1 kk provided by Torch Vision (Paszke al., 2019), which has top-1 accuracy of 73.33% 11, We generate 10,000 images with 512x512 using the Stable Diffusion 2.1 and resize them to 224 x24x224 for evaluation, matching the typical resolution used during ImageNet-Ik training. For comparison, we generate 10,000 images using DeepInversion (DI) (Yin et al., 2020) and PluglnInversion (PII) (Ghiasi et al., 2022), the only Data-Free Image Synthesis (DFIS) studies addressing large-resolution image synthesis, following their official GitHub implementations and proposed their ImageNet-Ik kk parameters. Figure 12 illustrates samples for 30 classes, including those with lexical overlapping issues. The proposed method effectively generates images that precisely match the classes learned by the model through CAT embedding optimization. For example, the class "Yellow Lady's Slipper" (class 986), which refers to a type of flower, demonstrates how DDIS alleviates lexical overlapping problems to produce accurate class images.
B.2. PACS Sample Synthesis
We the first to address the synthesis of non-photo-ppeciffic domain datasets in DFIS. We focus on the PACS dataset synthesis (Li et al., 2017), benchmark commonly used in domain generalization, which consists of four domains: Photo, Art Painting, Cartoon, and Sketch. We specifically handle the Art Painting and Cartoon domains, as the Photo domain's 's excellence is well-described with the ImageNet- Ik experiment, and the Sketch domain represents failure case in our study, Each domain contains seven classes: dog, elephant, giraffe, guitar, horse, house, and person. We fine-tune the ResNet-34 pre-trained on ImageNet-Ik Ik with two domains, excluding the training domain data for inference on the remaining three domains to measure 1 accuracy. The top-1 accuracies for Art Painting and Cartoon pre-trained ResNet-34 models are 54.73% and 61.78%, respectively. We generate 512x 512 images using Stable Diffusion 2.1 and resize them to 224x224 for evaluation, aligning with the typical PACS training resolution. For comparison, we generate 3,000 images using DeepInversion (DI), Pluginlnversion (PII), and our DDIS method. We reproduce the DI and PII utilizing the official GitHub implementations and ImageNet-1k 1k parameters matching the PACS dataset resolution.
Figure 13 shows the results of synthesizing all classes in the PACS Art Painting domain, while Figure 14 displays the results for the PACS Cartoon domain. We demonstrate that our proposed DAG-basee diffusion sampling process and CAT embedding optimization align the target dataset's 's domain and class information more effectively than the baseline methods.
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B.3. Style-Aligned Sample Synthesis
To demonstrate the effectiveness of our methodology across a broader range of domains, we utilize Style-Aligned (Hertz et al., 2024) to create high-que nigh-quality, domain-specifie datasets. We select the domains of manga, caricature, and sketch and synthesized 400 images for each of the seven classes within these domains using Stable Diffusion 2.1. The synthesized datasets are illustrated in the Figure 15. Subsequently, we finetune a classifier using ResNet-33 pre-trained on ImageNet- 1k, following the same approach as with ImageNet- Ik and PACS experiments. The top-1 accuracies for manga, caricature, and sketch are 85.70%, 76.80% and 98.90%, respectively.
C. Additional Experiments
C.1. Efficiency of Domain Alignment Guidance
We demonstrate that in DAG, the gradient of LBN. which aligns synthetic image statistics with the BN layer running statistics, provides stable guidance unaffected by the time step. To avoid confusion, we compare our method with Classifier- Guidance (CG) (Dhariwal & Nichol, 2021), which only provides class guidance at the last time step T. First, running statistics within all BN layers are derived from the entire training set, making them more robust and stable than providing guidance based on conditional probability gradients for individual samples. Second, we demonstrate that the BN statistics of generated images remain stable across different time steps. Figure 18 illustrates the layer- wise mean and variance of images generated at every time step. We sample 30 images from the "hero" class in the Style Aligned Manga domain over time steps, passing them through ResNet-33 pre-trained on the Manga dataset observe the variation in layer-wise mean over time steps. The laser-wise statisties of noise samples at each time step surprisingly exhibit similar trends from time step 0 to T, remaining consistent across different time steps. This result indicates that the model's 's regardless of the time step, demonstrating that guidance aligning synthetic image statistics with BN layer running statistics at each time step is highly reliable.
internal statistics remain stable
C.2. Comparative Analysis of Class/Domain- Wise Token
To demonstrate the effectiveness of our Class Alignment Token, we compare our CAT with the domain- token. The comain-wise token is trained to observe the overall classes, while class-wise tokens are trained to observe each class independently. Figure 10 illustrates the results for the PACS-cartoon domain when using a single token embedding for all classes. We observe that the domain-wiss token embedding captures the domain knowledge of the training set but generates images with ignored class-specific features, averaging across all classes. In contrast, our CAT embedding encodes class-specife information, capturing precise features corresponding to each class.
C.3. Zero-shot Image Synthesis
We conduct zero-shot image synthesis experiment to evaluate whether Stable Diffusion can effectively generate images for unseen classes. First, using Stable Diffusion V3, select 10 classes that the Stable Diffusion 2.1 model used in our study has never encountered and generate images for those classes with SD V3. Then, we train a ResNet-50 model using the dataset of these 10 classes (see Figure 19). Finally, we utilize Stable Diffusion 2.1 to synthesize the images used for training ResNet-50 with the our proposed DDIS. Surprisingly, as shown in Figure 20, although Stable Diffusion 2. struggles with these classes, the CAT embedding can capture the class attributes from the training set and generate images with a distribution similar to that of the original dataset.
