CSGO: Content-Style Composition in Text-to-Image Generation
Authors: Peng Xing
Haofan Wang
Yanpeng Sun
wangqixun
Baixu
Hao Ai
Jen-Yuan Huang
Zechao Li
Venue: NeurIPS 2025 poster ()
Keywords: image generation
style transfer
Primary area: deep_learning
TL;DR: We present a dataset of 210,000 triplets (content, style, stylized images) and an end - to - end stylization framework tailored for it, ensuring efficient style transfer.

Abstract: The advancement of image style transfer has been fundamentally constrained by the absence of large-scale, high-quality datasets with explicit content-style-stylized supervision. Existing methods predominantly adopt training-free paradigms (e.g., image inversion), which limit controllability and generalization due to the lack of structured triplet data. To bridge this gap, we design a scalable and automated pipeline that constructs and purifies high-fidelity content-style-stylized image triplets. Leveraging this pipeline, we introduce IMAGStyle—the first large-scale dataset of its kind, containing 210K diverse and precisely aligned triplets for style transfer research. Empowered by IMAGStyle, we propose CSGO, a unified, end-to-end trainable framework that decouples content and style representations via independent feature injection. CSGO jointly supports image-driven style transfer, text-driven stylized generation, and text-editing-driven stylized synthesis within a single architecture. Extensive experiments show that CSGO achieves state-of-the-art controllability and fidelity, demonstrating the critical role of structured synthetic data in unlocking robust and generalizable style transfer. Source code: \url{https://github.com/instantX-research/CSGO}

CSGO: Content-Style Composition in Text-to-lmage Generation
Peng Xing 1.2
Haofan Wang² Yampeng Sun'
Qixun Wang
Xu B
Hao Ai
Jen-Yuan Huang
Zechao Li
Nanjjng University of Science and Technology InstantX Team
"Xiaohongshu Inc
"Beihang University Peking University
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haofanwang. a i gma 1. com
Corresponding author
Abstract
The advancement of image style transfer has been fundamentally constrained by the absence of large-scale, high-quality datasets with explicit content-style-stylizee supervision. Existing methods predominantly adopt training-free paradigms (e.g., image inversion), which limit controllability and generalization due to the lack of structured triplet data. To bridge this gap, we design a scalable and automated pipeline that constructs and purifies high-fidelity content-style-styiizee image triplets. Leveraging this pipeline, we introduce IMAGStyle- the first large-scale dataset of its kind, containing 210K diverse and precisely aligned triplets for style transfer research. Empowered by IMAGStyle, we propose CSGO, unified, end- to-end trainable framework that decouples content and style representations via independent feature injection. CSGO jointly supports image- driven style transfer, text-driven stylized generation, and lext-editing-driie stylized synthesis within single architecture. Extensive experiments show that CSGO achieves state-of- the-art controllability and fidelity, demonstrating the critical role of structured synthetic data in unlocking robust and generalizable style transfer. Source code: https: / thub. com intnnntsstttttttm research/Che
Introduction
Recent advancements in diffusion models have significantly improved the field of text-to-image generation [39 [6]. Models such as SD (29) excel at creating visually appealing images based textual prompts. playing a crucial role in personalized content creation [3] 47 37]. Despite
numerous studies on general controllability, image style transfer remains particularly challenging.
Image style transfer aims to generate plausible target image by combining the content of one image with the style of another, ensuring that the target image maintains the original content's semantics while adopting the desired style 120. 9 This process requires fine-grained control over content and style, involving abstract concepts like texture, color, and visual quality, making it a complex and nuanced challenge [7].
significant challenge in style transfer the lack of large-scale stylized dataset, which makes it impossible to train models end-to-end and results in suboptimal style transfer quality for non- end-to-end methods. Existing methods typically rely on training-free structures, such as DDIM inversion 33 or carefully tuned feature injection layers of pre-trained IP-Adapter [48). Methods like Plug-and-Play 40). VCT 4 and the ofate-of-the-ar StyleID (7] employ content image inversion and sometimes style image inversion extract and inject image features into specifically designed layers. However, inverting content and style images significantly increases inference time, and DDIM
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Figure 1: (1) Comparison of the style transfer results of the proposed method with the recent state- of-the-art method StyleID 77. (2) Our CSGO achieves high-quality text-driven stylized synthesis. (3)
Our CSGO achieves high-quality text editing -diive stylized synthesis.
leading to failures, as shown in Figure InstantStyle [41 employs the pre-trained IP-Adapter. However, it struggles with accurate content control. Another class of methods relies on a small amount of data train LoRA and implicitly decouple content and style LoRAs, such as ZipLoRA [35] and B-LoRA [2], which combine style and content LoRAs As to achieve content retention and Style transfer. However, each image requires fine-tuning, and implicit decoupling reduces stability.
inversion can lose critical information 22
To overcome the above challenges, start by constructing style transfer-speeific dataset and then design a simple yet effective framework to validate the beneficial effects of this large-scale dataset on style transfer. Initially, we propose dataset construction pipeline for Content-Style- Stylized Image Triplets (CSSIT), incorporating both a data generation method and an automated cleaning process. Using this pipeline, we construct a large-scale stylized dataset, IMAGStyle, comprising 210K content-style-stylize image triplets. Next, we introduce an end-to-end trained style transfer framework, CSGO. Unlike previous implicit extractions, it explicitly uses independent content and style feature injection modules to achieve high-quality image style transformations. The framework simultaneously accepts style and content images inputs and efficiently fuses content and style features using well-designed feature injection blocks. Benefiting from the decoupled training the
framework, once trained, CSGO realizes any form of arbitrary style transfer without fine-tuning inference stage, including sketch or nature image-driven style transfer, text-driven, text editing -driven stylized synthesis. Finally, we utilize Content Alignment Score (CAS) and CSD (8 score to evaluate the quality of style transfer, effectively measuring the degree of content and style loss post-transfer. Extensive qualitative and quantitative studies validate that our proposed method achieves advanced zero-shot style transfer.
2 Related Work
Style transfer has garnered significant attention and research due to its practical applications in art
creation [14] 36 42 Early methods, both optimization-basse [14] and inference-based based (2 11. are
limited by speed constraints and the diversity of style transfer. The AdaIN approach [18), which separates content and style features from deep features, has become representative method for style transfer, inspiring series of techniques using statistical mean and variance K [5]. Additionally, transformer-based methods such StyleFormer [46] and Tty S improve content bias. However, these methods primarily focus on color or stroke transfer and face limitations in arbitrary style transfer.

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Figure 2: (a) Left: Failure cases in step (1), which fail to maintain the spatial structure of the content image. (b) Right: Samples from our IMAGStyle dataset.
Currently, inversion-based Style Transfer (InST) [50] is proposed to obtain inversion latent of style image and manipulate attention maps to edit generated Images. However, DDIM (Denoising Diffusion Implicit Models) inversion results in content loss and increased inference time [39] Hertz el al.
explore self-attention layers using key and value matrices for style transfer [15]. DEADiff (26) and StyleShot [13] are trained through two-stage style control method. However, it is easy to lose detailed information within the control through sparse lines. InstantStyle [41 42] to achieve high-
quality style control through pre-trained prompt adapter [48 and carefully designed injection layers. However, these methods struggle with achieving high-precision style transfer and face limitations related to content preservation. Some fine-tuning approaches, such as LoRA [17, DB-LoRA (32), Zip-LoRA (35). and B-LoRA [12), enable higher-quality style-controlled generation but require fine-tuning for different styles and face challenges in achieving style transfer. Our proposed method introduces a novel style transfer dataset and develops the CSGO framework, achieving high-quality arbitrary image style transfer without the need for fine-tuning.
3
Data Pipeline
In this section, we first introduce the proposed pipeline for constructing content-style-sttlized image triplets. Then, we describe the constructed IMAGStyle dataset in detail.
3.1 Pipeline for Constructing Image Triplets
The lack of a large-scale open-source dataset of content-stylee-stliize image pairs (CSSIT) in the community seriously hinders the research on style transfer. In this work, we propose a data construction pipeline that automatically constructs and cleans to obtain high-quality content-style- stylized image triplets. given only arbitrary content images and style images. The pipeline contains two steps: (I) stylized image generation and (2) stylized image clcaning.
Stylized image generation. Given arbi- trary content image C and an arbitrary style image S, the goal is to generate stylized im- age T that preserves the content of C' while adopting the style of S. We are inspired by B-LORA 12), which finds that content LoRA
Algorithm 1 Pipeline of Constructing CSSIT
Input: content images Sefcuntent: style images Sotutale
Output: Content-syll-sttliss image triplets Set
1:
for each E Sel,antent do
2:
CLaRA +- Train LoRA for C
and style LoRA can be implicitly separated by SD-trained LoRA, preserving the original im-
3:
Separate content LoRA in CLORA
4:
for each E Setutzle do
age's content and style information, respectively.
5:
SLOBA Train LoRA S
6:
Sitt +- Separate style LoRA in SLURA
Therefore, we first train a large number of Lo- RAs with lots of content and style imges. To ensure that the content of the generated image T aligned to C much as possible, the loRA for C' is trained using only one content image
7:
CSLORA Cambine Cvonient and Syttyle
8:
T (Ti:Ta... T.) Generate images by
CSLaRA
9:
CASL, CAS2, CAS, 4- Compute CAS for each
generated image based on Equ.
10:
i <- Obtain the index of the mimmuum value all CAS Sct.append([[] S, T.D
C. Then, Each trained loRA is decomposed into a content LoRA A and style LoRA through im- plicit separate mentioned by work [1]. Finally, the content LoRA of image C' is combined with
11:
12:
end for
13:
end for
14:
return Set
3

