DomainGallery: Few-shot Domain-driven Image Generation by Attribute-centric Finetuning
Authors: Yuxuan Duan
Yan Hong
Bo Zhang
jun lan
Huijia Zhu
Weiqiang Wang
Jianfu Zhang
Li Niu
Liqing Zhang
Venue: NeurIPS 2024 poster (2024)
Keywords: few-shot domain-driven image generation
model transfer
text-to-image model finetuning
Primary area: diffusion_based_models

Abstract: The recent progress in text-to-image models pretrained on large-scale datasets has enabled us to generate various images as long as we provide a text prompt describing what we want. Nevertheless, the availability of these models is still limited when we expect to generate images that fall into a specific domain either hard to describe or just unseen to the models. In this work, we propose DomainGallery, a few-shot domain-driven image generation method which aims at finetuning pretrained Stable Diffusion on few-shot target datasets in an attribute-centric manner. Specifically, DomainGallery features prior attribute erasure, attribute disentanglement, regularization and enhancement. These techniques are tailored to few-shot domain-driven generation in order to solve key issues that previous works have failed to settle. Extensive experiments are given to validate the superior performance of DomainGallery on a variety of domain-driven generation scenarios.

DomainGallery: Few-shot Domain-driven Image Generation by Attribute-centric Finetuning
Yuxuan Duan'
Yan Hong²
Bo Zhang'
Jun Lan²
Huijia Zhu
Weiqiang Wang?
Jianfu Zhang Li Niu
Liqing Zhang
Shanghai Jiao Tong University
AAnt Group
(a) fow shot target dataset
"a (V) facee
(b) "a shetch face by Stable Diffusion v1.4
(d) Cross-category
(c) Extra Attribute
(f Personalization
DomainGallery
(c) Intra-category
dog
(V) old
subject dataset
happy face
"a [P3 [W] dog"
"a [V] vase"
[V] facc woaring a subject
"a (WF)
"o Juce"
[V] car
[V] dog dog
"a [V] [IF
Figure I: Given a fho-shot target dataset of specific domain such as sketches painted by an artist (a),
it is usually difficult to directly generate images of this domain using pretrained text-to-image models (b). By using DomainGallery we propose this work, we can achieve domain-driven generation in intra-category (c); cross-category (d); extra attribute (e); and personalization (f) scenarios.
Abstract
The recent progress in text-to-image models pretrained large-scale datasets has enabled us to generate various images as long as we provide a text prompt describ- ing what want. Nevertheless, the availability of these models is still limited when we expect to generate images that fall into specific domain either hard to describe or just unseen the models. In this work, we propose DomainGallery, a few-shot domain-driven image generation method which aims at finetuning pre- trained Stable Diffusion on few-shot target datasets in attribute-centric manner. Specifically, DomainGallen features prior attribute erasure, attribute disentangle- ment, regularization and enhancement. These techniques are tailored few-shot domain-driven generation in order to solve key issues that previous works have failed to settle. Extensive experiments are given to validate the superior perfor- mance of DomainGallery on variety of domain-driven generation scenarios.
*Conresponding authors.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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Introduction
Walking down the street, you artist painting portrait sketches for people. You are fascinated by a couple of masterpieces set by the side, showing his/her unique painting style which you find it difficult to describe by words. Deeply interested, you are in the mood for seeing more sketches, and it would be perfect to see him/her painting other things like dogs, especially your favorite ones at home.
As fundamental topic in computer vision, image generation has been attracting enormous research efforts. However, through the years from VAEs (23] GANs [3] to diffusion models [16], generative models are becoming more and more data-hungry in order to properly model the distribution of images, as the most recent Stable Diffusions (35] have been trained on billions of text-image pairs [39]. Thus unfortunately, it is usually infeasible to directly train generative model given only few-shot (around ten, or fewer) images of a specific target domain.
To tackle such challenging scenarios, one paradigm of solutions is model transfer, which first trains model on relevant source domain and then transfers it to the target domain by finetuning on the few-shot target dataset. Nevertheless, as Zhao et al. [56] have pointed out, the performance of model transfer methods will be significantly influenced by the relevance between source/target domains. Therefore, the applicability of these methods will be limited if we either fail to find proper source dataset, or just do not have enough resources to train a generative model from scratch.
With the recent progress in pretrained text-to-image (T2I) models [28 31 3 35 38 52]. it seems that anything can be generated simply by putting text prompt into an off-the-shelf pretrained T2I model. However, T2I models are still far from once for all solutions to image generation. Sometimes it is difficult or even impossible to precisely describe certain styles (e.g. sketches by an artist) and contents (e.g. new concepts or personalized subjects), or what we want is simply unseen (thus unknown) to the model. Fortunately, T2I models can serve as universal source models to be finetuned on specific target datasets. Recent works finetuning T2I models have mostly focused on either finetuning with relatively abundant images (tens, hundreds, or more) 11. or few-shot subject-driven generation whose datasets consist of a single person or object 12
37 On the contrary, few-shot domain-driven generation analogous to the conventional model transfer has rarely been explored.
In this work, we analyze and perform few-shot Hemain-driven image generation from the view of attributes, domain defined by common attributes shared among images (see Sec. 3 We seek to master four generation cases as illustrated in Fig. T Intra-category: The generated images contain both the domain attributes and the categorical attribute of the given target dataset, as in conventional model transfer; Cross-category: While containing con-categorical domain attributes. images of other categories can be generated through text control, as a feature of T2I models; Extra attribute: Either intra- or cross-category, we can attach additional attributes to the images; Personalization: We hope to combine domain-driven and subject-driven generation for better personalization. In order to achieve these goals, we propose DomainGallery, adopting DreamBooth-like (37) finetuning paradigm where the non-categorical domain attributes are learned and bound to an identifier word, so that the generation can be done via normal T2I pipeline. DomainGallery features four attribute- centric finetuning techniques which respectively settle four challenges:
(1) Prior attribute erasure: The prior attributes of the identifier word may possibly show up even if we have bound new domain attributes to Therefore, we pre-erase these prior attributes to avoid unexpected elements in images.
The domain/categorical attributes corresponding the identi- fier/category word may be leaked into each other, causing missing domain attributes and/or un- expected categorical attributes when we change the category word in cross-category generation. Therefore, we explicitly encourage domain-category disentanglement to prevent such leakage.
(2) Attribute disentanglement:
(3) Attribute regularization: The model is prone to overfitting when finetuned on few-shot datasets. Therefore, we regularize the finetuning process (with a strategy to construct paired source/target latent codes and regularization loss) to reduce overfitting caused by excessive presence of domain attributes and possible biases of dataset distributions.
(4) A Attribute enhancement: Sometimes the strengths of the domain attributes learned on a specific dataset category are insufficient for cross-category generation. Therefore, we adjust the intensity of the domain attributes when generating cross-category images for better fidelity.