C.4. Further Analysis of CAT Embedding Optimization with Frozen SD Networks
To evaluate the effectiveness of our CAT embedding optimization, we conduct ablation studies on three design choices, synthesizing four lexically ambiguous ImageNet- IK classes: Kite (21), Tiger Cat (282), Beach Wagon (436), and Mail Bag (636) like in the main paper. We compare confidence scores and visual quality under these settings. Firstly, we test three fine-tuning configurations for Stable Diffusion (SD): (b) UNet only, (c) text encoder only, and (d) Full fine-tuning, using the Cross-Entropy (CE) loss used for CAT optimization. As shown in Figure 21 and Table 5, fine-tuned SD produces distorted, low-confidence images, failing to generate class-aligned images. It suggests that the SD fine-tuning in Data-Frea Image Synthesis (DFIS) disrupts the prior knowledge within the SD, degrading image quality. Generally, SD is fine-tuned using real data to adapt to specific domains or styles, but in DFIS, the lack of real images leads to unstable training (right side of Figure 21). Moreover, per-class SD tun-tt leads to individual SD networks, increasing computational cost. In contrast,
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Table 5. Design Choice 1: Partial Full Fine-tuning of SD)
Method
C-21
C-282
C-436
C-636
Avg. Confidence
(a) Ours
94.12
65,94
85.73
67.66
78.36
(b) SD UNet
0.77
0.67
2.47
1.53
1.36
(c) SD Text-Encoder
0.03
21.25
17.35
2.57
10.30
(d) SD Full
0.26
0.40
0.01
0.01
0.17
Table Design Choice Optimizing Multiple Tokens Embeddings Confidence Scores
Method
C-21
C-282
C-436
C-636
Avg. Confidence
Token (Ours)
94.12 12
65.94
85.73
67.66
78.36
Tokens
0.01
30.19
5.04
4.10
9.83
3 Tokens
0.01
30.15
5.08
3.19
9.60
4 Tokens
0.01
30.17
5.07
3.59
9.71
5 Tokens
0.01
29.14
1.32
3.54
8.50
Table 7. Design Choice 3: Adding BatchNorm loss CAT embedding optimization Confidence Scores
Method
C-21
C-282
C-436
C-636
Avg. Confidence
Ours
94.12
65.94
85.73
67.66
78.36
CAT optim. w/BN Loss
0.01
26.15
8.26
0.03
8.61
our method freezes SD and optimizes a single token, preserving SD's image prior while efficiently generating high-quality, class-aligned images.
Secondly, we explore whether multiple CAT embeddings improve class expressivity by optimizing embeddings for one to five tokens. As shown in Figure 22 and Table 6, performance drops with more tokens. Lexical ambiguity persists, and confidence scores drop. In data-free settings with simple prompts (e.g.. "A {class}") more tokens amplify the effect of randomly initialized embeddings, hindering desired class-aligned image generation. Therefore, single token is sufficient and more effective for encoding class information in DFIS.
Lastly, we test optimizing CAT embedding with BatchNorm (BN) loss alongside vanilla CE loss. As shown in Figure 23 and Table 7, BN loss negatively affects capturing class semantics by inducing synthetic images toward the averaged statistics of the entire dataset, which causes class mixing and reduces separability. Therefore, optimizing CAT embedding with CE loss alone effectively captures class-specins attributes.
In conclusion, the above design studies validate that desired concepts or complex information can be encoded by optimizing only token embeddings on frozen SD. Building on this, we adopt the idea to DFIS and demonstrate that single CAT token effectively captures class semantics while preserving SD's priors. Across all design choices, our approach consistently outperforms alternatives, highlighting its effectiveness for DFIS.
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ON AR' AR'ROIS'S
A ON ABPN
STONS
Elephant
Person
Horse
Figure 10. Visualization of PACS (cartoon) samples generated with domain wise tokens. While domain wise tokens capture domain information, they fail to distinguish unique class attributes, resulting in averaged class images.
Guidance scale
Gradient Flow scale
Ours (20)
Ours (0.01)
Figure II. Study Gradient Flow scale and Guidance scale
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Title Suppressed Due to Excessive Size
(b)
(c)
Figure 12. Visualization of ImageNet-1k Ik samples. We select 30 ImageNet- Ik classes with lexical overlapping issues and present visual results. Panel (a) displays our method, (b) shows PluglnInversion, and (c) depicts DeepInversion. (class index: 1, 2, 3, 6, 13, 18, 21, 31, 33, 37, 282, 436, 460, 461, 465, 466, 467, 489, 490, 492, 496 ,636, 726, 862, 982, 984, 986, 991, 996, and 999)
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Figure Visualization class samples PACS (Arl Painting) dataset. (a) through (g) sequentially represent classes: dog, elephant, giraffe, guitar, horse, house, and person. Our method demonstrates superior capture of target domain and class information compared to baselines (DI, PII) and highlights the domain discrepancy issues that arise when directly applying Stable Diffusion to DFIS.
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Figure Visualization class samples the PACS (Cartoon) dataset. (a) through (g) sequentially represent classes: dog, elephant, giraffe, guitar, horse, house, and person. Our method demonstrates superior capture of target domain and class information compared to baselines (DI, PII) and highlights the domain discrepancy issues that arise when directly applying Stable Diffusion to DFIS.
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Manga
hero
villain
samurai
monster
ninja
dragon
robot
Caricabure
UV
politician
celebrity
athlete
teacher
chef
scientist.
doctor
Sketch
dog
elephant
giraffe
guitar
horse
house
person
Figure 15. Synthetic datasets are generated using Style-Aligned (Original data). We treat these synthetic data as the training set and then
train ResNet-34 model. Finally, we invert ResNet-34 pre-trained on Style-Aligned dataset generate images.
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Layer-wise Mean Values over the diffusion time steps
399Mrt 4Ver #
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Figure 16. Layer-wise mean values
Layer-wise Variance Values over the diffusion time steps
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Figure 17. Layer-wise variance values
Figure Visualization laser-wise mean and variance over diffusion time steps (DDIM sampler, 30 steps).
