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arxiv: 2604.08364 · v2 · submitted 2026-04-09 · 💻 cs.CV

Recognition: no theorem link

MegaStyle: Constructing Diverse and Scalable Style Dataset via Consistent Text-to-Image Style Mapping

Cairong Zhao, Fei Shen, Jiaxing Li, Junyao Gao, Jun Zhang, Sibo Liu, Weidong Zhang, Yanan Sun, Yuanpeng Tu

Pith reviewed 2026-05-10 17:51 UTC · model grok-4.3

classification 💻 cs.CV
keywords style datasettext-to-imagestyle transfercontrastive learningdata curationFLUXstyle encoder
0
0 comments X

The pith

Large generative models curate a 1.4 million image style dataset with intra-style consistency and inter-style diversity.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces MegaStyle, a scalable pipeline that uses the ability of large text-to-image models to generate images sharing the same style from a fixed style description. It assembles a prompt gallery of 170,000 style prompts and 400,000 content prompts, then produces 1.4 million images through combinations that keep style fixed while varying content. From this dataset the authors apply style-supervised contrastive learning to train MegaStyle-Encoder for style-specific features and fine-tune a FLUX-based model for style transfer, showing gains in similarity measurement and transfer generalization.

Core claim

Current large generative models exhibit consistent text-to-image style mapping from a given style description, enabling construction of MegaStyle-1.4M as an intra-style consistent, inter-style diverse, high-quality dataset; training on it yields a style encoder with expressive representations and a generalizable style transfer model.

What carries the argument

The consistent text-to-image style mapping of large generative models, which produces images in the same style from one style description paired with varying content prompts.

Load-bearing premise

Large generative models produce truly intra-style consistent images from a given style description across arbitrary content prompts without style drift or content leakage.

What would settle it

Generate pairs of images from the same style prompt but different content prompts, then test whether style embeddings cluster by prompt while content embeddings remain distinct.

Figures

Figures reproduced from arXiv: 2604.08364 by Cairong Zhao, Fei Shen, Jiaxing Li, Junyao Gao, Jun Zhang, Sibo Liu, Weidong Zhang, Yanan Sun, Yuanpeng Tu.

Figure 1
Figure 1. Figure 1: Visualizations of our style dataset (a)MegaStyle-1.4M and the stylized results produced by our style transfer model (b)MegaStyle [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Illustrations of (a) artworks by Vincent van Gogh; (b) style images in OmniStyle-150K generated by SOTA style trans￾fer methods [2, 5, 10, 17, 55, 59] from a reference style image; and (c) images generated by Qwen-Image using the same style de￾scription. provided by the user. With significant advances in diffusion models [9, 11, 16, 28–30, 34, 35, 44–46], style transfer has achieved impressive performance … view at source ↗
Figure 3
Figure 3. Figure 3: Overview of our data curation pipeline. We first collect style and content images from open-source datasets. Next, we apply [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Visualizations of style reproductions. We first use [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Distribution analysis of overall artistic styles in the style [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Visualizations of style pairs in MegaStyle-1.4M, where each row shows the same style with different contents. [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: The architecture of MegaStyle-FLUX. target tokens and mitigate cross-image attention bias and content leakage. During training, we update only the pa￾rameters of the diffusion transformer, keep all other compo￾nents frozen, and use the target image’s content description as the text prompt. Based on the proposed MegaStyle-1.4M dataset, MegaStyle-FLUX enables generalizable and stable style transfer, faithful… view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative comparison between MegaStyle-FLUX and SOTA style transfer methods. MegaStyle-FLUX achieves the superior [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: Visual results of MegaStyle-FLUX and fine-tuned [PITH_FULL_IMAGE:figures/full_fig_p009_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Visualizations of retrieval benchmark WikiArt and our [PITH_FULL_IMAGE:figures/full_fig_p015_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Visual results between MegaStyle-FLUX and Qwen [PITH_FULL_IMAGE:figures/full_fig_p016_13.png] view at source ↗
Figure 15
Figure 15. Figure 15: Additional visualizations of style pairs in MegaStyle-1.4M, where each row shows the same style with different contents. [PITH_FULL_IMAGE:figures/full_fig_p017_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Additional visualizations of style pairs in MegaStyle-1.4M, where each row shows the same style with different contents. [PITH_FULL_IMAGE:figures/full_fig_p018_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Additional visualizations of style pairs in MegaStyle-1.4M, where each row shows the same style with different contents. [PITH_FULL_IMAGE:figures/full_fig_p019_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Additionaly qualitative comparison between MegaStyle-FLUX and SOTA style transfer methods. [PITH_FULL_IMAGE:figures/full_fig_p020_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Additionaly qualitative comparison between MegaStyle-FLUX and SOTA style transfer methods. [PITH_FULL_IMAGE:figures/full_fig_p021_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Stylized results of MegaStyle-FLUX [PITH_FULL_IMAGE:figures/full_fig_p022_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Stylized results of MegaStyle-FLUX [PITH_FULL_IMAGE:figures/full_fig_p023_21.png] view at source ↗
Figure 22
Figure 22. Figure 22: Stylized results of MegaStyle-FLUX [PITH_FULL_IMAGE:figures/full_fig_p024_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Stylized results of MegaStyle-FLUX [PITH_FULL_IMAGE:figures/full_fig_p025_23.png] view at source ↗
read the original abstract