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These techniques spreading over pre-fietuning (1)(2, finetuning (2)() and inference (4), are tailored few-shot domain-drived generation, aiming at solving key issues that previous works have failed to settle. Later in Sec. [ we conduct thorough experiments on several few-shot datasets. These experiments manifest the superior and satisfying performance of DomainGallery on all of the four generation scenarios, which can serve as a state-of-the-ar method of few-shot domain-driven image generation.
2 Related Work
Model Transfer Model Transfer (of conventional noise-to-image models instead of T2I ones) mainstream paradigm of solutions to few-shot image generation. Methods following this paradigm transfer models trained on related source datasets to target domains by finetuning on few-shot target datasets. Model transfer has been thoroughly explored using GANs 1O T8 25 30
27 29
34
43 46 47 50 51 55 58 611, with few base on diffusion models [20] 59 Since
T2I models came to light, people have been freed from choosing proper source datasets/models, and attention has been turned to finetuning T21 models as generic source models.
Subject-driven Image Generation As one of the most frequently explored finetuning scenarios, subject-driven generation has attracted much research effort (2) S 7 17 [4]] 44] 48 [54] since the
pioneering works Textual Inversion [12] and DreamBooth 37.. Actually, subject-driven generation can be categorized as special case of comain-driven generation, where the domain is defined by particular person or object. To preserve the subject identity, fidelity is highly preferred to diversity, as diversity is scarcely evaluated quantitatively by these works. On the contrary, in general domain- driven generation the domains are usually not confined to a specific subject. Therefore diversity is as important as fidelity, and we will evaluate both just as model transfer works do.
Few-shot Dorain-driven Image Generation We follow previous works to name our goal as few-shot domain-driven image generation. Analogous to subject-driven, the term domain-driven implies finetuning from T2I models, which enables us take advantage of the multi-modal capability of these models to achieve variety of generation scenarios (see Fig. c-f)). To the best of our knowledge, there is only one previous work focusing on this topic. namely DomainStudio [60]. It finetunes a Stable Diffusion model towards the target domain by learning an identifier similar to DreamBooth (37, yet equipped with additional losses to enhance diversity and high-frequency details. In Sec. 4 we will analyze some crucial issues in domainn-divee generation that previous works have failed to settle, and accordingly propose attribute-centric solutions to these problems.
Other Similar Tasks There are some other works focusing on resembling tasks. For instance, Everaent al. [ have focused on finetuning under limited data (tens to hundreds) with per-image text prompts. Such requirement of image quantity and prompts has limited its applicability. Another similar topic is T2I style transfer single style image and controls the content via text. A key issue shared by these works is how to clearly defining the boundary between style and content from single image. Instead, domains can be naturally delimited as the common attributes shared among multiple images, which also enables us to learn domain of certain contents, rather than styles.
40]. which usually extracts style information from
3 Preliminary
Domain Formally, a domain DD can be defined as a sample space A' and a data distribution Pasta on X [.. However, this definition is excessively general as any group of arbitrary images can form a domain. In this work, we would like to provide rather intuitive definition from the viewpoint of common attributes. We regard an image X to be composed of set of attributes J.N where each a, can be either abstract like a certain style, or concrete like specific category or certain content. Then, an image domain D can be defined as the common attributes shared by all the images of this domain: ap = IxED X. According to such definition, an image belongs to this domain if and only contains all the common attributes: X D ( ap C X. Take the few-shot sketches of faces in Fig. aa) as an example, up includes shared categorical attribute of human faces and the attributes of this specific painting style, while the content attributes indicating individuals are not shared. Therefore, any facial sketch of any person in such style belongs to this domain.
3

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Since in real-world scenarios, images in sho-shot datasets usually share a common category (e.g. face in Fig. Ta)), it is natural that categorical attribute should be one of the domain attributes. However, to extend domain-driven generation to cross-category scenarios as in Fig. [d), in this work we exclude the categorical attribute from op so that the domain attributes refer to non-categorical attributes only. For instance, the domain in Fig. 1 will be referred to as sketches (of anything) in this certain style.
Diffusion Model Diffusion model 16 42 is recent genre of generative models. It aims at reversing a diffusion process by recurrently predicting the noises based on noisy data and denoising them accordingly till proper images are rendered. For practical usage in high-resolution and condi- tional cases, Latent Diffusion Model (LDM) 35 is often adopted which moves the diffusion process latent spaces with pretrained VAEs 23 LDM commonly trained using a simplified objective as
(1) where 1, c, and t are respectively latent codes, conditions, fround-truth noises and time steps. The module Ta is the encoder of the condition and eg is the noise-pre predie-peiiting network which is usually a UNet [36]. As special instances of LDM, Stable Diffusion (SD) series are pretrained on large-scale text-image datasets such as LAION-5B 33 They serve as oate-of-the-ane T2I models that are widely used as base models in many tasks, including our DomainGallery as well.
LLDM (,,.... [||e co(le, t, Te(c))
DreamBooth As a pioneering work in subject-driven image generation, DreamBooth 37 binds the information of the subject to an identifier [V], which is a rarely used word such as sks, together with corresponding category word [N], such as dog. Then images of the target subject can be generated by using prompts like "a [V] [N]". For dorain-driven image generation, we inherit such design to bind cateerogg)) domain attributes to [V], so that by changing category words or adding extra attributes via text, DomainGallery is capable of generating various images within the given domain.
Low-Rank Adaptation Low-Rank Adaptation (LoRA) 19 is popular finetuning method fre- quently used on SD models. Instead of finetuning the parameters W E R x xdaut, LoRA finetunes rank decomposition matrices A E Rdm x and B E RRxxdous as in W = W + A B, where r is very small and W is fixed. Finetuned LoRA parameters be easily shared and used with base models due to much smaller sizes. DomainGallery adopts LoRA when finetuning SD on target datasets.
4 DomainGallery
In this section, we will give detailed description of DomainGallery. As in Fig. 2 the full pipeline has three steps: prior attribute erasure in Sec. 4.1 finetuning in Sec. 4.2 and inference in Sec.
4.1 Prior Attribute Erasure
Following DreamBooth, we link target domain attributes to an identifier [V]. Although we expect to
select rarely used word without obvious meaning, this word may have still been bound certain prior attributes. For instance, the commonly used sky is actually the abbreviation of a rifle [49], thus images generated with [V] in prompts will contain military elements, like the helmet in Fig. 2 In subject-driven generation such prior attributes are not problems, since the text condition "a [V] [NT", as a whole, will gradually overfit to the given subject dataset and override these prior attributes. Also, [V] will never be paired with another category (e.g. "a [V] car" when the subject is a dog), while in domain-driven generation we expect [V] to be applicable to any category. According to the results in Sec. 5.2 and Appendix B.I] if not pre-erased, these prior attributes will appear in cross-category images, which verifies that these prior attributes are merely concealed rather than eliminated, and necessary to erase them before usage.
Since the prior attributes are bound to the identifier in data-driven manner when T2I models are pretrained, it is difficult to theoretically specify which attributes have been linked to [V]. Therefore, we propose an empirical solution to prior attribute erasure. Based on noisy source latent Lare that has been added noise e in the forward process, DomainGallery predicts the added noises érrc and Ex using the same LoRA-equipped UNet respectively with source text condition Care "a [N]" and target condition Cegs = "a [V] [N]". Then, the prior attribute erasure loss is defined as
(erre = 60.0 (fere, Care) (60-> = 60,0 (faree Otge)
Ieerae MSE(E MSE(()=..,B((ec)))) (e((rr)));
(2)

