Pith. sign in

REVIEW 3 major objections 5 minor 63 references

AnyStyle: A Single LoRA is Sufficient for Image-Guided Style Transfer

T0 review · 3 major / 5 minor · reviewed 2026-07-11 · grok-4.5

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read A single style LoRA plus query attention from the content image is enough for controllable image-guided style transfer.

desk verdict Clean engineering simplification: single style LoRA plus query modulation works and is easier to control than dual-LoRA fusion, but the dual baseline is under-specified and the eval set is small. read the letter →

arxiv 2607.04677 v1 pith:KO2UNTIJ submitted 2026-07-06 cs.CV

Yongwen Lai , Chaoqun Wang This is my paper
classification cs.CV
keywords styletransferLoRArectifiedflowattentionmodulationcontentpreservationdiffusionmodelssingleadapter
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper argues that dual-LoRA style transfer is unnecessary and often harmful. Independently trained content and style adapters fight each other when fused, making the content–style trade-off hard to control. The authors show that a training-free signal—queries recorded while the content image itself is denoised—already preserves structure better than a dedicated content LoRA. They therefore keep only one LoRA, trained on the later transformer blocks that carry style, and inject the content queries into the generation path with a simple time-dependent strength. The resulting AnyStyle pipeline produces stylized images that keep the original layout while matching the reference style, without the entanglement and coefficient tuning that dual adapters require.

What carries the argument

Attention modulation that replaces the generation query Qt with the linear blend β·Qc + (1−β)·Qt, where Qc is the query taken from an auxiliary denoising of the content image and β falls linearly from βmax to βmin across timesteps.

What would settle it

On a held-out set of content–style pairs, disable the query blend and keep only the style LoRA; if structure collapses or the dual-LoRA baseline suddenly matches AnyStyle on both content and style metrics, the claim that the single-LoRA-plus-query design is sufficient fails.

Watch

Extended reading notes

Core claim

A single LoRA placed only on the later single-stream blocks of a rectified-flow DiT, combined with training-free structural guidance obtained by blending content-image queries into the generation path, is sufficient for image-guided style transfer and yields better controllability and perceptual quality than dual-LoRA fusion.

Load-bearing premise

That the model’s later blocks can be treated as style-only and that blending only the content queries (never keys or values) will lock layout without leaking the content image’s own appearance.

Editorial extensions

If this is right

  • Content-specific LoRAs can be dropped from style-transfer pipelines, cutting training cost and removing fusion coefficients.
  • The same query-blend schedule can be reused across different rectified-flow or diffusion backbones without retraining a content adapter.
  • Users gain a single continuous knob (the β schedule) that moves outputs smoothly from content-preserving to style-dominant.
  • Style LoRAs trained this way remain reusable across arbitrary new content images at inference time.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same content-query injection could serve as a lightweight alternative to ControlNet-style spatial adapters for other layout-preserving edits.
  • If early blocks really encode structure, freezing them entirely (not just omitting a content LoRA) may further stabilize results on rare object categories.
  • The linear β schedule is a convenient default; a content-adaptive schedule that inspects noise level or attention entropy might tighten the content–style frontier still more.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes AnyStyle for image-guided style transfer on FLUX (rectified-flow DiT). It argues that dual-LoRA fusion of independently trained content and style adapters is hard to balance (Fig. 2) and that a dedicated content LoRA is inferior to training-free structural guidance extracted from the content image’s own self-attention (Table 1, Fig. 3). The method therefore fine-tunes only a single style LoRA on later single-stream blocks (11–38) via DreamBooth, and at inference modulates only the query tensors of the main path with content queries under a linear β schedule (Eqs. 6–7, Algorithm 1/2). Experiments on 400 content–style pairs report competitive CLIP/DINO/DreamSim scores (Table 2), ablations favoring query-only modulation and the style LoRA (Tables 3–4), a 70-person user study preference, and supplementary comparisons on SDXL and classical baselines.