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Title Suppressed Due to Excessive Size
"a of a degen octopus juguling cryplo tokens"
"a photo of a flex model with VR drip digital runway"
"a photo of flying submarine gliding
futuristic harbor"
4. "a photo of ghost kitchen drone delivering byte-snacks"
5. "a photo of a posthuman cyborg sipping matcha in a digital cafe"
6. "a photo of a robot hummingbird hovering over neon flowers"
7. "a photo of robotic tiger prow ling through a oyberpunk alley"
"a photo steampunk robot playing grand piano in a dimly room"
"a photo of astronaut riding glant manta ray in space"
photo a frosty pixel lion"
Figure 19. Prompt with unseen class for zero-shot image synthesis. The classes above are unseen by Stable Diffusion 2 (SD2) and were used as prompts to generate images with Stable Diffusion (SD3). For each prompt, we generate 400 images per class, and the resulting images are used as the training dataset for the classifier.
SD3
SD2
Ours (SD2)
Class
2
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Figure 20. Shro image synthesis results for unseen classes. We first find 10 classes described in 19 that Stable Diffusion 2 (SD2) struggles to generate, but Stable Diffusion (SD3) handles well. Using SD3, we generate 400 images per class and then train a ResNet-50 classifier with these images. Using this trained classifier, we apply the DDIS method to synthesize samples that approximate the training set distribution. While the vanilla SD2 (without DDIS) produces samples that deviate significantly from the training set distribution, our proposed method generate images that closely resemble the original data distribution, for classes that SD2 has
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Title Suppressed Due to Excessive Size
Kite
Beach Wagon Tiger Cat Cat
Mailbag
Kite
Beach Wagon
Tiger Cat
Mailbag
(a)
(b)
(c)
NILA A
(d)
Figure 21. Sample visualization for Design Choice #1 (Stable Diffusion (SD) Fine-tuning vs. CAT embedding optimization). Synthetic images for four lexical ambiguous ImageNet-II Ik classes using CAT embedding optimization with frozen SI) and (b-d) fine-tuning haselines: (b) UNet, (c) text encoder, (d) full SD. For each row, the images on the left of the arrow are generated at the minimum Cross- Eotso-y loss, and those on the right after epochs. SD muninn leads to degraded and unstable outputs over time.
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Title Suppressed Due to Excessive Size
Kite
Beach Wagon Tiger Cat
Mailbag
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(b)
(c)
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BANG
(e)
Figure 22. Sample visualization for Design Choice #2 (Number of CAT tokens). Synthetic images generated with varying numbers of optimized CAT embeddings SD: (a) single token (ours), (b-e) two five tokens. Image quality degrades the number tokens increases and lexical ambiguity persists, indicating that a single token is sufficient for capturing class- class-specific attributes.
Kite
Beach Wagon Tiger Cat
Mailbag
(a)
(b)
Figure 23. Sample visualization for Design Choice #3 (Effect of BatchNorm loss on CAT embedding optimization). (a) CAT embedding optimization with Cross-Entroph loss (ours). (b) CAT optimization with CB and BN loss. BN loss encourages synthetic samples match overall dataset statistics, often producing mixed-class images and reducing class separability.
23


Reviews:
[{"review_id": "iJH4sxBaES", "reviewer": "Reviewer_83BW", "summary": "This paper proposed a novel Diffusion-assisted Data-free Image Synthesis method designed to improve the quality of images generated without access to training data. Traditional methods struggle to approximate the original data distribution due to the absence of natural image priors. DDIS overcomes this by leveraging a text-to-image diffusion model as a strong prior, extracting knowledge from a pre-trained model to guide synthesis. The approach introduces Domain Alignment Guidance (DAG) to align synthetic images with the training data and a Class Alignment Token (CAT) for capturing class-specific attributes. Experiments on PACS and ImageNet confirm DDIS’s superiority over existing methods.\n\n##update after rebuttal\nThe authors have addressed most of my concerns satisfactorily, I am therefore updating my recommendation to a weak accept.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We would like to express our gratitude for your thoughtful comments and valuable feedback. Our response to the weakness is as follows.\n|       Method (C10)      |   IS   |   FID   | Precision | Recall  | KD Result (\\%) |\n|:------------------:|:------:|:-------:|:---------:|:-------:|:---------:|\n| [1] CMI            |  2.78  | 178.42  |  0.5048   | 0.0745  |   94.84   |\n| [2] SpaceShipNet   |  3.02  | 165.40  |  0.5117   | 0.0146  |   95.39   |\n| [3] NAYER          |  2.53  | 186.51  |  0.4847   | 0.0062  |   95.21   |\n| Ours               |  5.31  |  38.93  |  0.7280   | 0.5192  |   91.44   |\n\n|     Methods (C100)     |  IS   |  FID  |   P    |   R    | KD Result |\n|:---------------:|:-----:|:-----:|:------:|:------:|:---------:|\n| [1] CMI         | 3.94  | 167.29| 0.4686 | 0.0114 |   77.04   |\n| [2] ShipNet     | 4.52  | 158.73| 0.4893 | 0.0132 |   77.41   |\n| [3] NAYER       | 3.87  | 157.28| 0.4984 | 0.0105 |   77.54   |\n| DDIS (Ours)     | 7.43  | 28.66 | 0.7945 | 0.5355 |   72.57   |\n\n\n|      CIFAR-10      |  50%  |  60%  |  70%  |  80%  |  90%  |\n|:-----------------:|:-----:|:-----:|:-----:|:-----:|:-----:|\n| [1] CMI           | 76.15 | 70.41 | 61.73 | 52.56 | 48.94 |\n| [2] SpaceShipNet       | 78.61 | 72.26 | 62.41 | 53.70 | 51.81 |\n| [3] NAYER         | 77.66 | 74.31 | 60.51 | 55.72 | 52.78 |\n| DDIS (Ours)       | 78.82 | 74.51 | 64.72 | 59.69 | 57.81 |\n\n|     CIFAR-100    |  50%  |  60%  |  70%  |  80%  |  90%  |\n|:---------------:|:-----:|:-----:|:-----:|:-----:|:-----:|\n| [1] CMI         | 62.67 | 56.71 | 53.66 | 49.71 | 44.92 |\n| [2] ShipNet     | 64.68 | 58.74 | 57.93 | 53.93 | 50.71 |\n| [3] NAYER       | 67.82 | 62.56 | 59.52 | 55.79 | 52.69 |\n| DDIS (Ours)     | 69.93 | 66.18 | 63.72 | 59.14 | 55.31 |\n\n**W1)** Previous DFIS methods like DeepInversion lack strong image priors and rely on weak constraints (e.g., total variation), leading to unrealistic artifacts or samples far from the true data manifold (see Figs. 2–5). They also optimize in high-dimensional pixel space (e.g., B×3×256×256) from random noise, making the process unstable and inefficient. In contrast, our method is the first to leverage strong priors from text-to-image (T2I) diffusion models, guiding synthesis toward realistic images and constraining the search space. Moreover, we operate in a compact embedding space (e.g., 1×784), which simplifies optimization. These improvements allow our method to more accurately match the original training distribution and address key limitations of prior work.