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CSGO
Text Pounge "Loa""
L0O0
F(S)
Cross Amentin
F(S)
Style Projectoo
Image Encoder
ContoolNet
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Imege
Costeat Projection
Crow Atteriion
Block
Choss Allention
Encoder
Noise
Contical Contrel
Base Model
Control
Figure 3: Overview of the proposed end-to-end style transfer framework CSGO.
the style LoRA of to generate the target images 7 = {Ti, 7..... using the base model. How- ever, the implicit separate approach is unstable, resulting the content and style LoRA not reliably retaining content or style information. This manifests itself in the form of the generated image T, which does always agree with the content of C, shown in Figure 2 (left). Therefore, it is necessary to filter 7', sampling the most reasonable T; as the target image.
Stylized image cleaning. Slow methods of cleaning data with human involvement are unacceptable for the construction of large-scale stylized data triplets. To this end, we develop an automatic cleaning method to obtain the ideal and high-quality stylized image T efficiently. First, propose content alignment score (CAS) that effectively measures the content alignment of the generated image with the content image. It is defined the feature distance hetween the content semantic features (without style information) of the generated image and the original content image. It is represented as follows:
CAS, = || |lAda(o(O)) - (TT()
(I)
where CAS; denotes the content alignment score of generated image Tú, o(-) denotes image encoder.
We compare the mainstream feature extractors and the closest to human filtering results is DINO-
V2 21 Ada(F) represents function of feature F to remove style information. We follow
AdaIN 18 to express style information by mean and variance, Ada(F) F-u(P) where ((()
p(F)
and p(F) represent the mean and variance feature F. Obviously, a smaller CAS indicates that the generated image is closer to the content of the original image. In Algorithm I pseudo-code of our pipeline.
we provide
3.2 IMAGStyle Dataset Details
Content Images. To ensure that the content images have clear semantic information and facilitate separating after training, employ the saliency detection datasets, ASRRA10K (3] 6 and MSRA- [19), as the content images. addition, for sketch stylized, we sample 1000 sketch images from Imagehet-Sketch [43] content images. We BLIP [21 to generate caption for each content image. A total of 11,000 content images are trained and used as content LoRA.
Style Images. To ensure the richness of the style diversity, sample 5000 images of different painting styles (history painting, portrait, genre painting, landscape, and still life) from the Wikiart generated 5000 images using Midjourney covering diverse styles, including Classical, Modern, Romantic, Realistic, Surreal, Abstract, Futuristic, Bright, Dark, etc. A total of 10,000 style images are used to train style LoRA.
dataset [33]. In addition,
Dataset. Based on the pipeline described in Section as shown in Figure 2 (right), we construct style transfer dataset, IMAGStyle, which contains 210K content-style-ste styziee image triplets as training dataset. Furthermore, we collect 248 content images from web containing images real scenes, sketched scenes, faces, and style scenes, well as 206 style images of different scenes as testing dataset. For testing, each content image transferred to 206 styles. This dataset will be used for community research style transfer and stylized synthesis.

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Styleln
StyleAignal
Figure 4: Comparison of image-driven style transfer results. Zoomed in for the best viewing.
4 Approach
4.1 CSGO framework
The proposed style transfer model, CSGO, shown in Figure 3 aims to achieve arbitrary stylization of any image without fine-tuning, including sketch and natural image-driven style transfer, text- driven stylized synthesis, and text editing-driven stylized synthesis. Benefiting from the proposed IMAGStyle dataset, the proposed CSGO supports an end-to-end style transfer training paradigm. To ensure effective style transfer and accurate content preservation, we carefully design the content and style control modules. In addition, to reduce the risk that the content image leaks style information or the style image leaks content, the content control and style control modules explicitly decoupled, and the corresponding features are extracted independently. To be more specific, we categorize our CSGO into two main components and describe them in detail.
Table 1: Comparison with recent otate-of-le the-art methods the test dataset.
StyTR² X
Style-Aligned
[
StyleID 7
InstamStyye 41
StyleShot 13
StyleShot-lincart B3
CSGO
CSD (1)
0.2695
0.4274
0,0992
0.3175
0.4522
0.3903
0.5146
CAS (4)
0.9699
1,3930
0.4873
1,3147
1.5105
1.0750
0.8386
AS
4.0387
3.7463
4.7643
5.4824
5.6728
5.2542
5.5467
Content Control. The purpose of content control is to en- sure that the stylized image retains the semantics, layout, and
Table 2: User Preference Score.
other features of the content image. To this end, we carefully designed two ways of content control. First, we implement con- tent control through pre-trained ControlNet [49], whose input is the content image and the corresponding caption. We leverage the capabilities of the specific content-controllable model(Tile ControlNet) to reduce the data requirements and computational costs of training content retention from scratch Following the ControlNet, the output of ControlNet is directly injected into the up-sampling blocks of the base model (pre-trained UNet in SD) to obtain fusion output D = D; + de C.. D; denotes the output of i-th block in the base model, C, denotes the output i-th block in ControlNet, &, represents the fusion weight.
VS
Win
Tic
Loss
StyleShot [3]
58.5
21.4
20.1
InstamiStyle 41
64.2
20.6
15.2
StyleAligned
L5
77.0
12.3
10.7
In addition, to achieve content control in the down- down-samplinn sampling blocks of the base model, we utilize an additional learnable cross atteettinn layer to inject content features into down blocks. Specifically, we use pre-trained CLIP image encoder [27 and learnable projection layer to extract the semantic feature F(CY' of the content image. Then, we utilize an additional artes-attention layer to inject the extracted content features into the down-sampling blocks, i.e., D'c = D + X x De. D denotes the output of in the base model, De denotes the output of content IP-Adapter, A, represents the fusion weight [48). These two content control strategies ensure small content loss during the style transfer.
5

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Figure 5: Comparison of generation results for text-driven stylized synthesis with recent methods.
Style Control. To ensure that the proposed CSGO has strong style control capability, we also design two simple yet effective style control methods. Generally, we feed the style images into pre-trained image encoder to extract embedding F(S) E Roxd and map them to the style embedding F(SY E I xxd through the Perceiver Resampler structure [.] Here, and represent the token number of original and style embeddings. d denotes the dimension of F(S). Then, we utilize an new cross-attention layer to inject the style embedding into the up-sampling blocks of the base model.
Furthermore, note that relying only the injection of the up- up-sampling blocks of the base model weakens the style control since ControlNet injections in the content control may leak style information of the content image C. For this reason, propose use independent cross attention module to simultaneously inject style features into, and the fusion weight is A. as shown in Figure The insight of this is pre-adjust the style of the content image using style features making the output of the Controlnet model retain the content while containing the desired style features.
In summary, the proposed CSGO framework explicitly learns separate feature processing modules that inject style and content features into different locations of the base model, respectively. Despite its simplicity, CSGO achieves
style transfer results.
4.2 Model Training and Inference.
Training. Based on the proposed dataset, IMAGStyle, our CSGO is the first implementation of end-to-end style transfer training. Given a content image C, caption P of the content image, a style image S, and a target image T. we train a style transfer network based on a pre-trained diffusion model. Our training objective is to model the relationship between the styled image T and Gaussian noise under content and style image conditions, which is represented as follows:
L E.s.s.PP.C.S.......... [|c - Eg (Ze, t, C, P)I3
where denotes the random sampled Gaussian noise, Se denotes the trainable parameters of CSGO, I represents the timestep. Note that the latent latent 2, is constructed with a style image T during training, 2t (T) + /1 ãie. where zi(-) mapping the original input the latent space function, a, is consistent with diffusion models [39 [6 We randomly drop content image and style image conditions the training phase enable classifier-free guidance in the inference stage.
Experiments
5.1 Experimental Setup
Setup. For the IMAGstyle dataset, during the training phase, we suggest using 'a [vep]' as prompt for content images and 'a [stp]' as prompt for style images. The rank is set to 64 and each B-loRA trained with 1000 steps. During the generation phase, we sug- gest using 'a [vep] in [stv] style' as the prompt. For the CSGO framework. we employ

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as the base model, pre- pre-trained VIT-H as image encoder, and TTPlaner/TTPLanet SDXL Controlnet Tile Realistic as ControlNet. we uniformly set the images to 512 x 512 resolution. The drop rate of text, content image, and style image is 0.15. The learning rate le-4. During training stage, > = X & 1.0. During inference stage, we suggest X, = X = 1.0 and 8e = 0.5. Our experiments are conducted on 8 NVIDIA H800 GPUs (80GB) with a batch size of 20 per GPU and trained 80000 steps.
Datasets and Evaluation. We use the proposed IMAGStyle as a training dataset and use its testing dataset as an evaluation dataset. It worth noting that the style transfer task, unlike the rest of the style control tasks, requires a trade-off between content retention and style quality at the same time. We use the CSD score [38] as an evaluation metric evaluate the style similarity and Aesthetic Score as a quality evaluation metric. Meanwhile, we employ the content alignment score (CAS) as an evaluation metric to evaluate the content similarity.
Stele image
Santt
houee
Figure 6: Generated results of the proposed Figure 7: The generated results of the proposed CSGO in text editing-driven stylized synthesis.
CSGO in text-driven stylized synthesis.
W Conten Contel
Gas
Careei Irags
Sayle Image
1l monpie C momple
Figure 8: Ablation studies of content control and style Figure 9: Ablation studies of style im- control.
age projection.
Baselines. We compare recent advanced inversion-based StyleII (7]. StyleAligned 15 methods, and StyTR² (8) based on the Transformer structure. In addition, we compare Instantstyle 44 and
StyleShot (and their fine-grained control method StyleShot-linears [13] that introduce ControlNet and IPAdapter structures baselines. For text-driven style control task, we also introduce DEADiff 26 as a baseline.
5.2 Experimental Results
Image-Driven Style Transfer. In Table we demonstrate the CSD scores and CAS of the proposed method with recent advanced methods for the image-driven style transfer task. In terms of style control, our CSGO achieves the highest CSD score, demonstrating that CSGO achieves ot the-art style control. Due to the decoupled style injection approach, the proposed CSGO effectively extracts style features and fuses them with high-quality content features. As illustrated in Figure 4 Our CSGO precisely transfers styles while maintaining the semantics of the content in natural, sketch, face, and art scenes. More results for style control can be found in the supplementary material.
In terms of content retention, it can be observed that StyleID 7 and StyleAligned [15], which are based on inversion, maintain the original content too strongly in sketch style transfer scenarios (CAS is very low). However. they are unable to inject style information since CSD score low. InstantStyle 41 and StyleShot [13] (including Lineart), which use lines to control the content,
7