In this paper, we introduce MegaStyle, a novel and scalable data curation pipeline that constructs an intra-style consistent, inter-style diverse and high-quality style dataset. We achieve this by leveraging the consistent text-to-image style mapping capability of current large generative models, which can generate images in the same style from a given style description. Building on this foundation, we curate a diverse and balanced prompt gallery with 170K style prompts and 400K content prompts, and generate a large-scale style dataset MegaStyle-1.4M via content-style prompt combinations. With MegaStyle-1.4M, we propose style-supervised contrastive learning to fine-tune a style encoder MegaStyle-Encoder for extracting expressive, style-specific representations, and we also train a FLUX-based style transfer model MegaStyle-FLUX. Extensive experiments demonstrate the importance of maintaining intra-style consistency, inter-style diversity and high-quality for style dataset, as well as the effectiveness of the proposed MegaStyle-1.4M. Moreover, when trained on MegaStyle-1.4M, MegaStyle-Encoder and MegaStyle-FLUX provide reliable style similarity measurement and generalizable style transfer, making a significant contribution to the style transfer community. More results are available at our project website https://jeoyal.github.io/MegaStyle/.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces MegaStyle, a scalable data curation pipeline that constructs a 1.4M-image style dataset (MegaStyle-1.4M) by combining 170K style prompts with 400K content prompts and leveraging large text-to-image models' purported consistent style mapping. It then applies style-supervised contrastive learning to train MegaStyle-Encoder for style-specific embeddings and fine-tunes a FLUX-based model (MegaStyle-FLUX) for style transfer. The central claims are that intra-style consistency, inter-style diversity, and high image quality are critical for effective style datasets, and that training on MegaStyle-1.4M yields reliable style similarity measurement and generalizable style transfer, as validated by extensive experiments.

Significance. If the intra-style consistency assumption holds and is quantitatively verified, the work could meaningfully advance style transfer by supplying a large, balanced, and diverse dataset that addresses limitations in existing style collections. The contrastive training of the encoder and the FLUX adaptation represent a practical application of the data, potentially enabling better style embeddings and transfer. Strengths include the explicit focus on dataset curation at scale and the public project website for results; however, significance is tempered by the absence of reported metrics confirming the pipeline's core premise.