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Cigt
UNet
Eg-at
CFG
a [N]
(P)
LORA
Lerast
Crr
x
0 [[V]"
[V] [N]
E
Src
Doisy
larc
UNet
ESTC
A A
x A2
Isec
LoRA
VN-N
Aiprior
V-ucond
Crrc
List
(a) Prior ttribute Erasure
UNet
Esrc
(c) A ttribute Enhancement (inference time)
Cagt
Cigt
Cigt
LoRA
Doisy legt
E
Egg
UNet
Etgt
UNet
UNet Encoder
ligt
LORA
paisy src
LoRA
Letm
Care
Carc
poisy Igrc
E
Tsre
UNet
Earc
UNet
Lare
UNet Encuder
Isre
LoRA
LoRA
LoRA
Apriir
Crc
Care
Lguson
Recureent
(b) Finetuming
UNet
Eare
Denoising
Figure 2: An overview of DomainGallery, (a) Before finetuning, we erase the prior attributes of the identifier [V] by matching the predicted noises when using source/target text conditions via Lerase- (b) During fintuning. besides training ordinarily on target datasets (top-left), domain-categen category attribute disentanglement loss Ldisen (bottom-left) and transfer-based similarity consistency loss Laim (right). (c) When generating cross-category images, we enhance the domain attributes referred by [V] in CFG-like manner. Dashed arrows indicate gradient stopping.
additionally impose
where omit time step & and text encoder in the UNet Eg for brevity, indicates LoRA parameters and gs(-) ) is the gradient stopping operation that stops the gradient from propagating through updating the parameters inside. By imposing Lerase- we hope that the model predicts the same with or without [V], hence the prior attributes [V] will be removed.
Besides Lerase, the prior preservation loss Lprior of DreamBooth is also applied which trains on source images Aare generated by the base model of SD itself training diffusion model ordinarily via Eq. (I). Also, disentanglement loss Leisen is also included, which will be detailed in Sec. 4.2 After erasure, the learned LoRA parameters will be used to initialize LoRA in the finetuning period.
4.2 Finetuning
With prior attributes of [V] erased, DomainGallery then learns to bind the target domain attributes to [V]. addition to a standard finetuning on target datasets by Lige via Eq. (I), with prior preservation on pre-generated source images, we propose domain-category attribute disentanglement loss Laisen and transfer-basee similarity consistency loss Lgims as depicted in Fig. 2b)
Domain-category Attribute Disentanglement Since few-shot datasets usually share common category (e.g. face in Fig. [a)), when finetuning on such datasets, the (non-categorical) domain attributes in [V] will always show up together with the categorical attribute in [N], both in target images Tegt and target prompts Ctgt- As result, possible that certain domain attributes may leak into [N], and/or conversely the categorical attribute may leak into [V]. Although is not
5

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problem either for subject-driven generation since [V] and [N] will always be paired when generating images, such entanglement between [V] and [N] will harm cross-category scenarios of domain-driven generation. As experimental results shown in Sec. 5.2 and Appendix B.1 if we replace [N] with another category, sometimes domain attributes are partially lost, or elements of the original category
still appear.
To tackle this issue, we try enhance the disentanglement between [V] and [N], so that all the domain attributes will only be learned into [V] without leaking into [N], and categorical attributes in [N] will not be lost. In other words, attributes of [N] after finetuning should not be different from those before. As we use LoRA, the base model before finetuning is ready to use by simply disenabling
LoRA parameters & temporarily since the UNet is fixed. Based on noisy source latent Lare and source text condition Cyrc: the fomain-category, attribute disentanglement loss can be formulated as
eaxc co Care)
L Liissn (FE(( gs((xc)))
(3)
Cesc = co Carc)
where Eo without e is the base UNet whose LoRA parameters are detached.
Attribute Regularization Adding regularization is a common practice of model transfer methods
30 [1 57 to prevent overfitting, where features from paired source/target images generated from the same noise are usually required. However, according to the training objective of SD in Eq. !
no fully denoised latent (i.e. at time step 0) will be generated, let alone paired source/target latents. DomainStudio [60] has proposed a regularization, which applies similarity consistency loss (30 on
batches of source/target images Igto/fgg decoded from denoised latents laro/log after single-step denoising from noisy latents lare/ However, there are four drawbacks in this design: (1) single-
step denoising usually does not lead to meaningful latents/images unless the timestep is small; (2)
decoding latents into images induces significant overhead of computation and storage: (3) computing
cosine similarity between pixel-level images is less reasonable; (4) lere/ are unpaired as they derives from unpaired input source/target images Tare/ which do not fit the similarity consistency loss requiring paired images/fcattres. In our DomainGallery, we propose strategy of constructing paired source/target latents. followed by a new regularization term named transfer-based similarity consistency loss, which overcomes the aforementioned drawbacks.
First try to settle (1) and (4) by constructing denoised, meaningful and paired latent codes. As in the right part of Fig. bb), given a batch of Lare at time step f,, we conduct an n-step recurrent denoising following the accelerated denoising process of DDIM 42 and a linearly decreasing time step schedule from / to O. We intuitively set n = 5 to balance denoising quality and speed. We do recurrent denoising for twice, respectively with source/target text Caro/ and obtain laro/ If we decode them into images f will find that ig- simultaneously have partial target domain attributes after conditioned on Otgt, and share certain similarity with ī. since they derive from the same lrre. Hence lor are paired. Next, without actually decoding them into images, we reuse the encoder of UNet as a pretrained feature extractor to directly extract multi-layer features from aare and 4, and compute the similarity consistency loss as
N B DKL. ((steellss(pate))
Taam B
N
5,k Pa-1 Softmax
(CosSiml (B (rs)), (' (rs))) (rse))
(4)
1.8 Pere
Softmax (CosSSim (frce Care), 1 (ere))
( Car))) brses)
where f represents features extracted by the UNet encoder at its k-th layer (of N layers). From the viewpoint of the i-th latent (of B latents in the batch), first its cosine similarities with other latents are computed, followed by a softmax operation transforming them into a probabilistic distribution p'. Then, Kullhack-Leibler Divergence will be computed between two distributions respectively from je and نا and will be averaged among all and layers k. In conclusion, Lsim helps prevent overfitting by matching the similarity distributions among batch of paired (. By operating on the features directly extracted from latents rather than on images, our Laim settles (2) and (3) well.

