Title: CSGO: Content-Style Composition in Text-to-Image Generation
Venue: NeurIPS 2025
Accepted: yes
Keywords: image generation

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}


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-26"}

comments: 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.

STRENGTHS AND WEAKNESSES:
[Strengths]
* The research topic regarding stylized image generation is important.
* Experiments validate the effectiveness of the proposed methods.

[Weaknesses]
* Some typos in the paper. For example, in line 97, loRA should be modified to LoRA. imges should be modifed to images.
* 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.
* 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. 
* 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. 
* 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.

QUESTIONS:
Please refer to the weaknesses.

LIMITATIONS:
N/A
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nA 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nS1. 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.\n\nQUESTIONS:\nQ1. 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?\n\nLIMITATIONS:\nPlease address the weakness points.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}