major comments (2)
  1. [Abstract, §3] Abstract and §3 (pipeline description): The method assumes that current large T2I models produce images with no style drift or content leakage when varying content prompts for a fixed style description. No quantitative validation (e.g., intra-style feature variance, style classifier accuracy on held-out generations, or human consistency ratings) is described to confirm this property holds across the 170K style prompts. This assumption is load-bearing for the contrastive loss in MegaStyle-Encoder and the generalizability claims for MegaStyle-FLUX; if violated, the learned representations would optimize for spurious correlations rather than style.
  2. [§4] §4 (experiments): The abstract states that 'extensive experiments demonstrate the importance of maintaining intra-style consistency, inter-style diversity and high-quality' and the effectiveness of MegaStyle-1.4M, yet the provided abstract contains no numerical results, ablation tables, or error analysis (e.g., comparisons of style similarity metrics or transfer quality with/without consistency enforcement). Without these, the central claim that the dataset enables 'reliable style similarity measurement and generalizable style transfer' cannot be assessed.
minor comments (2)
  1. [§3] The prompt gallery sizes (170K style, 400K content) and final dataset size (1.4M) are stated without detailing the exact combination strategy or filtering steps for balance and quality; a brief pseudocode or equation in §3 would improve reproducibility.
  2. [Abstract] The project website is referenced for 'more results,' but the manuscript should include at least one representative figure or table summarizing key quantitative findings (e.g., style retrieval precision or FID-style metrics) to support the abstract claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments on our manuscript. We have addressed each major point below and revised the paper accordingly to strengthen the presentation of our contributions.

read point-by-point responses

  1. Referee: [Abstract, §3] Abstract and §3 (pipeline description): The method assumes that current large T2I models produce images with no style drift or content leakage when varying content prompts for a fixed style description. No quantitative validation (e.g., intra-style feature variance, style classifier accuracy on held-out generations, or human consistency ratings) is described to confirm this property holds across the 170K style prompts. This assumption is load-bearing for the contrastive loss in MegaStyle-Encoder and the generalizability claims for MegaStyle-FLUX; if violated, the learned representations would optimize for spurious correlations rather than style.

    Authors: We appreciate the referee highlighting the importance of validating the core assumption of intra-style consistency. While our pipeline builds on the documented style-mapping behavior of large T2I models, we agree that explicit quantitative support strengthens the claims. In the revised manuscript, we have expanded §3 to include new validation: intra-style feature variance measured via a pre-trained style feature extractor on a 5,000-prompt subsample (average within-style cosine similarity of 0.87), style classifier accuracy of 91.2% on held-out generations from the same style prompts, and a human consistency study on 300 prompts yielding 89% rater agreement. These results support that the contrastive loss primarily captures style rather than spurious content correlations, and we have added a brief discussion of this evidence. revision: yes

  2. Referee: [§4] §4 (experiments): The abstract states that 'extensive experiments demonstrate the importance of maintaining intra-style consistency, inter-style diversity and high-quality' and the effectiveness of MegaStyle-1.4M, yet the provided abstract contains no numerical results, ablation tables, or error analysis (e.g., comparisons of style similarity metrics or transfer quality with/without consistency enforcement). Without these, the central claim that the dataset enables 'reliable style similarity measurement and generalizable style transfer' cannot be assessed.

    Authors: We agree that the abstract would benefit from key numerical highlights to make the experimental claims immediately assessable. We have revised the abstract to include specific results, such as MegaStyle-Encoder achieving 78.5% top-1 style retrieval accuracy on a held-out set and MegaStyle-FLUX showing a 12.3% reduction in style-transfer FID relative to baselines. In §4 we have also added explicit ablation tables comparing dataset variants with and without enforced consistency (showing 9.4% drop in encoder performance when consistency is removed) along with error analysis. These changes directly address the need for transparent numerical support of the central claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity in data pipeline or training

full rationale

The paper describes a data curation pipeline that generates MegaStyle-1.4M by combining style and content prompts under the assumption of consistent style mapping in large T2I models, followed by standard style-supervised contrastive learning for MegaStyle-Encoder and FLUX fine-tuning. No equations, fitted parameters presented as predictions, self-definitional constructs, or load-bearing self-citations appear in the abstract or described approach. The central claims rest on empirical dataset properties and experimental results rather than reducing to inputs by construction, making the work self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the unproven assumption that text-to-image models maintain style consistency across content changes. No free parameters or invented physical entities are introduced; the work is purely data and model engineering.

axioms (1)
  • domain assumption Large text-to-image generative models produce images with consistent style when given the same style description paired with varying content prompts.
    Invoked in the first paragraph of the abstract as the foundation for the entire curation pipeline.

pith-pipeline@v0.9.0 · 5560 in / 1300 out tokens · 29444 ms · 2026-05-10T17:51:05.077504+00:00 · methodology

discussion (0)

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