\n\n**W2,W6)**(Please see this link: https://drive.google.com/file/d/1S2Qt0XSgMH2pOzdzsNekhlyP-bLBY5kt/view?usp=sharing)\nWe compare DDIS with methods [1–3] on synthetic data quality and KD performance using CIFAR-10/100, as [1–3] don’t support large-scale, multi-domain synthesis. All methods use ResNet-34 to generate 50K images per dataset. We evaluate IS, FID, precision, and recall (see Fig. 1–2), and Table 1 shows DDIS outperforms all baselines with more realistic images.\nFor data-free knowledge distillation (DFKD), we use ResNet-34 as teacher and ResNet-18 as student. While student accuracy is slightly lower than KD-focused methods using special losses, our goal is to match real data distribution, not optimize for KD.\n\nTo show generality, we apply DDIS to data-free pruning. Table 2 reports top-1 accuracy of pruned ResNet-34 (ratios 0.5–0.9) fine-tuned on our synthetic data. DDIS consistently achieves the best results, proving its broad applicability.\n\n**W3)** NaturalInversion (NI) focuses on small-scale datasets like CIFAR-10/100 and faces scalability issues due to quality and compute limits. It shifts optimization to parameter space using both a main and sub-generator (FTP), increasing trainable parameters. Scaling to larger datasets requires bigger generators, expanding the search space and computational cost, making large-scale synthesis infeasible. For fair comparison, we apply our method to CIFAR-10/100 with pretrained ResNet-34, generating 50k samples. As shown in Table 1, our method outperforms NI in IS, FID, precision, and recall, better aligning with the original data distribution. These results demonstrate our method’s scalability and effectiveness, and we will include them in the revised paper to strengthen our evaluation.\n\n**W4)** Algorithm 1 outlines Domain Alignment Guidance (DAG) used during diffusion sampling. The noisy latent $z_t$​ is decoded by $D$ into $x_t$​, which is input to classifier $f$ to compute BN loss. The gradient of this loss guides $z_t$​, yielding z_t^{\\tilde} (line 6). This guided latent is then used for classifier-free guidance (line 7) and for predicting the next-step latent (line 8), following standard diffusion sampling. This process repeats until z_0^{\\tilde} is obtained. The full pipeline with DAG is detailed in Algorithm 2.\n\n**W5)** As detailed in **W1)** to Reviewer #e4Ls and Appendix A.3–A.4.", "claims_and_evidence": "1. The related work section states that finding an optimal sample that closely approximates the training distribution within a vast search space remains a significant challenge. However, it should explicitly clarify the specific limitations of prior work in addressing this issue and how this paper overcomes them. Identifying gaps in existing methods and demonstrating the proposed approach’s advantages would strengthen the argument.\n\n2. This paper claims to be the first to successfully generate samples across various domains beyond photo datasets. However, this claim lacks a direct comparison with data-free knowledge distillation methods, some of which also focus on generating training samples. Without such a comparison, the claim remains unsubstantiated.", "methods_and_evaluation_criteria": "1. The methods should be compared with recent data-free knowledge distillation approaches, as listed in the \"Essential References Not Discussed\" section, to ensure a comprehensive evaluation.\n\n2. The paper states that it does not compare performance with NaturalInversion because NI focuses on small-scale datasets. However, the rationale for this exclusion is unclear.\n\n3. The proposed method leverages batch normalization (BN) statistics, which limits the proposed method's generalization to other model architectures.", "theoretical_claims": "This paper does not present any formal proofs or theoretical claims.", "experimental_designs_or_analyses": "1. The logical correctness of Algorithm 1, particularly within the for loop, appears questionable. The usage of the final guided latent after the DAG process in the subsequent iteration is unclear.\n\n2. Considering the high computational cost of diffusion models, how does DDIS compare in efficiency with other DFIS methods? Additionally, the optimization of the class alignment token seems time-consuming, yet its implementation details are not addressed.", "relation_to_broader_scientific_literature": "The design of domain adaptive guidance might be related to the broader scientific literature.", "essential_references_not_discussed": "This paper lacks discussion on existing data-free knowledge distillation methods in the related work section. Given that the primary objective of data-free image synthesis in this study is to facilitate downstream tasks such as knowledge distillation, it is crucial to position the proposed approach within the broader context of prior research.\n\n[1] Gongfan Fang, Jie Song, Xinchao Wang, Chengchao Shen, Xingen Wang,\nand Mingli Song. Contrastive model inversion for data-free knowledge dis-\ntillation. arXiv preprint arXiv:2105.08584, 2021.\n\n[2] Shikang Yu, Jiachen Chen, Hu Han, and Shuqiang Jiang. Data-free knowl-\nedge distillation via feature exchange and activation region constraint. In\nProceedings of the IEEE/CVF Conference on Computer Vision and Pattern\nRecognition, pages 24266–24275, 2023.\n\n[3] Minh-Tuan Tran, Trung Le, Xuan-May Le, Mehrtash Harandi, Quan Hung\nTran, and Dinh Phung. Nayer: Noisy layer data generation for efficient and\neffective data-free knowledge distillation. In Proceedings of the IEEE/CVF\nConference on Computer Vision and Pattern Recognition, pages 23860–\n23869, 2024.", "other_strengths_and_weaknesses": "The strengths and weaknesses have been presented above.", "other_comments_or_suggestions": "1. In the phrase \"Expressively, since the Text conditioned\", the word \"Text\" should be in lowercase (\"text\") to maintain consistency in terminology unless it specifically refers to a defined term or concept within the paper.\n\n2. Comma Placement within Formula (Line 247, Left Column, Page 5): It would be more appropriately included within the notation of formula (10).\n\n\n3. The phrase \"=0\" appearing at the last line of both algorithms seems redundant.\n\n4. The sentence \"We evaluate image quality of synthetic 10k ImageNet-1k, 2.8k PACS, and 1.4k Style-Aligned samples.\" (Line 345, Right Column, Page 7) requires verification. Specifically, the phrasing \"synthetic 10k ImageNet-1k\" may lack clarity. \n\n5. The abbreviation \"DE methods\" in the caption of Figure 6 and Table 2 should be explicitly defined to enhance clarity and avoid ambiguity. If it refers to a known methodology, consider providing a brief explanation or reference upon its first occurrence.