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4.3 Objective
The objective functions of prior attribute erasure and finetuning are respectively
Lerasure = Isprior + Adisen Ledisen + Aerasn Lerase,
(5)
Lfinetune Legt Aprior Lprior + Adisen Ldiisn + Asim
-igim:
where Aprior = 1.0, Adisen = 10.0, Aerase = 10.0, Asim 1.0 generally renders good results. Note that utilize LoRA in DomainGallery, only the additional parameters & of LoRA will be updated.
4.4 Inference
In preliminary cross-category experiments, the domain attributes are not sufficiently manifested sometimes. A possible reason is that Lsim has limited the strengths of these attributes to the minimal, just enough to transfer images of the original category, while for cross-category scenarios these attributes may need enhancing. As shown in Fig, 2)c), propose inference-time attribute enhancement based on classifier-fret free guidance (L5. Specifically, after applying CFG with default weight \1 7.5, we additionally increase the strength of [V] by either of
VN-N: (("") Ar(e("a (V) (V))) -""")) + A Ae(e("a (V) (N]") (N]"));
(6)
V-uncond: = <(")) Ai(e("a [V] [N]") -<("")) + A >a(e("IVI") e("")).
Between the enhancing modes above, we empirically find that V-uncond generally outperforms its counterpart (see Appendix B.I. We will by default apply V-uncond with X = 1.0 during
cross-category generation.
4.5 Personalization
For personalization scenarios, we straightforwardly combine our DomainGallery with DreamBooth in single stage. Specifically, during the finetuning process in Sec. 4.2] the model is additionally finetuned on target subject images and source images subject category via Eq. (I. In such cases, the objective of finetuning in Eq. (5) will be rewritten
Ipperson
L-inetune + Asubject (T.ubbjcc + >prior (Tigt
I Zzsujjctt) prrior
where Àsubject is empirically set to 1.0. While suppose that there may be a more delicate way to equip DaleryGallery with subject-driven methods, we would like to leave it for future works.
Experiment
5.1 Experimental Setting
Baseline Our baseline list includes DreamBooth [37. as the basis of our method; a LoRA version of DreamBooth, since we utilize LoRA in DomainGallery; and finally DomainStudio 600 as
19
the only previous work in sew-shot Homain-driven image generation.
Dataset We test our method on five widely used 10-shot datasets, including CUFS sketches 45
f[ce), FFHQ sunglasses [20 ([N]:face), Van Gogh houses (30) ([N]: house), watercolor dogs 41 (IN]: dog) and wrecked cars [30] ([N]: car). Note that though sunglasses and wrecked cars may also be generated by directly mentioning their content attributes in text prompts, we still try on these datasets prove that DomainGallery can also learn content attributes. Experiments are conducted on resolution 512 x 512 except for DomainStudio which is only capable of 256 x 256 even on a 40GB VRAM GPU,
Metric We provide quantitative results for intra-category generation since we have dataset images as ground truth. For datasets with full sets (CUFS sketches and FFHQ sunglasses), we compute FID 14 between 1,000 samples with the full sets. For the others, we replace FID with KID (x 10a) which better fits few-shot scenarios 99] 10
56 58]. Intra-clustered LPIPS 30 53 of
1,000 samples with the few-shot training sets is also reported as a standalone diversity metric.
Detail For other details of the experiments and DomainGallery, please refer to Appendix A
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Table 1: Quantitative results of the intra-category scenarios on CUFS sketches, FFHQ sunglasses, Van Gogh houses, watercolor dogs and wrecked cars. The underlined results of DreamBooth have severe overfitting issues hence achieve good KID scores, see qualitative results in Fig. To
Method
sketches
sunglasses
houses
dogs
cars
FID
I-LPIPST
FID
I-LPIPS
KID|
I-LPIPS
KID|
I-LPIPS
KID|
I-LPIPS
DreamBooth [37
70.41
0.4609
44.90
0.6451
48.43
0.6882
32.60
0.4005
8.81
0.5661
DreamBooth+Look
52.80
0.4636
41.22
0.6452
44.51
0.6744
68.80
0.4992
26.68
0.6063
DomainStudio
600
51.73
0.4184
66.66
0.6089
41.06
0.6367
71.33
0,4059
32.31
0,5577
DomainGallery
44.86
0.5060
43.10
0.6924
32.20
0.7255
61.95
0.5216
23.63
0.6336
Dranllich
18-bie 9% Auniii
Darals lamenttedio
Figure 3: The 10-shot CUFS sketches dataset (left) and the intra-category samples generated by the baselines and DomainGallery with prompt "a (V) face" (right).
5.2 Experimental Result
Intra-category As the most basic scenario, we generate target images of the original categories. According Tab. [ DomainGallery generally outperforms the baselines w.r.t. both fidelity and diversity. These scores also match the qualitative results on CUFS sketches in Fig. 3 where Domain- Gallery can precisely capture the painting style of the target domain. Also, due to the effectiveness of our transfer-basee
similarity consistency loss Leim, the diversity of DomainGallery surpasses the baselines by large margins, while achieving competitive or even better fidelity. Refer Fig. I0 in Appendix B.2 for qualitative results on the other datasets.
Cross-category We illustrate qualitative results of cross-categon generation on Van Gogh houses and watercolor dogs in Fig. 4 Since no previous method has pre-erased the prior attributes of [V] (sks) before usage, prior attributes of military elements can be observed in the samples generated by all the baselines. Besides, none of the baselines explicitly imposes disentanglement between [V] and [N], attribute leakage can be observed on both datasets. Some images of DomainStudio still contain houses even if we change [N], manifesting leaked categorical attribute in [V]. On the other hand, many cross-category images of the baselines do not properly depict target domain attributes while their intra-category images do in Appendix [B.2] Such phenomenon verifies that domain attributes have been partially leaked into [N] and will disappear if we change it. By adopting prior attribute erasure and enhancing domain-category attribute disentanglement, our DomainGallery avoids these issues and performs well. Samples on the other datasets are shown in Fig. 11 in Appendix B.2
Extra Attribute In Fig. 5 and Fig. [[c), we show some images generated by DomainGallery on CUFS sketches with extra attributes added to either intra- or cross-category scenarios. We may infer from these results that though we only provide simple prompt (i.e. "a [V] [N]") rather than detailed description for each image of the target dataset, training DomainGallery does not destruct the original text-image structures of SD. The images are still under full control through text prompts, including facial expressions, additional contents (e.g. accessories), sub-category (e.g. breed of animals), background, and specific instances (e.g. celebrities or brands).
Besides, the bottom row of Fig- 5 llsstrrtess some samples where the extra attributes (blue) provided in the text prompt are in conflict with the domain attributes (colorless). In such case, DomainGallery is capable of generating images with partially fused attributes. While the images are generally in
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16-ahot Van Gregh Iouics
DamaisCaleey DonsieSaain
"a (V) Jace
(V] cat"
[V] car
DeessBood
18ahie wutercobn dogs
DenaisGatles Domaintaaioo
mmn
ta (?) our"
"a [V] vase
[V] house"
Figure 4: The 10-shot datasets (left) and the cross-category samples generated by the baselines and DomainGallery (right), Van Gogh houses (top) and watercolor dogs (bottom).
(V) girl face"
"a [V] sad face
face face wearing a mark""
"o [V1 face of Elon Musk"
Persian eat"
"a [V] dog swimmingg
house mountain"
Lamborghint
C "a [a[V] bird with blue feathers"
Figure 5: Intra-category (top row) and cross-category (middle row) samples with extra attributes given by texts generated by DomainGallery, on CUFS sketches. The bottom row additionally show the case where the text contains conflicting attributes.
grayscale, some blue feathers still appear. These results verify the generalization ability of our method and suggest that may be open to other generation scenarios such as local editing and style blending.
Personalization the last scenario, DomainGallery is combined with DreamBooth to learn target domain and target subject simultaneously. as described in Sec. 4.5 Results in Fig. 6 manifest that such combination is feasible for both intra-category (the target dataset and subject share the same category, e.g. watercolor dogs and the subject of specific dog) and cross-category (otherwise) pairs of datasets. Together with the results the previous scenario with extra attributes, the satisfying performance of DomainGallery shows its potentials to be applied to parallel downstream tasks.