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Content Image
12
16
32
Style Image
Promot:
helicapter
Promot:
Figure 10: Ablation studies of style token number Left: Image style transfer results. Right: Text-driven stylized synthesis results.
affected by the level of detail of the lines and have different degrees loss of content (such as face scenes). The proposed CSGO directly utilizes all the information of the content image, and content preservation optimal. The quantitative results in Table 4 also show that the proposed CSGO maintains high-quality content retention with precise style transfer. It is worth noting that it is possible to implement a content-style cycle transfer in CSGO (as shown in Figure 12; see the Appendix for more results).
Text-Driven Stylized Synthesis. The proposed method enables text-driven style control, i.e, given a text prompt and a style image, generating images with similar styles. Figure shows the comparison of the generation results the proposed CSGO with the state-of-the-ar methods. In a simple scene, it is intuitive observe that our CSGO obeys textual prompts more. The reason for this is that thanks to the explicit decoupling of content and style features, style images only inject style information without exposing content. In addition, in complex scenes, thanks the well-designed style feature injection block, CSGO enables optimal style control while converting the meaning of text. As illustrated in Figure we demonstrated more results.
Text editing-Driven Stylized Synthesis. The proposed CSGO supports text editing-driven style control. As shown in Figure 7 in the style transfer, we maintain the semantics and layout of the original content images while allowing simple editing of the textual prompts. The above excellent results demonstrate that the proposed CSGO is powerful framework for style control.
User Preference.We We randomly selected 100 sets of results from the test set. Of these, 20 were portraits and 20 were sketches, and the rest were randomized. We then conducted a user study experiment comparing CSGO with Styleshot-lineart, instantStyle, and Stylealigned. Each group contained four generated results, and the user chose the best result. The results are shown in Table ?? where it can be noticed that CSGO gets the highest preference score.
5.3 Ablation Studies.
Content control and style control. We discuss the impact of the two feature injection methods, as shown in Figure aand and Table 3 If content images are injected into the base model only through an additional cross attention layer, only semantic information guaranteed, while the full content information is not preserved (Figure 8 I)). After introducing the ControlNet injection, the quality of content retention improved, as shown in Figure X However, if the style features are injected into base UNet only without ControlNet injection, this weakens the style of the generated images, which can be observed in the comparison of Figure 2) and (3).
Therefore, the proposed CSGO pre-injects style features in the ControlNet branch to further fuse the style features enhance the transfer effect.
Style image projection layer. The style image projection layer can effectively extract style features from the original embedding. We explore the normal linear layer and the Resampler structure, and the experimental results are shown in Figure Using the Resampler structure captures more detailed style features while avoiding content leakage.

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Figure 11: Ablation studies of content scale 8c, CFG, content scale X and style scale Ag.
Content
Style
0.7
8, = 0.5
8 0.3
6,
L
Figure 12: Effects of content scale in style transfer task.
Table 3: Ablation results on the test dataset.
Token number. We explore the effect of the number of token in the style projection layer on the results of style transfer and text-driven style synthesis. The experimental results are shown in Figure g where the style control becomes progressively better as L. increases. This is in line with our expectation that influences the quality of feature extraction. A larger means that the projection layer can extract richer style features.
W/O Content Control
W/O W/O Style Injection
W/O Resampler
CSGO
CSD CAS (
0.5381 1.7723
0.4873 0.8372
0.4926 0.8279
0.5146 0.8386
The impact of content scale d.. As shown in Figure II when d, is small, the content feature injection weak, and CSGO obeys the textual prompts and style more. As d, increases, the quality of content retention becomes superior. However, we notice that when ö_ is large (e.g., 0.9 and 1.2). the style information is severely weakened.
The impact of CFG scale. Classifier-free guidance enhances the capabilities of the text-to- -oxt-to-image model. The proposed CSGO is similarly affected by the strength of CFG scale. As shown in Figure [1] the introduction of CFG enhances the style transfer effect.
The impact of style scale X and content scale X.. The style scale affects the degree of style

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injection. Figure 11 shows that if the style scale is less than 1.0, the style of the generated image is severely weakened. We suggest that the style scale should be between 1.0 and 1.5. Content control in the Nown-sampling blocks utilizes the semantic information of the content image to reinforce the accurate retention of content. Figure TT shows that Å. is most effective when it is near 1.0. In style transfer, the retention of content and style varies from person to person. We can set the hyperparameter content scale d so that the generated result meets the expectation. As shown in Figure 12 different levels of detailed information can be retained by different content scales to meet different design requirements.
Conclusion
We first propose pipeline for the construction of content-sttle-stylized image triplets. Based on this pipeline, we construct the first large-scale style transfer dataset, IMAGStyle, which contains 210K image triplets and covers wide range of style scenarios. To validate the impact of IMAGStyle on style transfer, we propose CSGO, a simple but highly effective end-to-end training style transfer framework, and we verify that the proposed CSGO can simultancously perform image style transfer, text-driven style synthesis, and text editing-driven style synthesis tasks in unified framework. Extensive experiments validate the beneficial effects of IMAGStyle and CSGO for style transfer. We hope that our work will inspire the research community to further explore stylized research.
7 Acknowledgments
This work was supported by National Natural Science Foundation of China (Grant No. 62425603) and Basic Research Program of Jiangsu Province (Grant No. BK2024001I)..
10

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Lowers the threshold for artistic creation, enabling the generation stylized works via simple prompts and promoting era of "creation for all."
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Facilitates the fusion of diverse artistic styles, enhancing the fun of creative expression.
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Appendix: CSGO: Content-Style Composition in Text-to-Image Generation
A Details.
A.1 Preliminaries
Text-to-Image Model. In recent years, diffusion models have garnered significant attention in the text-to-image generation community due to their powerful generative capabilities demonstrated by early works [T0 28 Owing to large-scale training [4,, improved architectures 27 24], and latent space diffusion mechanisms, models like Stable Diffusion have achieved notable success in text-to- image generation. (28]. The focus controllability in text-to-image -moge models has grown in response to practical demands. Popular models such as ControlNet [49]. T2ladapter [23]. and IP-Adapter [48) introduce additional image conditions enhance controllability. These models sophisticated feature extraction methods and integrate these features into well-designed modules to achieve layout control. In this paper, present style transfer framework, CSGO, based image-conditional generation model that can perform zero-shot style transfer.
text encoder and the UNet structure (30] as a latent denoising network. We refer to the pre- pre-trained UNet as the base model. In general, the U-Net contains multiple down-sampling blocks, middle block, and multiple samslipgnng blocks [25). Studies on the controllability of diffusion models usually inject control features into the base model.
Stable Diffusion. The backbone of the stable diffusion model (9 uses CLIP
ControlNet. ControlNet models have been developed for image conditions such as depth map, canny, sketch, which effectively enhance the controllability of text-to-image models. ControlNet A takes images as the control condition, trains zero-convolution layers and replicates encoder (down-sampling blocks and middle block) of the base model, and injects the resulting outputs of each block into the up-sampling blocks and middle block correspondingly. Controlnet output features are directly weighted with base model features.
IP-Aeereer. IP-Atapter 48 implements image prompt features injected into the text-to-image model by decoupling the cross attention module. In general, image prompts are first obtained as image embeddings by pre-trained encoder, and then mapped to the Key matrix and Value matrix of the attention. Then, they interact with the Query matrix of the base model's 's attention layer and weight the outputs with the original outputs. IP-Atetter's simplicity and effectiveness in injecting image conditions has received wide attention from the community.
A.2 Datasets.
Content Images. We employ the saliency detection datasets, MSRAI0K 5 and MSRA-B 19 as the content images. In addition, we sample 1000 sketch images from ImageNet-Sketta content images to sketch-stylized. The category distribution of content images is shown in Figure
43 as
We show the original content image, the style image, and the best result obtained by CAS filtering (target image), and large number original results generated by B-LORA. These cases show that CAS can clean illogical generated graphs for pose, size, and so on. However, we emphasize that since B-LoRA is actually more stable for the generation of styles, it is up to to filter the images with CSD. In our experiments, it is possible to filter using only CAS without CSD.
In addition, we show 10 of CAS filtering examples Figures 17 18 19
20
21
Toss
Trarsporatios
22%
Clothing
Tools
Landscapes
Fummnuu
Tecannial Artrrals
175
8%
Others
Figure 13: Distribution of content images.
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Content Image
Style Image
Results
Figure 14: Failure cases.
Setting. In order to construct IMAGStyle, we trained a total of 11,000 content LoRAs and 10,000 style LoRAs. The.A.TTetttillllly. the combination of these LoRAs can generate 11, 000 10, 000 110M images. However, image generation is very time and resource consuming. Secondly, not any combination of content LoRAs and style LoRAs can get the desired results. Therefore, we generated only 210k data triples in limited time. In the future, we expect to improve the quality of IMAGStyle further.
Inference. During the inference phase, we employ classifier-free guidance. The output of timestep indicated follows:
C0(2) C,S, = C.S. 1o01--)ooooe w)co(z,t))
(3)
where w represents the classifier-fres guidance factor (CFG).
B More Results.
B.1 Failure cases.
As shown in 14gure]4 first, for real portrait stylization, shown in the first row, there is potential loss of facial identily. Portrait images can difficult to collect due to the privacy issues involved, leading to some limitations in CSGO's style migration for real portraits. Second, despite incorporating styles into the ControlNet and base model, CSGO may still leak information, such as the original image's color.
In the future, we aim enhance the CSGO framework in several ways. First, we plan use CSGO in conjunction with LoRA to improve the portrait segment of the IMAGStyle dataset and enhance portrait stylization capabilities. Second, we will redesign and train the content extractor and style encoder to minimize content leakage. Third, we intend to refine the current IPA fusion method and explore more effective approaches style and content fusion. However, we acknowledge that these improvements may not be achievable in the short term.
B.2 The proposed IMAGStyle for traditional style transfer method
To comprehensively evaluate the performance of IMAGStyle, we conducted two experiments and the results Table First, retrained StyTr² using only IMAGStyle. Second, fine-tuned StyTr² using IMAGStyle, leveraging the released model weights pre-trained for 160,000 steps. StyTr² employs non-trivial training approach, wherein the model is implicitly constrained produce results with content closely aligned to the content image and style closely aligned to the style image. The primary advantage of IMAGStyle lies in its <content, style, target> triplet structure. To further enhance the performance of StyTr? we introduced explicit pixel-level constraints by incorporating MSE loss. This addition enforces the generated results to closer the target map within the triplet, thereby improving style transfer fidelity.
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Content image
Generate results of B-LoRA
CAS-2,2403
CAS-2.2182
CAS-1.7679
Style image
CAS-224157
CAS-1.0246
CAS-1.1343
Figure 15: Example of generating result that is filtered by CAS.
Content Image
Style Image
CSGO
B-LoRA
Figure 16: Comparison with LoRA.
23