\n\n6. There appears to be a repetition in these lines (Lines 424-426, Right Column, Page 8). A careful review is needed.", "questions_for_authors": "1. The related work section states that finding an optimal sample approximating the training distribution within a vast search space is a significant challenge. However, the paper does not clearly specify the limitations of prior methods in addressing this issue. Could the authors explicitly outline these limitations and how the approach overcomes them? A clear comparison would help clarify the novelty and effectiveness of your method.\n\n2. The paper claims to be the first to generate samples across various domains beyond photo datasets. However, this claim lacks a direct comparison with existing DFKD methods, some of which also generate training samples. How does the proposed method compare to recent DFKD approaches, such as those listed in the “Essential References Not Discussed” section? A comparative analysis would help substantiate the uniqueness of the contributions.\n\n3. The paper states that it does not compare performance with NI because NI focuses on small-scale datasets. Could you clarify why this exclusion is justified? Would NI not provide at least some insights into small-scale performance, or could an adapted comparison be made? Including such a discussion would strengthen the completeness of the evaluation.\n\n\n4. In Algorithm 1, the logical correctness within the for loop is unclear, particularly regarding the use of the final guided latent after the DAG process in the subsequent iteration. \n\n5. Given the high computational cost of diffusion models, how does DDIS compare in efficiency with other Data-Free Image Synthesis (DFIS) methods? Additionally, the optimization of the class alignment token seems time-consuming, yet implementation details are not provided. It would be better to elaborate on its efficiency and potential optimizations? \n\n6. The related work section lacks discussion about existing DFKD methods.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "0Ch1riu5sX", "reviewer": "Reviewer_e4Ls", "summary": "This paper focuses on DataFree Image Synthesis and claims that the existing methods usually produce samples that deviate significantly from the training data distribution. To address this problem, the authors aim to leverage a text-to-image diffusion model to extract knowledge about the learned distribution. To achieve this, the authors propose Domain Alignment Guidance and Class Alignment Token module. The experiments show that the proposed method can achieve state-of-the-art performance in data-free applications.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We would like to express our gratitude for your thoughtful comments and valuable feedback. We understand your concerns, and hope the following response addresses them.\n\n| Method                                     | DeepInversion | PlugInInversion | Ours  |\n|:------------------------------------------:|:-------------:|:---------------:|:-----:|\n| Total Iteration for 100k samples synthesis (ImageNet-1k) | 80,000K       | 1,120K          | 30K   |\n| Times per 1 iteration (sec)                | 0.83          | 0.79            | 15.17 |\n| Total training cost (hours)                | 18444         | 245             | 126   |\n\n**W1)** Compared to existing DFIS methods, our DDIS offers significant improvements in efficiency and cost-effectiveness for large-scale dataset generation on ImageNet-1k while maintaining high image quality. To illustrate, we compare the number of optimization iterations required to synthesize 100,000 ImageNet-1k images. All experiments were conducted using a single RTX 4090 GPU:\n\n* **DeepInversion (DI)**: DI requires iterative optimization for each batch of images. With an optimal batch size of 250 on a single RTX 3090, approximately 20,000 iterations are needed per batch. To generate 100,000 images, DI must repeat this process 400 times, resulting in a total of 800,000K iterations.\n\n* **PlugInInversion (PII)**: PII similarly optimizes a randomly initialized input but across 7 progressive upsampling stages (from 7×7 to 224×224), with 400 iterations per stage. Thus, generating 100,000 images with a batch size of 250 results in around 1,120K total iterations.\n\n* **DDIS (Ours)**: DDIS optimizes only class-wise CAT embedding vectors in a relatively low-dimensional space (1×784). For ImageNet-1k with 1000 classes, we perform just 30 iterations per class, totaling only 3,000 iterations. Once a CAT embedding is found, we can synthesize 100,000 images simply by sampling latent vectors—without additional training or optimization. CAT optimization takes slightly longer per iteration than prior works, but the total required iterations are significantly lower, making the overall process more efficient.\n\n**W2)** As you said, comparing data distributions is inherently challenging. Generally, in the image synthesis tasks, Is, FID, Precision, and Recall are widely used to quantify fidelity and diversity, respectively, providing a comprehensive view of how closely the synthetic data approximates the real training distribution. The metric results presented in Table 1 reflect this distributional gap between the real and synthetic data. Our proposed method, DDIS, consistently outperforms prior approaches across all metrics (IS, FID, precision, and recall), indicating strong alignment with the true data distribution. This is further supported by the results in Table 2 and Figure 6, where using our synthetic data in place of the original training set yields the best performance in applications such as DFKD and pruning. We would also like to say that Reviewer #UzMa, in Claims and Evidence #1, acknowledged these results as strong evidence supporting the effectiveness of our approach.\n\n**W3)** In our view, the proposed method, DAG, is conceptually more aligned with Classifier Guidance than with Classifier-Free Guidance. DAG guides the diffusion sampling process by leveraging the running statistics stored in a classifier, which closely resembles the mechanism of Classifier Guidance. In traditional Classifier Guidance, It requires $t$ additional classifiers trained on noisy latents $z_t$ at different time step t, making it incompatible with a standard pretrained classifier. In contrast, DAG makes use of batch normalization statistics instead of classifier outputs, allowing it to work with general classifiers that are trained on $z_0​. Furthermore, A key distinction lies in the role each method plays: DAG provides guidance based on the training set domain, encouraging the generated samples to follow the underlying distribution of the original data. On the other hand, CG is typically used to guide an unconditioned diffusion model to generate images belonging to a specific target class. \n\n Classifier-Free Guidance (CFG), by contrast, does not rely on a separate classifier and instead offers text-conditioned guidance to ensure prompt fidelity. Importantly, DAG and CFG can be used together, as their mechanisms are orthogonal and complementary.