---

subject dataset
CUT'S sketches
Van Gogh houses
watercolor dogs
"a [W] [V dog"
[Vy] dog
"a [V3] [WF dog
"a (W7 vase"
[[ (W] vase"
"(a (V3) [W] vase"
"a [V. [W] vose"
Figure 6: Few-shot subject datasets (left, partially shown) and the personalized samples generated by DomainGallery on CUFS sketches, Van Gogh houses and watercolor dogs.
Ablation Study To prove that the proposed attribute-centrie techniques can indeed effectively improve the performance DomainGallery in various generation scenarios. we conduct extensive ablation studies focusing on these techniques and leave them in Appendix B.T due to page limit.
6 Conclusion
In this work, focus on few-shot Nomain-drived image generation by analyzing several key issues that previous works have failed to settle, and accordingly proposing new method named Domain- Gallery. DomainGallery features four attribute-centric finetuning techniques that aim at solving these issues, namely prior attribute erasure, attribute disentanglement, attribute regularization and attribute enhancement. With these designs tailored domain-driven generation, our DomainGallery achieves convincing performance both intra-category and cross-category generation scenarios, while sup- porting extra attributes added by text prompts. Additionally, DomainGallery can be aggregated with subject-driven generation as well, which further extends its applicability. In Appendix C we will discuss possible limitations and potential future works DomainGallery.
Acknowledgements
The work was supported by the National Science Foundation China (62076162, 62471287, 62302295), and the Shanghai Municipal Science and Technology Major Project, China (2021SHZIDZX1002) This work was also supported by Ant Group.
References
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Appendix
A Implementation Detail
Model Our DomainGallery takes Stable Diffusion (SD) 35 as its base model. For the sake of faireess, DomainGallery and all the baselines share common base model of SD th.4gthough Do- mainGallery is probably applicable to newer versions since the attribute-centric techniques proposed in this work are based on specific structures of the current version.
During the periods prior attribute erasure and finetuning, we apply LoRA 19 of PEFT to UNet of SD, with rank r 4, and on parameters of to_k, lo_q. 10_1, moout.0, add kprrjj and add v proj by default. We do not finetune the text encoder т or apply to
Training For prior attribute erasure, we train the model for 500 steps, with batch size 4 and learning rate 1 x 10-4. While for finetuning, we initialize LoRA with the parameters 6 where prior attributes of the identifier [V] are erased, and train the model for 1,000 steps. with batch size and learning rate 5 x 10 10-5, During both periods, gradient checkpointing and 8bit Adam are also applied to save VRAM. All the experiments running DomainGallery in this work are done single NVIDIA RTX 4090 GPU with 24GB VRAM.
For finetuning, we additionally apply offset noise on CUFS sketches and watercolor dogs, as their images are obviously lighter than average.
When generating images during inference period, we apply DDIM 422 scheduler with 50 steps and scale of CFG [13 A = 7.5. When generating cross-category images, attribute enhancement is also applied as Sec. 4.4 Note that delicately selecting scheduler and its parameters may render better images, however it Is beyond the scope of this work.
Inference
B
Additional Experiment
B.1
Ablation Study
In this section, we provide ablation studies regarding the four attribute-centric techniques (prior attribute erasure, attribute disentanglement, attribute regularization, and attribute enhancement) proposed in this work, to prove that these techniques are indeed effective to few-shot domain-driven generation.
Prior Attribute Erasure In Fig. toop we ablate the process of prior attribute erasure described in Sec. 4.T. and generate some cross-category images after finetuning. Compared with the full DolainGillerry in Fig. (bbottom) military elements can be commonly observed, as these prior attributes of the identifier sks have been kept. Hence, pre-erasing the prior attributes of [V] is necessary before finetuning.
Attribute Disentanglement We remove the domain-category attribute disentanglement loss Ldisen in Sec. 4.2 during finetuning and generate some cross-category images in Fig. [middlee. Without Ldisen enhancing disentanglement, the domain attributes are partially lost when we change the category word [N], as some images do not present proper styles. On the other hand, the original category has been leaked into [V], as human faces (or patterns like human faces) appear in the images sometimes, though have changed [N]. These phenomena necessitate the enhancement of disentanglement in DomainGallery.
Attribute Regularization In Fig. we illustrate the trend of FID and I-LPIPS scores CUFS sketches when we use different weights Asim between 0.0 and 2.0 for the transfer-basel similarity consistency loss T.gm- As the results shown, the diversity of the generated images is indeed improving
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DomainGallery wio prior
DomainGallery w/o attribute disentanglement
DomainGallery
(V] dog"
"a [V] vase
[V7
Figure 7: The cross-category samples generated by DomainGallery without prior attribute erasure (top), DomainGallery without attribute disentanglement (middle), and the full DomainGallery (bot- tom) on CUFS sketches.
64
2.0
62
0.0
60
56
54
52
0.2
50
0.5
48
46
44
0.49
0.50
0.52
I-LPIPS @
Figure 8: FID and I-LPIPS scores achieved by DomainGallery with different Asim (annotated above the corresponding data points) ranging from 0.0 to 2.0, CUFS sketches.
increase Agim- Besides, since Lsim can prevent the model from learning unnecessary attributes induced by the bias of the few-shot datasets, it also enhances the fidelity of the generated images. However when the weight exceeds 1.0, the regularization inhibits the model from learning necessary domain attributes as the fidelity begins to deteriorate.
Attribute Enhancement As the last part of the ablation study we investigate the effects of attribute enhancement in Sec. 4.4 during inference time. First we try apply our attribute enhancement to the three baselines (DreamBooth (37], DreamBooth + LoRA, and DomainStudio (60). As the top three rows of Fig. 9 show, enhancing [V] alone cannot improve the fidelity of the images, unless
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DreamBooth w/ V-uncond
DreamBooth + LoRA V-uncond
DomainStudio V-umcond
DallerGallery w/o attribute
DolaerGallery w/ VN-N
DomainGallery V-uncond
(V] dog"
"a (V) vase
[V] car"
Figure 9: The cross-category samples generated by the three baselines with attribute enhancement (top three rows), by DomainGallery without attribute enhancement (fourth row), and by DomainGallery with attribute enhancement of either VN-N or V-uncond mode (last two rows) on CUFS sketches.
properly learn the target domain attributes into [V] in the first place, as our DomainGallery does (see the bottom row of Fig. Dp.
Besides, as we have proposed two modes of attribute enhancement in Sec. 4.4 we would like make comparison between them. In the last two rows of Fig. 9 we illustrate samples generated following either mode. Although both modes can enhance the domain attributes to certain extents compared to DomainGallery without attribute enhancement in the fourth row of Fig. 9 the mode of V-uncond generally performs better than counterpart. Therefore, we utilize V-uncond DomainGallery by default when generating cross-category images.
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B.2 Additional Result
In this section, we present additional qualitative results that are not illustrated in the main paper due to page limit.
Intra-category Besides the intra-category images on CUFS sketches shown in Fig. we depict those on the other datasets in Fig. TT] Generally, our DomainGallery surpasses the baselines on all the datasets. It is also worth mentioning that in few-shot domain-driven generation, the domains are not limited to certain styles (as in CUFS sketches, Van Gogh houses and watercolor dogs). Instead, our method is also applicable domains of certain contents (FFHQ sunglasses and wrecked cars).
Cross-category Cross-categors images of the other datasets not shown in Fig. 4 of the main paper are Fig. [1] Similar to the results the main paper, elements of the prior attributes of [V], and attribute leakage between [V] and [N] can also be observed in these images, which necessitates prior attribute erasure and attribute disentanglement proposed in DomainGallery.
C Limitation and Future Work
In this work, we propose DomainGallery, new method for few-shot dorvin-driven image generation. Although the experiments in Sec. and Appendix [ have validated the capability of DomainGallery. there still some limitations w.r.t. the availability of our method which indicate directions for future works, as discussed below.
Our method may not be able handle the cases where the datasets consist images of different categories (e.g, set of paintings of various objects by a certain artist), since DomainGallery follows DreamBooth that finetunes on a single category word [N], However, with minor modification to the DreamBooth-lice finetuning pipeline, DomainGallery may be capable of such cases by using per-immn per-image category words.
Although assume that domains should be defined based obvious common attributes, sometimes there are composite domains that include several sub-domains (e.g. portraits painted by several artists of the Renaissance). In such cases the common attributes among all the images may be subtle and hard to tell. Therefore, for few-shot domain-driver methods (not limited to DomainGallery), domains with clear common attributes are preferred.
Currently the performance of DalainGallery on domains of contents (e.g. FFHQ sunglasses) is still in need of further improvement, as we admit that the cross-category images on FFHQ sunglasses shown in Fig. 11 have undergone some cherry-picking. We suppose that it is much more difficult to finetune models on few-shot datasets of certain local contents than global styles (e.g. CURS sketches), since semantic relations between the contents and the backgrounds (e.g. where to put sunglasses on faces, or even on faces of animals) can only be well learned through rather adequate data. Therefore, how make few-shot domain-driven methods master on content domains is another direction for future works.
D Broader Impact
As new method for few-shot fomain-driven image generation, DomainGallery can be used either in creative Al applications, generating image data as a non-traditional data augmentation for various downstream tasks. Furthermore, as image generation is fundamental task in computer vision, the idea of DomainGallery may also be applied to researches in other topics.
However, similar to all the methods of image generation (including but limited to few-shot domain- driven image generation), our method may induce possible societal harms, including fake image generation for misuse and copyright violation, depending on the specific applications. Therefore, hereby request proper usage of DomainGallery.
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Drnosbood Druanlloch ...A
18-bee 09T10 unglaae
miruntcttio DamaiiSedoo
Drandoon
Hi-abes Vann Coegh boseon
Desesboch -ERA
Hbbbat materaate dags
Dreastech -LRA
Disuietattr Domenttario
Dmaniiiehh
16-Oler uncced cars
Droesbalt LoKA
Figure 10: The 10-shot datasets (left) and the intra-category samples generated by the baselines and
DollinGallery (right), respectively on FFHQ sunglasses ("a [V] face "), Van Gogh houses ("a (V]
house") "), watercolor dogs ("a [V] dog") ") and wrecked cars ("a [V] car") (from top to bottom).
18