---

Table 4: Comparison of style transfer metrics across different methods
Metric
StyIr²
Stylr² with IMAGStyle
line-tune Stylt" with IMAGSSyle
CSD
0.2695
0.3597 0,3430
Aesthetics Soore
4.1387
4,5146
4,6975
B.3 More Results.
We show the results of additional supplementary experiments.
Comparison with LoRA. LoRA-based style transfer schemes need to be fine-tuned for different style images. We compared this with the current SOTA style transfer LORA scheme B-LoRA. We trained 3 sets of B-LoRA and the results are shown in Figure [6 It can be seen that CSGO, which does not require re- re-fine-tuning, outperforms the style transfer results of B-LoRA.
Ablation studies on style token. As expected, the number of style tokens influences the quality of style features. As shown Figure 23 as the style token increases, the style transfer quality is better.
Line control and original image control. We show in Figure 24 the results of controlling the content using lines and the original image, respectively. It can be clearly noticed that using lines loses detail information.
Sketch-driven style transfer results. Figures transfer results.
25
26
and
27 show sketch-driven style
Image-driven style transfer results. Figures transfer results.
28
29
30
and
31 shows image-driven style
Content-style cycle transfer results. Figure shows content-style cycle transfer results.
Face image style transfer result. Figure 33 shows face image style transfer results.
Text-driven stylized synthesis results. Figure 34 and Figure 35 show text-driven stylized synthesis results.
Text feittngg-river stylized synthesis results. Figure 36 shows text editing-driven stylized synthesis results.
B.4 Difference with IP-Adapter, StyleAdapter, InstantID, InstantStyle.
As shown in 5 show the differences between CSGO and the above methods in the table below. In particular, IP-Adapter and InstantIl are different from the proposed tasks applicable to CSGO. Compared to StyleAdapter and InstantStyle, CSGO support more diverse style control tasks, more detailed content and style control capabilities, and high-quality ternary style datasets.
Table 5: Method comparison summary
Methods
PP-Alee 48
StyleAdapter
InstantID
InstantStyle 44
CSGO
Task
Content consistency
Text-driven
consistency
Text-driven syothesis
Image-driven transfer, Text-driven synthesis, Text-edi edilerritor lasks
maintensace
synthesis
Training Data
Reconstruction method, Reconstraction method, Reconstraction method, Reconstruction method, IMstytele (triplet)
No puir data
No puir data
No pair data
No pair data
Structural Properties
IP. Ad-Attreer injects features all
PCA features injected via IP-Adapter
Face features through identity net
IPAdapter weights in up, blocks attentions,
Dual branch conten//style control IP-Adapter and ControlNet layer-spocific feature injection.
24

---

Input
Geneeation
Comtant
9966
Trrgst Imags
Content Image
Styk Imags
Torget
Figure 17: Example of data cleansing using CAS.
25

---

Result
Generation LoRA
Content Image
Style Inage
Tragot Imoge
Style Imsage
Tagge finge
Figure 18: Example of data cleansing using CAS.
26

---

Input Resalt
Gemention resils D-LoRA
Contntt Drags hrugs
Style Image
Target fmags
Contion Image
Soyle Image
Target Image
Figure 19: Example of data cleansing using CAS.
27

---

Result
Generation results LoRA
Contert Image
Siye Inage
Target Image
Conkeal Image
Style image
Target Image
Figure 20: Example of data cleansing using CAS.
28

---

Input
BLOA
Content Image
Style
Target Image
Content Image
Style image
Thagss Image
Figure 21: Example of data cleansing using CAS.
Content Image
Style Image
ControlNet
w/o ControlNet
Figure 22: Ablation studies of ControlNet.
29

---

Costrnl Jaage
Stvle
m7?
TRIANEÉ LLATIE
RAIANTA MHARAYIE
TAFANTÀ WIATYNE SAL APPE LAJANTÁ MAALTIE IMAHEA MAALVIE
TAIANTA WILLYDE TNTESTA RAIEE TAIANEÁ HMAAYYE TAARREA ARALYIE
Figure 23: Ablation studies on style token.
Cantenr taage
Smugee
Tildkyer AWAMN!
lik Camelettaooo
Figure 24: Results comparison between using line control content and original image control content.
30

---

Style
Figure 25: Sketch driven style transfer results.
31

---

Style
Content
Figure 26: Sketch-driven style transfer results.
32

---

Style
Content
Figure 27: Sketch-driven style transfer results.
33

---

Style
Content
Figure 28: Image-driven style transfer results.
34

---

Content
Figure 29: Image-driven style transfer results.
35

---

Style
Content
Figure 30: Image-driven style transfer results.
36

---

Style
Content
Figure 31: Image-driven style transfer results.
37

---

Centert
Figure 32: Content-style cycle transfer results.
38

---

Contert
Figure 33: Face image style transfer results.
39

---

mart
dog
Style Image
doc
*s pands
Style
Stale Image
La digs
ponda"
Figure 34: Text-driven stylized synthesis results.
40

---

Style Image
Prottge
Generation Insiges
Style Imagee
Prompi
Genamaiio Imaga
skoiis glow risel encralis
body ling an sverd.
heeer cena bill mecume the shen. perellyyree mechine
pawe, covipridty biddngg weal colie bekiing avvrd
sset tens pieeet jeslly merings sile. open
wabienss lpst bilding dos dents
Fodd de cload
leows lel hiladd grms shis the shy
andk
lockng Hiove, upai body doy peniee Marry tenes ayis callibees ckin
Cheese
mall Juius sai huir, now pnrie dhan balk ecuples Shis penaaall kinee liin cnalaee Mas doy
boyaan thick
bick Hulurna muakerpees callity
Figure 35: Text-driven stylized synthesis results.
Coand
Sn compe
Now prrnac
sing wngerd
Figure 36: Text editing-driven stylized synthesis results.
41