\n \nIn summary, DAG shares the classifier-based foundation of CG but distinguishes itself by using the gradient of the BN loss and focusing on aligning samples with the overall training distribution, rather than generating class-specific outputs.", "claims_and_evidence": "The claim, i.e., all the existing methods usually produce samples that deviate significantly from the training data distribution, needs evidences.", "methods_and_evaluation_criteria": "Yes", "theoretical_claims": "No Theoretical Claims", "experimental_designs_or_analyses": "I check the validity of any experimental designs.", "relation_to_broader_scientific_literature": "The key contribution of leveraging a text-to-image diffusion model as a powerful image prior can related to the broader scientific literature that diffusion models can serve as plug-and-play priors.", "essential_references_not_discussed": "N/A", "other_strengths_and_weaknesses": "Strengths\n1. The idea of leveraging a text-to-image diffusion model as a powerful image prior is reasonable.\n2. State-of-the-art performance in data-free applications is achieved.\n\nWeaknesses\n1. The Computation Overhead is still heavy.\n2. The claim, i.e., all the existing methods usually produce samples that deviate significantly from the training data distribution, needs evidence. \n3. The batch normalization (BN) has been proved that it can reflect domain-related knowledge. The DAG starts from this and leverages CFG  to achieve Domain Alignment Guidance. The key differences over the existing methods are still not clear.", "other_comments_or_suggestions": "N/A", "questions_for_authors": "Please see the weakness.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {"ethical_review_concerns": "N/A"}}, {"review_id": "DRhjGik61Y", "reviewer": "Reviewer_UzMa", "summary": "- This paper proposes DDIS, a data-free image synthesis method that leverages a pre‐trained text-to‐image diffusion model as an image prior.\n- It introduces two key components: Domain Alignment Guidance (DAG), which uses the classifier’s batch normalization statistics to steer the diffusion sampling toward the original data domain, and a Class Alignment Token (CAT) that learns to capture class‐specific features, thereby resolving lexical ambiguities.\n- The method is shown to generate images that more closely reflect the unknown training data distribution and to improve downstream tasks such as data‐free knowledge distillation.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for providing meaningful feedback. We appreciate your thoughtful consideration. We have\n tried our best to provide responses to address your comments.\n\n**Claims and Evidence**\n\nPlease refer to our response **W1)** to Reviewer #83BW for a detailed explanation. As a result of supporting the above limitation of previous works, prior methods fail to approximate the original data distribution, resulting in low-quality synthesized images shown in Figure 2-5 in the main paper. This mismatch also negatively impacts performance in downstream applications, shown in Table 2 and Figure 6 in the main paper, further highlighting the practical limitations of existing approaches.\n\n**Methods and Evaluation Criteria**\n\nThank you for providing valuable discussion points regarding related works. We have carefully reviewed all the papers you mentioned and have identified the following key differences between our work and theirs.\n\n|                | Textual Inversion                                                | Null Text Inversion                                                              | Ours                                                    |\n|----------------|------------------------------------------------------------------|----------------------------------------------------------------------------------|---------------------------------------------------------|\n| **Task**           | personalized image generation                                    | image editing                                                                    | data-free image synthesis                               |\n| **Input**          | few (3-5) source images                                            | single source image                                                              | pre-trained classifier                                  |\n| **Output**         | personalized image                                               | edited image                                                                     | image that follows classifier's training data distribution |\n\n*1)Null-Text Inversion*: We would like to first clarify the differences between Null-Text-Inversion(NTI) and the problem setting of our research(DAG). NTI is primarily focused on effective image editing by performing DDIM inversion to obtain z_T​ from a given input image. In contrast, our goal is to synthesize data that approximates the distribution of the training dataset learned by a classifier—without requiring any input image. We start from random noise and leverage the classifier’s knowledge(BatchNorm statistics) as guidance. In other words, while NTI requires a source image as input and produces an edited version of that image as output, our method only requires an arbitrary classifier. To ensure clarity, we will explicitly detail these differences in the revised version of the paper.\n\n*2)Textual Inversion (TI)*: TI and DDIS both optimize a pseudo-word token embedding, but with different goals. TI personalizes text-to-image models by learning an embedding from a few reference images to capture a visual concept, guiding image generation accordingly. In contrast, DDIS generates data approximating a classifier’s training distribution without reference images, optimizing the embedding via cross-entropy loss between classifier predictions and target labels to steer generation toward the target class. While TI enables controlled generation, our method leverages a fixed diffusion model as a strong prior to constrain the image search space in data-free synthesis, yielding realistic, class-aligned samples.\n\n\nWe also appreciate your feedback regarding the ethical review. We will provide the following discussion and incorporate it into the revised version of the paper.