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DramnBoohh
Desateon
1b-hi CTPS C1IS slatches
DamissCalar Damanstuali
"a [V] dog.
"o [V] vuse
"a [V] car"
DrommBoodh
1babee F7H1 sungless
Dewettoot -Lok
DamaitGalkry DamainSSnnll
"a [V] dog
oat"
monkey"
Dreanbooth
Deaethook
10 shat wrecked curs
DonaisSodi
"a [P]
"a [V]
(P]
Figure 11: The 10-shot datasets (left) and the cross-category samples generated by the baselines and DomainGallery (right), CUFS sketches (top), FFHQ sunglasses (middle) and wrecked cars (bottom).
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NeurIPS Paper Checklist
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Reviews:
[{"review_id": "qksx7JVrJL", "reviewer": "Reviewer_2oCE", "summary": "This paper addresses the few-shot domain transfer problem in text-to-image diffusion generation, where the goal is to keep the style and attributes of the source examples while being able to generate images of potentially different subject matters. It binds the attributes to rarely-used tokens, but observes that these tokens could potentially induce correlated unintended features, and it adopts a prior-attribute erasure procedure to remove such correlation. It then finetunes the model for domain transfer, enforcing disentanglement of attributes and subject categories and applying attribute regularization. Additionally, for cross-category generation, it proposes an attribute enhancement technique to make the learned attributes more prominent in the generations.", "questions": "* It would be helpful to have more clarifications on whether the qualitative results mostly randomly selected or hand-picked, especially given that the performance of cross-category generations are primarily demonstrated through qualitative visualizations. The paper mentions that the cross-category generations for FFHQ sunglasses in Fig 11 involved some cherry-picking, but does not seem to specify for the others.\n\n* For better understanding of the proposed method's behavior, it would interesting to explore the boundary for what attributes get encoded into the binding tokens. For example, when all examples contain one subject centered in the pictures, whether/to what extent the number, position or subject size are considered part of the attributes and get picked up by [V]. It would also be interesting to see how the method behaves when the textual prompt contains conflicting attributes with the learned binding token (e.g. \"A [V] bird with blue feathers\" where [V] is learned from colorless sketches).", "limitations": "The authors have adequately discussed the limitations and societal impact.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "* The paper investigates an interesting task of attribute-centric transfer. \n* The method is well-motivated and conceptually solid. The paper observes limitations in existing approaches, such as the attribute correlation with binding tokens, and entanglement between attributes and subject categories, and effectively addresses these limitations in the proposed method. \n* The experiments consider a variety of settings such as intra- and inter-category transfer, and transfer with additional attribute or specific subject identity. They encompass a variety of domains and settings, and provide numerous qualitative visualizations to facilitate understanding of the method performance. The ablation studies effectively verify the contribution of each component.", "weaknesses": "* The paper has discussed most of the limitations of the method, such as in cases where the examples contain multiple subject categories or multiple sub-domains, or in cases with certain local/content attributes. \n\n* One additional case is that as the method infers the attributes infers from the examples, sometimes it can pick up unintended attributes that the few-shot examples happen to share (e.g. for FFHQ glasses, cross-category generations mostly have outdoor/nature backgrounds, even though this may not be necessarily intended for this setting). It would be interesting to see extensions that allow more explicit control over including/excluding certain attributes.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "### Weakness 1 & 2\n\nWe would like to thank the reviewer for raising concerns about the *unintended attributes*, which is indeed one of the core issues in few-shot image generation. Due to the lack of sufficient data samples, there is usually a gap, either large or little, between the distribution of the (few-shot) dataset and the (agnostic) distribution of the domain. For example, an unexpected attribute that happens to be shared among all the dataset images, as mentioned by the reviewer. (With regard to this topic, we recommend a survey on few-shot image generation. [1])\n\nAs in Line 102 of the paper, we define a (dataset-derived) domain as *the common attributes shared by all the images*. Therefore, all the attributes will be encoded into the identifier as long as they are shared among the images, though some of them may be unexpected (e.g. outdoor backgrounds shared by the sunglasses dataset). From this point of view, our DomainGallery still complies with such definition.\n\nIf we intend to manually exclude some unwanted attributes, a possible solution is to put them into the text prompt when finetuning the model, so that they will not be encoded into the domain identifier [V], just as we explicitly exclude the shared categorical attribute by adding a category word [N]. Since it takes time to adapt our DomainGallery to such setting, it is regretful that we are not able to present some results during this response and discussion period. However, we are still grateful for this suggestion and we will do research on this topic in future work.\n\n\n### Question 1\n\nExcept for the case of sunglasses described in the limitations in Appendix C, our qualitative results are indeed mostly randomly selected. Due to the page limitation, we are only able to present just a couple of samples for each setting, so we may have slightly picked some representative samples that manifest the typical quality of the images in order to show more clearly the drawbacks of the previous methods and how our method solves them.\n\nIn the PDF file in the global response (Fig. 3), we have shown some **uncurated** qualitative results. Although our DomainGallery is not yet a 100% perfect model (just as any other generative model), we have already made great progress in settling the challenges mentioned in our paper, such as prior attributes, attribute entanglement, and inadequate strengths of attributes.\n\n\n### Question 2\n\nJust as our response to Weakness 1 & 2 says, our model will learn all the common attributes into [V]. Thus if all the images of a certain dataset contain identical or similar number, position, or size of the subject, they will be included in [V] as well. As in Fig. 3 of our paper shows, all the generated samples have one face in the center, with a medium size and a frontal or nearly-frontal view.\n\nAbout possible conflicting attributes in [V] and the text prompt, we have illustrated some samples in the PDF file in the global response (Fig. 1) with prompt *\"a [V] bird with blue feathers\"* where [V] is learned from CUFS sketches, as the reviewer suggested. Although there are conflicting attributes, our DomainGallery seems to be capable of generating images with partially fused attributes. While the images are generally in grayscale, some blue feathers still appear. Although such trials may bring some interesting outcomes, we suggest handling conflicting attributes with care.\n\n\n[1] Z. Li et al., A Comprehensive Survey on Data-Efficient GANs in Image Generation.\n\nWe will try our best to settle the reviewers' concerns. Please feel free to leave further comments. Thanks!", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "dvjou9Pp3y", "reviewer": "Reviewer_Ue4P", "summary": "Text-to-image generation models pre-trained on large-scale datasets made progress but the models are still limited when we expect to generate images that fall into specific domain or style that are hard to describe or unseen to models. This paper proposed the DomainGallery, a few-shot domain-driven image generation method which aims at finetuning pre-trained stable diffusion on few-shot target datasets in an attribute-centric manner. The proposed method focuses on attribute operations, including erasure, disentanglement, regularization and enhancement.", "questions": "Please reply with points in the weakness.", "limitations": "Please see weakness", "rating": 5, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "-\tThe paper is well-structured and well-motivated.\n-\tThe method seems sound and reasonable.\n-\tThe problem to tackle is a sensible and essential problem.", "weaknesses": "-\tIt seems to me that there are several pipelines and tasks to perform in this paper, but the technical challenges should be illustrated in a concise and precise manner. It seems to me that the engineering solutions and objectives are clearly illustrates but the technical challenges are not summarized well. In this way, the technical contributions seem weak and unclear. \n-\tThe proposed method seems like a combination of existing techniques. The novelty seems limited unless the authors argue specifically. \n-\tThere seems to lack specific error analysis on whether the result improvement is due to the proposed method. It would be very important to demonstrate this, either quantitatively, or qualitatively. At least, in different dataset directions, e.g skeches, sunglasses. shall we demonstrate what exactly causes the improvements? Why improvements on which samples in the dataset?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "### Weakness 1\n\nThanks for the advice. Here we recap the four technical challenges we have mainly focused on in our work:\n\n- **Challenge 1:** Prior attributes of the identifier [V] may show up in the generated samples even if we have bound new domain attributes to it.\n- **Challenge 2:** The identifier [V] and the category word [N] may sometimes leaks some attributes into each other.\n- **Challenge 3:** The model is prone to overfitting when finetuned on few-shot datasets.\n- **Challenge 4:** Sometimes the strengths of the learned domain attributes are inadequate for cross-category generation scenarios.\n\nIn order to let the readers better comprehend these technical challenges and how DomainGallery settles them, in Sec. 4, we chose to discuss these four challenges and correspondingly our four solutions **one by one** from Sec 4.1 to 4.4 (also see the four techniques in our response to Weakness 2 below). Due to page limitation, we were not able to give detailed description in the introduction, yet we briefly mentioned them in Line 52--57 when we pointed out our four attribute-centric techniques. We will follow the reviewer's advice and deliver a better and clearer summary on these challenges in the next version.\n\n\n### Weakness 2\n\nWe would like to further clarify the novelty of our DomainGallery and its four attribute-centric finetuning techniques as below, which respectively settle the four challenges summarized in our response to Weakness 1 above.\n\n- **Technique 1: Prior attribute erasure & Technique 2: attribute disentanglement:** To the best of our knowledge, no previous work (either on domain-driven or subject-driven generation) has adopted similar strategies. As explained in Line 141 and 171, these two issues do not even exist in subject-driven generation, since the identifier [V] and the category word [N] will always be paired together. While in domain-driven generation where they **are** problems indeed, the only previous method (i.e. DomainStudio) failed to recognize and try to solve these two issues either.\n- **Technique 3: Attribute regularziation:** With regard to this part, our core contribution is not the similarity consistency loss itself, but is the strategy to construct **paired** source/target codes in **latent space** via **few-step recurrent denoising** within the single-timestep training procedure of diffusion models, so that our DomainGallery is open to various choices of regularizations (including but not limited to the similarity consistency loss we apply in our model). Without such strategy, the previous work DomainStudio directly applied regularization on **single-step-denoised, unpaired, and pixel-level** source/target images, which we have discussed and proved to be much less effective. Refer to Line 189--194.\n- **Technique 4: Attribute enhancement:** There are indeed some previous works (though not in domain-driven or subject-driven generation) adopting similar methods to enhance certain attributes. However, in this part we have proposed two modes (VN-N & V-uncond) of attribute enhancement, and have verified the effectiveness of our final choice (i.e. V-uncond) throughout the ablation studies in Appendix B.1.