Reviews:
[{"review_id": "kFVmGJdfON", "reviewer": "Reviewer_QqDz", "summary": "The paper addresses a key bottleneck in image style transfer research: the lack of large-scale, supervised datasets.  To solve this, the authors introduce a two-fold contribution. First, they design an automated pipeline to create and purify high-quality \"content-style-stylized\" image triplets. This pipeline is used to build IMAGStyle, a novel dataset comprising 210000 diverse image triplets, the first of its kind for style transfer research. \n\nSecond, leveraging this new dataset, the authors propose CSGO, an end-to-end trainable framework for style transfer. The CSGO model explicitly decouples content and style representations by injecting their features into different parts of a diffusion model. This single, unified framework is capable of performing image-driven style transfer, text-driven stylized generation, and stylized synthesis based on text editing, without needing to be retrained or fine-tuned for new styles. Experiments show that CSGO achieves state-of-the-art results in both style fidelity and content preservation compared to recent methods.", "questions": "Q1. CAS uses feature normalization to isolate content by removing style.  Could you provide further justification or ablation studies on the effectiveness of this approach compared to other potential methods for disentangling content and style?\n\nQ2. Your ablation study shows that injecting style features into the ControlNet branch is beneficial.  Did you observe any trade-offs? Specifically, does this pre-injection of style into a content-focused module ever lead to undesirable content distortions or artifacts?\n\nQ3. It has been mentioned that the model struggles with face stylization.  Could you elaborate on the specific failure modes? Is this primarily an issue with the underlying ControlNet, a lack of sufficient facial diversity in the IMAGStyle dataset, or the feature separation itself?", "limitations": "Please address the weakness points.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "S1. The creation of the IMAGStyle dataset is a major contribution that directly addresses a long-standing challenge in the field, enabling new research avenues in end-to-end trainable style transfer models. The automated pipeline developed for its construction is also a valuable asset.\n\nS2. The CSGO model is powerful due to its versatility. It handles three distinct and important stylization tasks (image-driven, text-driven, and text-editing-driven) within a single architecture, which is a significant advantage over methods that require separate models or fine-tuning for each style. \n\nS3. The paper demonstrates through extensive qualitative and quantitative comparisons that CSGO outperforms a range of contemporary state-of-the-art methods. The model achieves a superior CSD score, indicating excellent style transfer, while maintaining strong content preservation. \n\nW1. The data generation pipeline is built upon the B-LoRA method, which the authors themselves describe as \"unstable\".  Although a data-cleaning step using a so-called CAS score is implemented to filter out poor results, the final dataset's quality is fundamentally dependent on this initial, imperfect generation process.\n\nW2. The CAS score, while is crucial for cleaning the dataset, relies on instance normalization to separate style from content features.  The assumption that this mathematical operation can perfectly and reliably remove \"style\" across all image types is strong and may not always hold true, potentially impacting the quality of the final IMAGStyle dataset.\n\nW3. The paper acknowledges that it does not report error bars or conduct statistical significance tests for its experimental results.  This makes it more difficult to assess the consistency and reliability of the reported performance gains over other methods.", "was_revised": true, "final_justification": "Authors have responsed my comments and provided additional results per request. I really appreciate it.", "rebuttal": "Response to Reviewer QqDz.\n\nWe thank reviewer for the constructive comments. We provide our feedbacks as follows.\n ***\n**Q1.**  CAS uses feature normalization to isolate content by removing style. Could you provide further justification or ablation studies on the effectiveness of this approach compared to other potential methods for disentangling content and style?\n\n **A1:** We verified through preliminary experiments that the proposed CAS has a very high similarity to manual screening. Secondly, we set up a strict screening mechanism and verified the reliability of the screening results. It is hoped that these experiments can alleviate the reviewers' concerns about CAS.\nFirst, to assess whether CAS aligns with human screening results, we randomly curated 300 content-style pairs and generated stylized results using the B-LoRA combination method introduced in our paper. For each of these 300 groups, each group contain  images as candiate image, we applied three distinct screening strategies to select the top-1 and top-2 results: (1) CAS scores computed using the CLIP visual encoder, (2) CAS scores computed using the DINOV2 model, and (3) manual human selection. The alignment rates between these computational methods (CLIP-based CAS and DINOV2-based CAS) and human choices are reported in Tables 1 (The visualization results (10 groups) are shown in Figures 5 to 9 of the supplementary material.).\n\nTable 1. Alignment results between CLIP-based CAS, DINOV2-based CAS and users.\n| Metric              | User A         | User A         | User B         | User B         | User C         | User C         | User D         | User D         |\n|---------------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|----------------|\n|                     | CAS-CLIP       | CAS-DINOV2     | CAS-CLIP       | CAS-DINOV2     | CAS-CLIP       | CAS-DINOV2     | CAS-CLIP       | CAS-DINOV2     |\n| alignment rate(top1)| 68.0%          | 85.3%          | 62.6%          | 84.0%          | 66.0%          | 82.7%          | 61.3%          | 86.7%          |\n| alignment rate(top2)| 86.7%          | 93.7%          | 82.6%          | 90.7%          | 80.7%          | 89.3%          | 82.0%          | 88.7%          | \n \n**Experimental results demonstrate that DINOV2-computed CAS scores achieve an high alignment accuracy.** **Representative visual examples are provided in Figures 9–14.** We analyzed the underlying factors contributing to this high alignment performance. As illustrated in Figure 3 of the supplementary material, we present the generated samples with their corresponding CAS-DINOV2 scores relative to the original content image. These scores exhibit strong consistency (aligning with subjective human assessment). \n\nSecond, the technical details of the dataset screened based on CAS scores: Selecting the sample with the lowest CAS score per group constitutes merely one candidate solution. In practice, we establish a global threshold by aggregating the 25th percentile of minimum CAS scores across all groups. Consequently, for each group, only those containing at least one sample below this threshold are retained; all others are discarded. Furthermore, we implement globally normalized scores as the evaluation metric to maintain cross-dataset consistency. This systematic approach significantly enhances the reliability of our proposed automated dataset construction framework.\n\n***\n**Q2.** Your ablation study shows that injecting style features into the ControlNet branch is beneficial. Did you observe any trade-offs? Specifically, does this pre-injection of style into a content-focused module ever lead to undesirable content distortions or artifacts?\n\n**A2:** In our experiments, injecting style features into the Controlnet branch can further improve the quality of style transfer without generating artifacts.  Since the proposed CSGO framework is trained end-to-end, unlike some training-free methods, it can correct some artifacts and abnormal states during training.\nThe advantages of style feature injection in ControlNet are twofold:\n(1) Ablation studies confirm the efficacy of style feature injection in ControlNet, contingent upon consistent application during both training and inference phases.\n(2)Our pre-injection design preserves content integrity without inducing distortions or artifacts. As empirically validated in Figure 11 and Figure 12, content and style hyperparameters exclusively mediate the preservation fidelity of respective attributes in output images.\n***\n**Q3.** It has been mentioned that the model struggles with face stylization. Could you elaborate on the specific failure modes? Is this primarily an issue with the underlying ControlNet, a lack of sufficient facial diversity in the IMAGStyle dataset, or the feature separation itself?\n \n**A3:** The suboptimal face stylization performance stems from two primary factors:\nFirst, the core limitation resides in training data paucity. As noted in Section 3, content prompts for our large-scale dataset derive from open-source salient object datasets (MSRA10K and MSRA-B). These collections exhibit strong bias toward non-human subjects, with severely insufficient portrait samples—directly causing deficient facial stylization data in our training corpus.\nSecond, inherent instabilities in the B-LoRA module compound this issue. During content-style decoupled LoRA training, B-LoRA fails to consistently preserve critical portrait content—precisely retaining facial feature details constitutes an intrinsically challenging task [1].\nFinally, we clarify that while our method exhibits elevated failure rates in portrait style transfer, it maintains generalization capabilities within this domain—as explicitly demonstrated by the successful cases in Row 2 of Figure 4.\n \n[1]Frenkel, Yarden, et al. \"Implicit style-content separation using b-lora.\" European Conference on Computer Vision. Cham: Springer Nature Switzerland, 2024.\n\n***\n**Q4.** The paper acknowledges that it does not report error bars or conduct statistical significance tests for its experimental results. This makes it more difficult to assess the consistency and reliability of the reported performance gains over other methods.\n\n**A4:** To address reviewer concerns regarding result stability, we compute the final metric as the mean of five independent evaluations with standard error bounds (Table 4). These results confirm that the CSGO model achieves both statistically significant improvements over baselines and consistent performance across trials.\n Table 4.More comprehensive quantitative comparison results between the proposed method and the other methods.\n\n| Method                | StyTR² [8] | Style-Aligned [14] | StyleID [7] | InstantStyle [32] | StyleShot [12] | StyleShot-lineart [12] | CSGO             |\n|-----------------------|------------|--------------------|-------------|-------------------|----------------|------------------------|------------------|\n| **CSD (↑)**           | 0.2695     | 0.4274             | 0.0992      | 0.3175            | 0.4522         | 0.4037 ±0.0174               | 0.5215 ±0.0162  |\n| **CAS (↓)**           | 0.9699     | 1.3930             | 0.4873      | 1.3147            | 1.5105         | 1.0910 ±0.0343                 | 0.8413 ±0.0270  |"}, {"review_id": "vPe4ViHZaQ", "reviewer": "Reviewer_Exzg", "summary": "This paper introduces IMAGStyle, a 210K diverse and precisely aligned triplet dataset for style transfer research. In addition, this paper proposes CSGO, a diffusion-based network that injects independent content and style features to support image-driven style transfer, text-driven stylized generation, and text-editing-driven stylized synthesis within a single architecture. Extensive experiments show that CSGO achieves state-of-the-art controllability and fidelity, demonstrating the critical role of structured synthetic data in unlocking robust and generalizable style transfer.", "questions": "1. Please provide a quantitative study showing how often the content LoRA and style LoRA truly isolate their intended factors.\n\n2. Why not incorporate a perceptual style metric like CSD alongside CAS when choosing the target image?\n\n3. AdaIN should divide by standard deviation, not variance.", "limitations": "Yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. They fill in the large-scale, high-quality data gap with explicit content-style-stylized supervision by IMAGStyle, a 210K triplet dataset.