\n\n**Ethical Review**\n\nThis work aims to enhance the utility of pre-trained models by generating synthetic data in scenarios where access to the original data is unavailable. We acknowledge that data-free image synthesis approaches, designed to recover data following the training dataset’s distribution, can raise concerns regarding privacy leakage and other ethical issues. However, our objective is not to reconstruct individual instances or facilitate unauthorized data access but rather to synthesize a surrogate dataset that can be effectively utilized for data-free applications such as data-free knowledge distillation or data-free pruning. Specifically, our method guides the data generation process by leveraging the running statistics from classifiers, which represent the averaged information of the entire dataset. This design inherently prevents the inclusion of individual instance details in the generated images, making it extremely difficult to recover any specific sample (see Figs. 2–5 in the main paper). We hope that this work contributes to the responsible development of data-free techniques for scenarios in which data sharing is limited or infeasible.", "claims_and_evidence": "- The authors claim that integrating diffusion guidance with DAG and CAT significantly improves the approximation of training set distribution, as evidenced by improved FID, precision/recall, and better performance in data‐free distillation.\n- Quantitative results on datasets like PACS and ImageNet support these claims, although the discussion on the limitations of prior DFIS methods could be more detailed.\n- Overall, the evidence supports the claims.", "methods_and_evaluation_criteria": "- Conceptually, the Domain Alignment Guidance (DAG) proposed in this work is extending the famous Null-text Inversion [1] into a full distribution setting, through batchify optimisation equipted with batch normalization and runnign statistics. It was ok to convert the existing method to a different problem setting; however, I was surprised to see that such a closely related work was completely missing through out the paper.\n- The relationship of the 𝐂𝐥𝐚𝐬𝐬 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭 𝐓𝐨𝐤𝐞𝐧 (𝐂𝐀𝐓) to Textual Inversion [2] should also be discussed.", "theoretical_claims": "There is no major issue with the theoretical claims, the finding that hacking BN statistics of pre-trained models and combined with image generation can reproduce training distribution is interesting.", "experimental_designs_or_analyses": "- The experiments are comprehensive, evaluating performance on multiple datasets and downstream tasks.\n- l.365-367 ‘The experimental setup for knowledge distillation is based on the protocol outlined in (Li et al., 2023).’, please brifely explain the experiment setup rather than redirecting the reader to the reference work.", "relation_to_broader_scientific_literature": "See below the ‘Essential References Not Discussed’ section", "essential_references_not_discussed": "[1] Mokady, R., Hertz, A., Aberman, K., Pritch, Y. and Cohen-Or, D., 2023. Null-text inversion for editing real images using guided diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 6038-6047).\n\n[2] Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A.H., Chechik, G. and Cohen-Or, D., 2022. An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618.", "other_strengths_and_weaknesses": "- Strengths: The integration of a diffusion model with BN-based domain guidance and class-specific tokens is innovative and produces strong empirical improvements in synthetic images that approximate unseen training data distribution.\n- Weaknesses: There is no major technique issue with the submission, but I hold my recommendation until an Ethical Review suggested below.", "other_comments_or_suggestions": "n/a", "questions_for_authors": "See above.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {"ethical_review_flag": "Flag this paper for an ethics review.", "ethics_expertise_needed": ["Privacy and Security"]}}, {"review_id": "ftetG4pCaS", "reviewer": "Reviewer_cfNr", "summary": "Working on the limitations regarding the inefficiency of open-source pre-trained models, which generate samples that significantly deviate from the training data, the authors proposed Diffusion-Assisted Data-Free Image Synthesis (DDIS) in the hope of improving synthetic image quality. The authors first extracted learned distribution knowledge from the given model and used it to guide the diffusion model for image generation. During the sampling process, they introduced domain alignment guidance to align the synthetic data domain with the training data distribution. They experimented with the PACS and ImageNet datasets, validating that the generated samples achieved a better alignment with the training dataset distribution.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your detailed comments. We sincerely appreciate your insights and hope to address your concerns.\n\n**W1)** A well-trained generator captures rich knowledge about the training data, enabling it to produce desired images through appropriate combinations of learned features. As a result, many recent studies have explored aligning models to specific tasks using only prompt tuning. The better the pre-trained model, the more effectively it can generalize to unseen combinations within the embedding space. Therefore, optimization within the embedding space alone is often sufficient.\nIn our setting, we leverage the strong image prior provided by Stable Diffusion. This allows even a simple prompt such as “a photo of [class]” to effectively generate class-representative images (see “SD 2.1” in Fig. 2–5).  However, the reason we utilize the CAT token is that the generated class is not perfectly aligned with the classifier’s learned class distribution (see Fig. 5, third column). For example, in ambiguous cases such as “kite,” which can refer to both a raptor and a toy, or for classes that require fine-grained details like “tiger cat,” the use of a Class token alone is sufficient to achieve closer alignment with the original dataset’s learned class distribution (see Fig. 5, fourth column).\n\n**W2-1) Validation of DAG**: To align BN statistics, we design a guidance mechanism within the diffusion sampling process. To address this concern of our DAG design, we conduct experiments using alternative design choices for BN statistics alignment. Specifically, instead of applying guidance during the sampling process, we optimize the CAT embedding directly using Equation (9) with the Cross-Entropy loss as the objective function. The results shown in Figure 2 of the attached link demonstrate the outcome when CAT token optimization is performed without DAG, where both cross-entropy loss and BN loss are optimized during training. In this case, although each PACS-art sample captures certain domain characteristics, the influence toward the target class becomes weaker. As a result, the generated images tend to lose critical target class attributes, leading to degraded class-specific representations. Furthermore, BN loss encourages the synthetic images to follow the averaged running statistics over the entire training set, which can lead to the generation of images that contain mixed information from multiple classes.