\n\n\n### Weakness 3\n\nAbout whether the result improvement is due to the attribute-centric finetuning techniques we propose, we have already presented **extensive ablation studies in Appendix B.1**, proving the necessity and the effectiveness of each part of our design with both qualitative and quantitative results (also approved by Reviewer 2oCE in Strength 3). Due to page limitation, we were not able to put them in the main paper. However, we do recommend reading this part to get a better understanding of our designs.\n\n\nWe will try our best to settle the reviewers' concerns. Please feel free to leave further comments. Thanks!", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "uW4pPOwgu9", "reviewer": "Reviewer_btQa", "summary": "This paper introduces a few-shot domain-driven image generation method that finetunes pretrained Stable Diffusion in an attribute-centric manner. It features prior attribute erasure, attribute disentanglement, attribute regularization and attribute enhancement.", "questions": "As seen in weakness.", "limitations": "The manuscript includes discussions on limitations and social impacts.", "rating": 5, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The results seem good without overfitting.\n2. Overall the paper is well-written and easy to follow.", "weaknesses": "1. The similarity consistency loss has been utilized in GAN-based methods. It is similar to “Few-shot image generation via cross-domain correspondence”[1].\n2. The comparative experiments for personalization are missing, such as textual inversion.\n3. The concept of multi-attribute transfer is quite similar to hybrid domain adaptation[2]. It needs to include a conceptual comparison in the related work section.\n\n> [1] Ojha U, Li Y, Lu J, et al. Few-shot image generation via cross-domain correspondence[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 10743-10752. \n\n> [2] Li H, Liu Y, Xia L, et al. Few-shot Hybrid Domain Adaptation of Image Generator[C]//The Twelfth International Conference on Learning Representations. 2023.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "### Weakness 1\n\nWith regard to the part of attribute regularization, we have utlized a variant of the similarity consistency loss in CDC [1]. However, our core contribution of this part is not the similarity consistency loss itself, but is the strategy to construct **paired** source/target codes in **latent space** via **few-step recurrent denoising** within the single-timestep training procedure of diffusion models, so that our DomainGallery is open to various choices of regularizations (including but not limited to the similarity consistency loss we apply in our model). Without such strategy, the previous work DomainStudio directly applied regularization on **single-step-denoised, unpaired, and pixel-level** source/target images, which we have discussed and proved to be much less effective. Refer to Line 189--194.\n\n\n### Weakness 2\n\nIn the generation scenario of personalization, the reason we combine DomainGallery and DreamBooth is just to show that it is promising to combine DomainGallery and subject-driven methods to achieve personalization of certain domain and certain subject. In this work, our goal is still to focus on domain-driven image generation and accordingly design DomainGallery, rather than trying to combine DomainGallery and a variety of existing subject-driven methods (including Textual Inversion [2]) to see which performs the best, which is slightly beyond the scope of this work. However, we still would like to thank the reviewer for raising this suggestion. Just as we have mentioned in Line 232, *while we suppose that there may be a more delicate way to equip DomainGallery with subject-driven methods, we would like to leave it for future works*.\n\n\n### Weakness 3\n\nThanks for recommending Hybrid Domain Adaptation (HDA), which is an insightful work. However, after reading it, we are afraid that the connection between HDA and our DomainGallery is not that strong. Here we list some key differences between them.\n\n- **Setting:** HDA focuses on transfering a model to a hybrid domain defined by multiple independent domains, while our work mainly focus on transferring to a certain **single** domain, which is the mainstream setting for domain transfer. (Also we humbly discourage the use of the term *multi-attribute transfer*, since a single domain usually contains multiple attributes as well.) It is also noteworthy that we have already mentioned and discussed many previous works on (single) domain transfer in Sec. 1 and Sec. 2 (e.g. Line 25--30, 64--70).\n- **Model:** Our DomainGallery is based on a pretrained Text-to-image diffusion model (i.e. Stable Diffusion), while HDA is a GAN-based work. Also, the designs of HDA cannot be directly applied to diffusion models either, especially the latent diffusion models adopting single-timestep training procedure, since iteratively denoising latent codes and decoding them into pixel-level images usually brings unacceptable time and space consumption during training periods.\n- **Generation scenario:** Since HDA adopts GAN, which follows a noise-to-image pipeline, it is only capable of generating intra-category images without further control, like the first scenario of the four in our work. On the contrary, our DomainGallery can take the benefits of the text control, and master in the other three generation scenarios (cross-category, extra attributes, personalization).\n\n\n[1] U. Ojha et al., Few-shot Image Generation via Cross-domain Correspondence.\n\n[2] R. Gal et al., An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion.\n\n\nWe will try our best to settle the reviewers' concerns. Please feel free to leave further comments. Thanks!", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "wcRSvu36sT", "reviewer": "Reviewer_N3qa", "summary": "This paper tackles the problem of few-shot image generation, in a scenario where it is domain-driven and defined by several attributes shared among different target data.", "questions": "1. I would like to ask if the authors consider the mixture of style/attributes, in contrast to the multi-subject customization work, using different unique identifiers?\nIndeed, I do see the mixture of [style] and [subject] results in Figure 6, but it is better if you can include the results of mixture-of-style as the supplement.\n\n2. Are visualization results in the experiments obtained via fixed/deterministic sampling and fixed input/noise? In this way, we can compare different methods clearly and fairly.", "limitations": "The authors discussed the limitations and broader impact properly in the appendix C and D. \n\nMeanwhile, I appreciate that the authors acknowledge the cherry-picking and issues of generated images on the Sunglasses dataset, as mentioned in the limitation section.", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The research topic is of general interest in the generative models, and this is a good bridge between subject-driven generation and the domain-driven generation, using few-shot training data.\n\n2. The proposed method/framework, DomainGallery, is shown effective on different generation scenario.\n\n3. The experiment results are somehow sufficient, good-looking, both quantitatively (FID,KID, i-LPIPS) and qualitatively (visualization in different setup).", "weaknesses": "1. The base models covered in this work are limited. In fact, while Dreambooth is one of the exemplar baseline for few-shot text-to-image (T2I) models, there are more latest and advanced T2I models also suitable for this study. I would suggest the authors to cover 1-2 more advanced T2I models to demonstrate that the proposed DomainGallery framework is a generalized method and applicable to different model structure / design.\n\n2. I did not see any discussion / visualization of the possible failure cases. It is better to include the failure examples to show the boundary of the proposed method.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "### Weakness 1\n\nWe are not quite sure whether the phrase *more latest and advanced T2I models* mentioned by the reviewer refers to the newer subject-driven methods after DreamBooth, or newer T2I models such as Stable Diffusion after version 1.4 we used in our work.\n- **If it refers to newer subject-driven methods**, we would like to thank the reviewer for this suggestion, as applying the designs of DomainGallery on newer subject-driven methods is indeed an interesting topic and may bring some extra benefits. However, we are afraid that it is slightly beyond the scope of our work, which focuses on domain-driven generation rather than subject-driven generation. To the best of our knowledge, none of the recent subject-driven methods has recognized and tried to settle the main challenges that our work has discussed and solved, such as prior attributes, attribute entanglement etc. Therefore, we suppose that using newer subject-driven methods as our base models is unlikely to significantly boost the performance on domain-driven generation scenarios. Nevertheless, we are glad to give it a try in future work, as we have mentioned in Line 232 of our paper, *while we suppose that there may be a more delicate way to equip DomainGallery with subject-driven methods, we would like to leave it for future works*.\n- **If it refers to newer T2I models**, as we have mentioned in Line 432, *DomainGallery is probably applicable to newer versions since the attribute-centric techniques proposed in this work are not based on specific structures*. If a newer T2I model (1) can be finetuned by setting identifier words similar to DreamBooth; (2) can be equipped with LoRA; and (3) follows the conventional single-timestep training procedure of ordinary diffusion models, then the idea of DomainGallery is very likely to be applicable to it. Since it takes time to adapt our DomainGallery to other base models, it is regretful that we are not able to present some results during this response and discussion period. However, we are still grateful for this suggestion and we will give it a try in future work.\n\n\n### Weakness 2\n\nActually we have already discussed some possible failure cases, including learning on composite domains (rather than single domain with clear common attributes) and learning on content domains (see Appendix C). It is true that less satisfying samples may also show up sometimes. In the PDF file in the global response (Fig. 3), we have illustrated some uncurated images. Currently, our DomainGallery is indeed not yet a 100% perfect model (just as any other generative model). However, we have already made great progress in settling the challenges mentioned in our paper, such as prior attributes, attribute entanglement, and inadequate strengths of attributes.\n\n\n### Question 1\n\nIn the PDF file in the global response (Fig. 2), we have presented some generated images with mixed styles of CUFS sketches and Van Gogh houses, where we load both LoRA modules separately learned on the two datasets and use the shared identifier [V] via *\"a [V] face\"*. These results have shown that we may generate some images with fused attributes from both domains. However, as also mentioned by Reviewer 2oCE, there are likely conflicting attributes between two domains of styles (e.g. the sketches are colorless while the painting style of Van Gogh has strong color). Hence, mixing multiple styles is sometimes not a well-defined problem. Although it may make some interesting results (such as what we show in Fig. 2), we still suggest handling it with care.\n\n\n### Question 2\n\nThe samples in our work are generated via deterministic DDIM sampler, yet possibly from different input noises. Although it may not be the fairest way for a qualitative comparison, due to the page limitation, we are only able to present just a couple of samples for each setting. Therefore, we may have slightly picked some representative samples that manifest the typical quality of the images in order to show more clearly the drawbacks of the previous methods and how our method solves them.\n\nIn the PDF file in the global response (Fig. 3), we have shown some uncurated qualitative results. From the results we may conclude that, though not yet perfect, our DomainGallery has already made great progress that most of the generated images are better than those produced by previous works w.r.t. both styles and contents.\n\n\nWe will try our best to settle the reviewers' concerns. Please feel free to leave further comments. Thanks!", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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:
### Recap