\n\n2. They separate content and style conditioning into two independent injection branches, which jointly support image-driven style transfer, text-driven stylized generation, and text-editing-driven stylized synthesis.\n\n3. They do plenty of ablation studies, including token counts, scaling factors, and branch designs. In addition, they compare with strong training-free baselines on both automatic metrics and human preference tests.\n\nWeaknesses:\n1. The pipeline assumes that each fine-tuned LoRA can be cleanly decomposed into content and style sub-LoRAs without quantitative evidence. \n\n2. During dataset cleaning, the target image is chosen solely by the Content Alignment Score, but no style-alignment metric is applied. Therefore, triplets can retain wrong or mismatched styles.\n\n3. AdaIN normalisation inside CAS divides by feature variance instead of standard deviation, making the score dimensionally inconsistent and casting doubt on every CAS-based claim.\n\n4. The factorisation and filtering are not perfect, thus, style information may leak into the content branch.", "was_revised": true, "final_justification": "1. They added quantitative decoupling study.\n2. They added justification for CAS only screening articulated with alignment table.\n3. Robustness of content and style separation is still uncertain at the instance level; averages do not guarantee absence of leakage.\n4. Dataset construction still lacks a style sanity check.\n5. Evaluation set and metrics are still not enough; no error bars or statistical tests; no perceptual or human study of CSGO.\n\nThe rebuttal does not fully resolve my concerns. Whether the proposed decoupling and dataset truly guarantee clean, scalable content-style separation. Therefore, I maintain my borderline reject rating.", "rebuttal": "Response to Reviewer Exzg\n\nWe thank reviewer for the constructive comments. We provide our feedbacks as follows.\n***\n**Q1.** Please provide a quantitative study showing how often the content LoRA and style LoRA truly isolate their intended factors.\n\n**A1:**  Regarding the risk of B-LoRA. \nOur conclusion is first derived from the original literature [1], which verifies that B-LoRA has good decomposition properties. In addition, to address reviewer concerns regarding evaluation robustness, we implement the following protocol:\n(1) 300 content LoRAs and 300 style LoRAs are combinatorially paired, generating  stylized images per pair (total: 4500 images)\n(2) Three style consistency metrics are quantified:\n    (i) Mean style similarity between outputs and reference style images\n    (ii) Inter-group average of maximum style similarity\n    (iii) Inter-group average of minimum style similarity\n(3) Two established style assessment models provide metric validation:\n    - CSD [1]\n    - OneIG-StyleEncoder [2]\n(Both output normalized scores ∈ [-1,1], where higher values indicate stronger style fidelity)\n\nTable 1．The average style similarity of 300 groups of images, as well as the inter-group averages of the maximum and minimum similarity within each group.\n| Metric               | mean   | max    | min    |\n|----------------------|--------|--------|--------|\n| **CSD↑**             | 0.618  | 0.738  | 0.547  |\n| **OneIG-styleEncoder↑** | 0.679  | 0.815  | 0.611  |\n\nTable 2．The average CAS score of 300 groups of images, as well as the inter-group averages of the maximum and minimum similarity within each group.\n| Metric      | mean | max-mean | min-mean |\n|-------------|------|----------|----------|\n| **CAS ↓**   | 1.67 | 2.43     | 1.13     |\n \nAs shown in Table 1, for unfiltered outputs, both assessors report mean style similarity ≈0.6, confirming that B-LoRA syntheses preserve high style fidelity regardless of content alignment quality.  On the other hand, we also present visualization results. The 10 sets of results shown in Figures 5-9 all indicate that the samples within these sets are relatively close to the original style images. \n\nFinally, we present in Table 2 the average CAS score among these 300 groups, the inter-group average of the maximum CAS score within each group, and the inter-group average of the minimum CAS score within each group. It can be observed that among the  generated results, we are able to obtain images with excellent alignment, so the inter-group average of the minimum CAS score within each group is 1.13. We show the visualization results of the CAS quantitative alignment in Figure 2 of the supplementary materials. It is quite consistent with the subjective perception that if the CAS is less than 1.2 points, the content can be considered relatively aligned. Therefore, from the experimental results of both aspects and the argumentation in the original article, it can be verified that B-LoRA can effectively decouple and combine content and style.\n \n[1] Somepalli, Gowthami, et al. \"Measuring style similarity in diffusion models.\" arXiv preprint arXiv:2404.01292 (2024).\n[2] Chang, Jingjing, et al. \"OneIG-Bench: Omni-dimensional Nuanced Evaluation for Image Generation.\" arXiv preprint arXiv:2506.07977 (2025).\n \n ***\n**Q2.** Why not incorporate a perceptual style metric like CSD alongside CAS when choosing the target image?\n \n**A2:**  The reasons for not using the CSD score for screening are threefold: \n(1) B-LoRA can easily generate results that are close to the original style images. Empirical evidence from Table 1 confirms that there is high style fidelity between the synthesized images and the reference images (with an average similarity > 0.6). (2) Based on the intrinsic properties of B-LoRA decomposition: style features exhibit significantly higher decomposition stability, while content features show substantially greater variance. Therefore, it is more important to prioritize content accuracy when considering screening results. Visual verification results can be found in Figures 4 to 9 of the supplementary materials. (3)The screening results using CAS have the highest alignment with manual screening.\nTo evaluate the alignment between different screening strategies and human selections, we conducted some verification experiments. We introduced a\\*CAS + b\\*(1-CSD) to represent the final score used for screening. We filtered the results using four strategies respectively and calculated the alignment with the manually screened results (top 1), as shown in Table 3. It can be observed that due to the instability in the content LoRA of B-LoRA, the results screened using only CAS are actually the closest to the manual screening results. Therefore, we choose to screen the dataset solely based on the CAS score.\n\nTable 3. The alignment between the screening results of different strategies and manual screening.\n| User   | a    | b    | Alignment Rate | User   | a    | b    | Alignment Rate | User   | a    | b    | Alignment Rate | User   | a    | b    | Alignment Rate |User   | a    | b    | Alignment Rate |\n|--------|------|------|----------------|--------|------|------|----------------|--------|------|------|----------------|--------|------|------|----------------|--------|------|------|----------------\n| **A**  | 1.0  | 0    | 85.3%          | **A**  | 0.75 | 0.25 | 65.3%          | **A**  | 0.5  | 0.5  | 53.3%          | **A**  | 0.25 | 0.75 | 16.7%          | **A**  | 0 | 1.0 | 9.7%          |\n| **B**  | 1.0    | 0  | 84.0%          | **B**  | 0.75 | 0.25 | 59.3%          | **B**  | 0.5  | 0.5  | 50.3%          | **B**  | 0.25 | 0.75  | 18.3%   |**B**  | 0 | 1.0 | 10.7%          |\n| **C**  | 1.0    | 0 | 82.7%          | **C**  | 0.75 | 0.25   | 58.3%          | **C**  | 0.5  | 0.5   | 47.7%          | **C**  | 0.25 | 0.75 | 15.3%  |**C**  | 0 | 1.0 | 13.0%          |\n| **D**  | 1.0    | 0  | 86.7%          | **D**  | 0.75 | 0.25  | 55.3%          | **D**  | 0.5  | 0.5  | 40.0%            | **D**  | 0.25 | 0.75  | 18.3%  |**D**  | 0 | 1.0 | 13.3%          |\n\n \n \n ***\n**Q3.** AdaIN should divide by standard deviation, not variance.\n \n**A3：** We thank the reviewer for highlighting the precise terminology requirement; the manuscript has been corrected to explicitly reference \"standard deviation\" in the computation procedure."}, {"review_id": "bbSQwrjWPH", "reviewer": "Reviewer_nay9", "summary": "This paper was well written with clear structure and easy to understand. The paper firstly proposes a high quality and carefully cleaned dataset with 210k Content-Style-Stylized Image Triplets. Then, the paper proposes a new style transfer framework CSGO, which uses independent content and style feature injection modules to achieve high-quality image style transformations. Finally, a new score matrix named CAS was introduced to measure content loss after content-style transferred.", "questions": "A total of 11,000 content images and 10,000 style images would theoretically yield 110 million potential combinations, yet the experimental output amounts to only 210,000 images. What explains this notable discrepancy between the theoretical combinatorial possibility and the actual production yield?\n\nWhy injecting style features into ControlNet warrants a more detailed analysis.\n\nTable 2 requires a more detailed explanation. Table 2 references appear to be missing.", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\nThe proposed data construction scheme is reasonable, and the effectiveness of the proposed index screening can be seen from the supplementary materials. The open-source 210k Content-Style-Stylized datasets of the article is valuable to the community. \n\nThe designed end-to-end style migration scheme is quite intuitive, and the experimental results are very competitive and good.\n\nThe ablation experiments are reasonably designed, and the visualized experimental results are quite stunning.\n\nThis paper introduces the strengths and limitations of current methods, that is, there are some deficiencies in stylization of portrait scenes, providing directions for future exploration.\n\n\nWeaknesses:\nThere are some details that may require further explanation.\nThe author's expressions are mostly clear and reasonable, but there are some minor formatting issues that need to be addressed.", "was_revised": true, "final_justification": "As a reviewer, I believe the paper demonstrates a high level of quality in dataset construction, method design, and experimental results, and the authors have provided thorough and reasonable responses to the review comments. The authors have clearly addressed the issues and corrected formatting problems, further improving the quality of the paper. Given its technical novelty and depth of experiments, I recommend awarding a score of 5.", "rebuttal": "Response to Reviewer nay9\n\nWe appreciate the reviewer's recognition and affirmation of various aspects of this study; your positive comments have provided significant motivation for us to further improve the research. Regarding the shortcomings you pointed out, we will attach great importance to them and handle them carefully.\n***\n**Q1.** A total of 11,000 content images and 10,000 style images would theoretically yield 110 million potential combinations, yet the experimental output amounts to only 210,000 images. What explains this notable discrepancy between the theoretical combinatorial possibility and the actual production yield?\n \n**A1:** Firstly, the theoretical combinatorial calculation is a brute - force estimate based on the simple multiplication of the number of content and style images. In practice, not all combinations are viable or meaningful. Our experimental design incorporates a screening mechanism to eliminate such unpromising combinations. This screening process significantly reduces the number of combinations that are actually processed and output.\nSecondly, resource constraints play a major role. Generating and processing each image combination demands substantial computational resources, including GPU time, memory, and storage. Given the limited hardware resources available for our experiments, we are unable to exhaustively compute all possible combinations within a limited time. To optimize resource utilization, we prioritize the combinations that are more likely to produce high - quality and relevant results according to our predefined criteria.\n \n \n ***\n**Q2.** Why injecting style features into ControlNet warrants a more detailed analysis.