\n  In contrast, our proposed DAG performs BN statistic alignment only during the diffusion sampling process, while the CAT embedding is optimized solely using cross-entropy loss. This separation allows the CAT embedding to focus on capturing class semantics while the sampling process handles domain characteristics. As a result, our method effectively approximates both the class and domain properties of the original training data. \n\n**W2-2) Zero-shot image synthesis** We conduct a zero-shot image synthesis experiment to evaluate whether Stable Diffusion can effectively generate images for unseen classes. First, using Stable Diffusion V3, we select 10 classes that the Stable Diffusion 2.1 model used in our study has never encountered and generate images for those classes with SD V3. Then, we train a ResNet-50 model using the dataset of these 10 classes. Finally, we utilize Stable Diffusion 2.1 to synthesize the images used for training ResNet-50 with the our proposed DDIS. Surprisingly, as shown in Figure 4 in the link, although Stable Diffusion 2.1 struggles with these classes, the CAT embedding can capture the class attributes from the training set and generate images with a distribution similar to that of the original dataset. (Please see this link: https://drive.google.com/file/d/1S2Qt0XSgMH2pOzdzsNekhlyP-bLBY5kt/view?usp=sharing)\n\n**W3)** Please refer to our response to Reviewer #e4Ls’s **W1)** for a detailed explanation. \n\n| Method | DI | PlI | SD|Ours  |\n|:------------:|:-------:|:-----------:|:-----:|:-----:|\n| ImageNet-1k | 32.11\\%       | 27.37\\%        | 64.31\\%  |76.61\\%|\n| PACS-art | 30.24\\%       | 22.85\\%        | 47.51\\%  |54.29\\%|\n| PACS-cartoon | 34.56\\%       | 22.15\\%        | 49.79\\%  |67.37\\%|\n\n**W4)** To assess whether our generated images are distinguishable by standard classifiers, we conduct an additional experiment where we pass 100,000 synthetic images through a ResNet-34 pretrained on ImageNet-1k and report the average top-1 classification confidence for each method. As shown in the above Table, our method achieves the highest average confidence across 1000 classes, indicating that our synthetic samples are more consistent with the model's learned distribution. These results further support the effectiveness of CAT embedding in generating class-consistent and classifier-recognizable images. We will include this analysis in the future to reinforce the practical impact of our method.", "claims_and_evidence": "Yes, the claims made in the submission are supported by clear and somewhat convincing evidence.", "methods_and_evaluation_criteria": "Yes, the proposed methods and evaluation criteria are well-suited to the problem, with relevant benchmark datasets and effective metrics for assessing the network's performance, excluding CLIP-based metrics and classification accuracies.", "theoretical_claims": "I believe the paper is more focused on practical applications.", "experimental_designs_or_analyses": "I reviewed the complete experimental design and analysis section, including both quantitative and qualitative evaluations, pruning performances, and ablation study.", "relation_to_broader_scientific_literature": "The contributions are related to the broader scientific literature. Additional experiments on downstream tasks using the generated images would further demonstrate the generalizability and robustness of the proposed model.", "essential_references_not_discussed": "The authors covered and discussed SOTA literature related to the domain.", "other_strengths_and_weaknesses": "**Strengths**\n\n*S1.* The paper is well-written, with clear pictorial depictions.\n\n*S2.* The idea of data-free image synthesis based on a text-to-image diffusion model is unique and well-motivated.\n\n*S3.* The authors performed comprehensive experiments on image synthesis, knowledge distillation, and pruning.\n\n**Weaknesses**\n\n*W1. Unclear methodological development.* While the idea of integrating a class-token as an embedding in the denoising process is interesting, I found the current methodological development somewhat unclear. How can a learnable class embedding token solve the class alignment problem while keeping all the other network blocks frozen? I believe this might lead to misalignment between the token and the fixed network parameters. While the $v_{Sc}$ ​ might represent an improved class-specific feature, the frozen network may not be able to properly utilize the new token representations.\n\n*W2. Validation of DAG and zero-shot experiments.* While the authors performed ablation studies on the presence of DAG/CAT, they did not thoroughly explore alternative design choices within these components, such as different methods for computing BatchNorm statistics alignment or alternative token optimization strategies. Besides, for a method that modifies tokens to control generation, they should test its generalization to unseen classes or domains, which is completely missing from the experiments.\n\n*W3. Computational complexity.* The authors did not evaluate or compare the computational requirements against baselines. Given the complexity of the dual guidance mechanisms, this is a critical practical consideration that remains unexplored.\n\n*W4. Experimental validation.* While the authors performed DFKD experiments, it lacks direct classification evaluations that would validate its central claims, testing whether standard classifiers correctly identify their generated images, comparing classification accuracy against competing methods, and demonstrating that their synthetic images can effectively train new classifiers from scratch (if possible). I believe these fundamental omissions weaken the claimed efficacy of their CAT contribution despite the model's purported ability to approximate training set distributions.\n\n\n*Minor point.* In Table 3, the symbols (in the DAG, CAT column) need to be consistent with those in the Method column. For instance, 'SD w/o DAG' should indicate the absence of DAG, so the symbol should appear in the CAT column, not in the DAG column. I might be wrong, please clarify.", "other_comments_or_suggestions": "The current rating reflects the diffusion-based data-free image synthesis idea, comprehensive experimentations, and ablation studies. I am open to reconsidering the rating if the authors address the raised weaknesses and concerns in the rebuttal.", "questions_for_authors": "Please see the weaknesses.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: The paper presents a method for DataFree Image Synthesis using text-to-image diffusion models. reviewers liked the idea and were mostly happy with the experiments and claifications from the authors after the rebuttal. The AC recommends acceptance
Decision: Accept (poster)