In this work, we propose DomainGallery, a method for few-shot domain-driven image generation, which analyzes the key issues that previous works failed to settle, and accordingly designs a series of attribute-centric finetuning techniques. With these novel and effective techniques, our DomainGallery greatly outperforms SOTA works in several generation scenarios.

### Global Response to All Reviewers

First we would like to express our sincere gratitude to all the reviewers for spending time and effort reviewing our paper, coming up with insightful comments and suggestions, and helping us make our work better. We especially appreciate that several strengths of our work have been recognized by the reviewers, listed as below.

- Our method is well-motivated, conceptually solid, sound, and reasonable. (Reviewer 2oCE, Ue4P)
- The problem and the task we focus on are sensible, essential, and of general interest in the generative models. (Reviewer 2oCE, Ue4P, N3qa)
- Our work effectively addresses the limitation of existing methods, thus achieving good performance. (Reviewer 2oCE, btQa, N3qa)
- The evaluation of our work encompasses a variety of settings, with numerous experimental results, and has sufficient ablation studies that verify the contribution. (Reviewer 2oCE, N3qa)
- The paper is well-written, well-structed and easy to follow. (Reviewer Ue4P, btQa)

Later on, we will reply to the reviewers' concerns separately. **Please also check on the PDF file below which contains some figures as a part of our response.**

Meta-review: This paper proposes a few-shot domain-driven image generation method which tries to finetune the pretrained Stable Diffusion on few-shot target datasets in an attribute-centric manner. It supports features including prior attribute erasure, attribute disentanglement, attribute regularization and attribute enhancement. The reviewers like this paper due to 1) the studied task is interesting; 2) the method is well-motivated and solid; 3) both qualitative and quantitative results look promising; and 4) the writing is clear and easy to follow. There are some minor concerns on the novelty, missing additional explanations and analysis, etc. But the rebuttal has resolved most of them, and the reviewers are satisfied in the end. The authors should include the reviewers' suggestions in the final version. (If the authors think some additional suggested references are not very related to this paper, feel free to ignore them.)
Decision: Accept (poster)