\n \n**A2:** We will elaborate on the reasons for choosing to pre-inject style features into ControlNet from two perspectives:\n(1) Style features require multi-level injection. Observations from InstantStyle experiments show that although style features can be condensed into certain two layers during downsampling, increasing the number of injection layers can significantly improve style similarity. Furthermore, ControlNet is essentially analogous to a part of the basic backbone network, such as the downsampling layers of U-Net. Moreover, for inpainting-oriented ControlNet, the downsampling layers themselves carry content information. Relying solely on feature injection in the upsampling layers may make it difficult to achieve high-quality style transfer. Therefore, injecting style features into the ControlNet branch, consistent with the injection method in InstantStyle, is intuitively reasonable.\n(2) The results of our ablation experiments are shown in the Table 1. The experimental results clearly demonstrate that injecting style features into the ControlNet branch can improve style similarity without causing style leakage or content loss.\n \nTable 1. Ablation studies style control.\n\n | Metric          | w/o pre-inject style | feature |\n |-----------------|----------------------|---------| \n| **CSD ↑**       | 0.5087               | 0.5146  | \n| **CAS ↓**       | 0.8462               | 0.8386  |\n \n ***\n**Q3.** Table 2 requires a more detailed explanation. Table 2 references appear to be missing.There is a typo in line 250.\n \n**A3.** Table 2 presents human evaluation results comparing our method against StyleShot [12], InstantStyle [32], and StyleAligned. We aggregated evaluators' preference distributions across three categories: preference for our method (\"better\"), baseline (\"worse\"), or perceptual equivalence (\"tie\"). Key conclusions emerge: (1) Our approach demonstrates statistically significant superiority in style transfer quality, suggesting the efficacy of large-scale automated dataset construction for this domain; (2) Human preferences strongly correlate with quantitative metrics (CSD: Content Structure Distance, CAS: Content Awareness Score), validating their utility as perceptual consistency indicators. \n\nThank you for the careful review. We will correct any typos."}, {"review_id": "tZ0NnBJPo1", "reviewer": "Reviewer_TeEm", "summary": "The authors propose IMAGStyle, a large-scale dataset of 210K content-style-stylized triplets, created using an automated pipeline to overcome the lack of structured data in style transfer. They further introduce CSGO, a unified framework that decouples content and style for versatile and high-quality style transfer. Experiments show state-of-the-art performance, emphasizing the value of structured data.", "questions": "Please refer to the weaknesses.", "limitations": "N/A", "rating": 5, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "[Strengths]\n* The research topic regarding stylized image generation is important.\n* Experiments validate the effectiveness of the proposed methods.\n\n[Weaknesses]\n* Some typos in the paper. For example, in line 97, loRA should be modified to LoRA. imges should be modifed to images.\n* The current form of Figure 3 is a little bit messy. Maybe the authors should consider re-draw the pipleline to make it more readable.\n* According to Figure 4, I can not find the strength of the proposed method. By contrast, the compared method is better regarding the generation quality, such as instantstyle. \n* Authors mainly explore the data-driven way to achieve the stylized image generation. I am thinking about, if there is a large amount of data, the model architecture could be more simplified. For example, we can simply use the architecture of MLLM, such as Kosmos-G and Bagel to achieve this task. \n* Could the authors explain more about the potential to expand the dataset, such as the possibility to scale the dataset to infinite amount of data. That could be more useful for the community.", "was_revised": true, "final_justification": "All concerns are addressed. Thanks!", "rebuttal": "Response to Reviewer TeEm\n \n We sincerely appreciate the reviewers' recognition of the strengths of our method. Next, we will respond to the reviewers' questions and concerns one by one.\n***\n**Q1.** Some typos in the paper. For example, in line 97, loRA should be modified to LoRA. imges should be modifed to images.\n\n**A1:** Thank you for the careful review. We will correct any typos.\n \n***\n**Q2.** The current form of Figure 3 is a little bit messy. Maybe the authors should consider re-draw the pipleline to make it more readable.\n \n**A2:** We thank reviewers for their valuable suggestion. We will implement the proposed architectural decomposition separating control mechanisms into: (1) content control in downsampling blocks, (2) style control in upsampling blocks, and (3) dual-control fusion. Due to current response constraints, this refinement will be implemented in the camera-ready manuscript.\n \n ***\n**Q3.** According to Figure 4, I can not find the strength of the proposed method. By contrast, the compared method is better regarding the generation quality, such as instantstyle.\n \n**A3:** Thank you for your attention. In Figure 4, the advantages of our method over InstantStyle are as follows: In the first row, InstantStyle has the issue of \"the trees next to the house being lost\"; in the second row, it suffers from the loss of key facial attributes and insufficient style restoration; in the third row, InstantStyle loses background attributes; in the fourth row, its generated results lack the \"stone texture\" possessed by the style image.\nThe reason for these differences is that InstantStyle relies on DDIM inversion based on content images to preserve content features, while DDIM inversion not only tends to lose content information of the original image but also takes almost twice as much time. Moreover, through large-scale training, the effectiveness of our proposed method in style transfer has been further improved.\n \n  ***\n\n**Q4.** Authors mainly explore the data-driven way to achieve the stylized image generation. I am thinking about, if there is a large amount of data, the model architecture could be more simplified. For example, we can simply use the architecture of MLLM, such as Kosmos-G and Bagel to achieve this task.\n \n**A4:** We thank the reviewers for their insightful observations. We concur that integrated generation-understanding architectures (e.g., Bagel, GPT-4o) represent an emerging paradigm where downstream task incorporation can streamline model design. While we acknowledge that sufficient data could theoretically enable Bagel to perform style transfer through full fine-tuning, such adaptation to multi-image inputs would necessitate substantial computational resources—a promising but resource-intensive research direction. Our plugin-based approach offers distinct advantages: it preserves base model capabilities while enabling parameter-efficient adaptation. We are actively exploring unified architectures for stylization and multimodal understanding-to-generation pipelines to bridge this methodological gap.  \n \nIn fact, our CSGO is still a basic and simple pipeline. We mainly verified that the automated large-scale construction of stylized datasets can significantly improve the effect of style transfer. We will actively explore a more elegant framework. \n \n  ***\n\n**Q5.** Could the authors explain more about the potential to expand the dataset, such as the possibility to scale the dataset to infinite amount of data. That could be more useful for the community.\n \n**A5:** \nThe scalability of IMAGStyle is architecturally facilitated through an automated pipeline. By training m content-specific and n style-specific B-LoRAs, we theoretically obtain m × n triplets (content image, style image, stylized output). Each additional B-LoRA linearly extends the combinatorial space by m or n triplets respectively. Crucially, B-LoRA training exhibits high efficiency—requiring merely ≈6 minutes per module on NVIDIA H800 hardware. Thus, given sufficient content/style images and GPU resources, the system can rapidly scale to massive triplet volumes. Furthermore, CSGO serves as a complementary expansion mechanism: rather than training new B-LoRAs, it enables immediate generation of novel triplets through direct application, substantially accelerating dataset growth with minimal marginal cost."}, {"review_id": "xqAf4MydUc", "reviewer": "Reviewer_a2Ei", "summary": "This paper establishes a new dataset, IMAGStyle, for style transfer with a <content, style, target> triplet structure. To obtain large-scale content-style-stylized image triplets, this paper introduces an automatic pipeline with a generation stage and a cleaning stage. This paper also proposes a style transfer approach that uses the IMAGStyle dataset for retrieval training. Experiments are widely conducted and show effectiveness in various style transfer scenarios.", "questions": "1. Although I appreciate the authors' efforts to construct such a large-scale dataset for style transfer, I doubt the necessity and quality of ground-truth stylized images. The task of style transfer is inherently subjective. It is generative and artistic, not reconstructive. On the other hand, the filtering algorithm (i.e., CAS) only relies on the content preservation metric, while the style information is not taken into consideration. This design might cause some content invasion from the style image. As shown in the examples of the first row in Fig. 2, two stylized dog images show the content of style images, i.e., wearing a shirt. This would hinder the disentanglement of content/style during training. This can also be observed in Fig. 4, a dandelion appears in the background of CSGO's result, which is the content of the style image. I guess this is also one reason for the failures of CSGO on portrait stylization (Appendix 2.1). Could the authors explain the reason for not introducing a style metric during data collection?\n\n2. CSGO introduces several hyperparameters to control the scale of injected content or style. I found the results in Fig. 11 lack interpretability, e.g., determining how much to preserve structure or how much to reflect style. For example, the results of $\\lambda_s=0.5,1,1.5$ all bias to the content image (might be because of a large $\\delta_c=0.5$). Also, too much weights complicate this control process.\n\n3. The architecture of CSGO is dedicated, and the ablation study design is not good. Since CSGO injects content features via (1) ControlNet, and (2) learnable cross-attention layer, what does \"W/O Content Control\" refer to? According to Fig. 3 (both in the box of the content control), it seems that two modules are both removed; in this case, the effect of each module is not fully studied. One suggestion to conceptualize these modules: (1) Content control, i.e., cross-attention in the down-sample blocks; (2) Style control,  i.e., cross-attention in the up-sample blocks; (3) Dual/Mixed control, i.e., ControlNet with information from both pixel-level content image and semantic-level style image. This might be clearer and easier for readers to follow, and help to design ablation experiments. \n\n4. Regarding the style stream, I suggest the authors provide more details about the style control, e.g., style token (Perceiver Resampler), and the formulation of $\\lambda_s$ (line 177). Also, it is not clear whether the input of the two cross-attention in style control is the same, i.e., both use F(S)' or two independent projections.\n\n5. Most ablation experiments are visually studied, but lack quantitative results (either learning-based or human-based). Especially these scale parameters, a quantitative comparison may help to demonstrate the style-content trade-off.", "limitations": "The authors have discussed the limitations of CSGO in facial stylization scenarios, but the quality of this paper could be further improved with discussions on other failure cases (e.g., content leakage or content overfitting) and social impact.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths**:\n1. This paper introduces a large-scale style transfer dataset, IMAGStyle, for stylized image retrieval. \n2. A learning-based style transfer approach is proposed by leveraging the IMAGStyle dataset.\n3. Experiments and ablation studies are extensively conducted to evaluate the proposed CSGO method.\n\n**Weaknesses**:\n\n1. The dataset largely relies on a well-established style transfer method B-LoRA and may involve several issues, such as a lack of generalization and adaptability, or content overfitting. \n2. The model is architecturally complex, incorporating multiple specialized modules. It also introduces many hyperparameters that may complicate the generative controlling process. The efficiency of training and inference is not reported.\n3. The experiments mostly focus on visual examples, but having more quantitative and statistically significant experiments could strengthen the paper's contributions further.", "was_revised": true, "final_justification": "The major contribution of this paper is the IMAGStyle dataset, which is constructed based on B-LoRA with CAS score filtering to ensure both style and content quality in the stylized images. This design is supported by the hypothesis and empirical findings that B-LoRA achieves style alignment but lacks content fidelity. The proposed CSGO method leverages this dataset and shows improvements over state-of-the-art methods towards controllable style transfer. While some concerns remain regarding the dataset quality and architectural design, I view the dataset as a positive contribution to the style transfer community and recommend a score of borderline accept.", "rebuttal": "Response to Reviewer a2Ei\n\nThank you for the reviewers' recognition. \n ***\n**Q1-1.** Why not introduce a style metric during data collection?\n\n**A1-1:** To address these concerns, we provide the following clarifications:\n(1) B-LoRA enables efficient and stable style transfer, consistently generating images that closely align with the style characteristics of the original references. This claim is substantiated by the following experimental validation:\n We constructed 300 unique content-style LoRA combinations by pairing 300 content LoRAs with 300 style LoRAs. For each combination, we generated 15 stylized images. Style fidelity was rigorously quantified using three key metrics:\n(a) The mean style similarity between generated images and original style references,\n(b) The average maximum style similarity score per content-style group, and\n(c) The average minimum style similarity score per content-style group.\nTo ensure robustness in style assessment, we employed two distinct style evaluation models (CSD and OneIG-StyleEncoder). Both models output similarity scores normalized to the range [-1, 1], where higher values indicate greater style congruence. As shown in the table below, the results confirm that B-LoRA effectively captures stylistic representations and achieves stable style transfer.\n \n|Metric|mean|max|min|\n|-|-|-|-|\n|**CSD ↑**|0.618|0.738|0.547|\n|**OneIG-styleEncoder ↑**|0.679|0.815|0.611|\n \n(2) The observed instability in B-LoRA stems primarily from content preservation inconsistencies rather than style transfer limitations. This distinction was verified through Content Alignment Score (CAS) analysis between generated images and original content references. We report three key metrics:\n(a) The mean CAS across all generated images,\n(b) The average maximum CAS per content-style group, and\n(c) The average minimum CAS per content-style group.\nThe CAS metric (range: [0, ∞)) employs an inverse scale where lower values indicate superior content preservation. As shown in the table below, CAS values exhibit significant variability, confirming B-LoRA's susceptibility to content degradation.\n\n|Metric|mean|max-mean|min-mean|\n|-|-|-|-|\n|**CAS ↓**|1.67|2.43|1.13|\n \n(3) To further validate the robustness of our dataset, we randomly sampled 1000 triplets and computed the average CAS, CSD, and OneIG-StyleEncoder scores of the selected images. The experimental results confirm that the samples obtained through our selection strategy are both reliable and stable.\n\n|Metric|mean|\n|-|-|\n|**CSD ↑**|0.646|\n|**OneIG-styleEncoder ↑**|0.661|\n|**CAS ↓**|1.11|\n\n（4） The screening results using only CAS are more aligned with the manual selection results. To evaluate the alignment rare (AR) between different screening strategies and human selections, we conducted some verification experiments. We introduced a\\*CAS + b*(1-CSD) to represent the final score used for screening. We filtered the results using four strategies respectively and calculated the alignment with the manually screened results (top 1). It can be observed that due to the instability in the content LoRA of B-LoRA, the results screened using only CAS are actually the closest to the manual screening results. Therefore, we choose to screen the dataset solely based on the CAS score.\n\n|User|a|b|AR|User|a|b|AR|User|a|b|AR|User|a|b|AR|User|a|b|AR|\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-\n|A|1.0|0|85.3%|A|0.75|0.25|65.3%|A|0.5|0.5|53.3%|A|0.25|0.75|16.7%|A|0|1.0|9.7%|\n|B|1.0|0|84.0%|B|0.75|0.25|59.3%|B|0.5|0.5|50.3%|B|0.25|0.75|18.3%|B|0|1.0|10.7%|\n|C|1.0|0|82.7%|C|0.75|0.25|58.3%|C|0.5|0.5|47.7%|C|0.25|0.75|15.3%|C|0|1.0|13.0%|\n|D|1.0|0|86.7%|D|0.75|0.25|55.3%|D|0.5|0.5|40.0%|D|0.25|0.75|18.3%|D|0|1.0|13.3%|\n\n ***\n**Q1-2.** Explanation of the \"Dandelion\" in Figure 4.\n\n **A1-2:** Comparative analysis in Figure 4 demonstrates that our method achieves superior stylistic alignment while maintaining optimal content preservation. We posit that stylistic representation inherently encompasses perceptual subjectivity, where brushstroke techniques, color palettes, and compositional arrangements constitute fundamental stylistic components. Illustratively, in a feline-form painting composed of \"grass\" elements, these botanical features function dually as content carriers and stylization primitives. This ontological duality suggests that strategically incorporating content-distinct elements can enhance style expression in transfer tasks.\n \n\n***\n**Q2.** Concerns about the hyperparameters.\n\n**A2:** Beyond the established Classifier-Free Guidance (CFG) technique commonly employed in diffusion models, the proposed CSGO method introduces two critical hyperparameters: the content scale δ_c and the style scale λs. Analogous to conditioning mechanisms in models such as Adapter or ControlNet, these hyperparameters are designed to enable finer-grained control over generative outcomes during inference.\nAs illustrated in Figure 11 and Figure 12, we demonstrate the impact of all hyperparameters on style transfer results. The presented examples show that these hyperparameters provide stable and controllable influence: for instance, a CFG value of 10 yields optimal performance in both example sets, while a content scale δc of 0.5 also achieves the best results in both cases.\nGiven the inherently subjective nature of style perception, these hyperparameters facilitate adjustable generation of style transfer outputs tailored to specific requirements, thereby accommodating the need for diverse stylistic interpretations. Crucially, these hyperparameters are not unduly complex or difficult to tune; their design aligns with an intuitive understanding of the style transfer task, ensuring both accessibility and robust control. The ease of use has been systematically validated in our experiments.\n\n \n**Table 1. Ablation studies of \\δ_c.**\n\n|Metric|0.3|0.5|0.7|0.9|\n|-|-|-|-|-|\n|**CSD ↑**|0.522|0.515|0.499|0.402|\n|**CAS ↓**|0.964|0.839|0.820|0.797|\n \n**Table 2. Ablation studies of λs.**\n|Metric|0.5|1.0|1.5|\n|-|-|-|-|\n|**CSD ↑**|0.406|0.515|0.554|\n|**CAS ↓**|0.784|0.839|0.883|\n\n***\n \n**Q3.** Results of the new ablation experiments.\n \n**A3:** We thank the reviewers for their insightful suggestions. In response, we refined our ablation study to systematically evaluate three critical components: (1) the content control module (cross-attention layers in downsampling blocks), (2) the style control module (cross-attention layers in upsampling blocks), and (3) the dual-control fusion strategy (the style features are inject to Controlnet ).(4) the dual-control fusion strategy without style feature. Quantitative results validating each module's contribution are presented as follow. Obviously, the hybrid content control module is missing, resulting in weak content feature retention capability. The style features are mainly related to the upsampling style control module and the style injection of the Controlnet branch. In order to balance the migration capabilities of style and content, we finally integrated the above-mentioned modules to achieve the optimal results.\n \n\n|Metric|w/o (1)|w/o (2)|w/o (3)|w/o (4)|CSGO|\n|-|-|-|-|-|-|\n|**CSD ↑**|0.512|0.431|0.546|0.509|0.515|\n|**CAS ↓**|0.852|0.847|1.578|0.846|0.839|\n \n***\n**Q4.** Please provide more details about the style token (Perceiver Resampler), as well as the formulation of (line 177) and F(S)' in style control.\n \n**A4:** (1) The style token refers to the number of tokens obtained after mapping the original style features using a resampler structure. A larger number of tokens means that more style features are retained. \n（2）Line 177 mainly describes that we not only inject style features into the upsampling layer, but also inject them into the Controlnet control module through an independent cross-attention module.\n（3） Yes, we use the same F(S)', but with two independent A.\n \n*** \n**Q5.** A quantitative comparison may help to demonstrate the style-content trade-off.\n \n**A5:** We thank the reviewers for their constructive feedback. In addition to the previously reported quantitative ablation studies on content- and style-related hyperparameters , we have supplemented the following:\n(1) Quantitative results analyzing the impact of style token t,\n\n|t|4|12|16|32|\n|-|-|-|-|-|\n|**CSD ↑**|0.4828|0.5046|0.5017|0.5146|\n|**CAS ↓**|0.8275|0.8312|0.8428|0.8386|\n\n(2) Performance metrics for text-driven stylized synthesis tasks\n\n|method|InstantStyle|StyleShot|StyleAligned|DEADiff|CSGO(ours)|\n|-|-|-|-|-|-|\n|**CSD ↑**|0.613|0.632|0.497|0.485|**0.648**|\n|**CLIP-Score ↑**|0.295|0.292|0.289|0.287|**0.309**|\n\n\nCollectively, these comprehensive quantitative analyses—supported by our visualization results—demonstrate CSGO's robustness in style transfer applications. We believe this expanded evidence addresses the reviewers' methodological concerns regarding the approach's validity.\n \n\n\n\n***\n**Q6.** inference time and training efficiency.\n\n**A6:** Regarding the training efficiency of B-LoRA: On a single H800, it only takes 6 minutes to train a B-LoRA. Regarding the inference efficiency of CSGO: On a single H800, without any optimization measures, it takes 4.5 seconds at 512 resolution and 12 seconds at 1024 resolution to generate results. In fact, the inference time of CSGO is significantly lower than that of other style transfer models. Most other stylization methods rely on DDIM inversion, which almost requires doubling the time for DDIM inversion.\n \n ***\n **Q7.** The authors have discussed the limitations of other failure cases and social impact.\n\n**A7:** Thank you for the reviewers' suggestions. In CSGO, due to the introduction of Controlnet, the content signal is very strong, and unreasonable inference hyperparameters may lead to excessive content, that is, style loss; setting the content control hyperparameter to 0.5 will help. Our research is the first to verify that large-scale synthetic data can improve style transfer, providing a new baseline, which will be helpful to the community."}]

Author rebuttal:

Meta-review: This paper makes a clear and timely contribution by introducing the large-scale IMAGStyle dataset and the unified CSGO framework for content–style disentanglement in text-to-image generation. Reviewers agree that the dataset fills a long-standing gap and that the model demonstrates versatility and strong performance across multiple stylization tasks. While there were questions during the discussion regarding the stability of B-LoRA and the reliance on CAS for dataset cleaning, the authors provided additional quantitative evidence, ablation studies, and clarifications that addressed many of these points. Final ratings settled at two accepts, two borderline accepts, and one borderline reject, reflecting an overall positive reception. Considering its clear contributions and likely impact on the community, I recommend acceptance.
Decision: Accept (poster)