Significance. If the central claim holds—that a single style LoRA plus query-only attention modulation is sufficient and more controllable than dual-adapter fusion—the work simplifies the dominant LoRA-based style-transfer pipeline, removes per-content fine-tuning, and supplies a practical, training-free content-preservation mechanism that generalizes across FLUX and SDXL. Strengths include a clear empirical motivation (reconstruction comparison), explicit ablations of the modulation design, a user study, released code, and supplementary efficiency and backbone-transfer results. The contribution is primarily engineering and empirical rather than theoretical, but it is of practical interest to the style-transfer and personalization communities.

major comments (3)
  1. §5.2 and Table 2: the head-to-head “Dual-LoRA” baseline that underpins the title claim is under-specified. The text only says “a dual-LoRA approach implemented on FLUX”; it does not state which blocks received the content LoRA, the rank/learning-rate/steps used for that adapter, or how fusion coefficients were chosen (the very coefficients Fig. 2 shows are difficult). Because the paper’s own reconstruction experiment already shows a weak content LoRA (Table 1), an under-tuned dual baseline would inflate the apparent advantage of the single-LoRA design. A fully specified dual-LoRA (or B-LoRA/UnZipLoRA re-implemented on the same FLUX backbone, ideally also with the same attention guidance) is needed before the “single LoRA is sufficient / superior” claim can be considered established.
  2. §4.1–4.3 and the weakest-assumption ledger: the design rests on the claim that FLUX single-stream blocks cleanly specialize (early = structure, later = style) so that placing the style LoRA only on blocks 11–38 and modulating only queries (Eq. 6) with the fixed linear β schedule (Eq. 7, β_max=0.7 / β_min=0.3) reliably preserves content without style leakage. The reconstruction comparison (Table 1) and the query/key ablation (Table 4, Fig. 10) support this on the curated set, but the paper provides no systematic stress test on highly structured or out-of-distribution content (beyond the qualitative failure cases in Fig. 11). Without such evidence, or at least a sensitivity analysis of the layer range and β schedule, the generality of the “single LoRA is sufficient” claim remains incompletely supported.
  3. §5.1 Datasets / Table 2: evaluation uses only 40 curated images (400 pairs) drawn from B-LoRA, StyleDrop and EditEval, with no error bars, statistical significance tests, or larger public benchmarks. StyleID still leads pure content metrics (DS, DINO-Content). While the user study and style/prompt metrics favor AnyStyle, the limited scale and lack of variance estimates make it hard to judge how robust the reported ranking is. Expanding the test set or reporting confidence intervals would strengthen the quantitative claims.
minor comments (5)
  1. Algorithm 1 (main text) and Algorithm 2 (supplement) are nearly identical; the main-text version should be the definitive one and the supplement should only expand variable definitions.
  2. §4.3: the statement that “systematically decreasing β values from 1 to 0 results in a smooth transition” is illustrated only in the supplement; a short reference or a small main-text panel would help readers.
  3. Fig. 2 caption and surrounding text: the dual-LoRA fusion coefficients are never numerically specified; stating the range used would make the figure more informative.
  4. Typographical / formatting: “Inmorerecentadvances” (§2.1), “T able 1”, and occasional missing spaces around citations should be cleaned.
  5. Supplementary Table 3 reports inference times; adding the corresponding training-time comparison (content LoRA vs. none) would reinforce the efficiency argument made in §4.1.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: empirical CV methods paper whose claims rest on external metrics and ablations, not on self-referential definitions or fitted-as-prediction loops.

full rationale

AnyStyle is a standard empirical style-transfer pipeline. Its central claim (single style LoRA on later single-stream blocks + query-only attention modulation with a linear β schedule) is justified by reconstruction experiments (Tab. 1 / Fig. 3), quantitative tables against external baselines (Tab. 2–4), qualitative figures, and a user study. All reported scores use independent external metrics (CLIP, DINO, DreamSim, LPIPS, PSNR, MSE). The free parameters β_max/β_min are explicitly ablated (Fig. 9) and never presented as derived predictions. There are no uniqueness theorems, no self-citation chains that load-bear the result, no ansatz smuggled via prior author work, and no equation that reduces a claimed output to an input by construction. The dual-LoRA baseline may be under-specified, but that is a fairness/correctness concern, not circularity. The derivation chain is therefore self-contained against external benchmarks.

Assumptions & free parameters 4 free parameters · 3 assumptions · 1 invented entities

The central claim rests on a small set of architectural observations about FLUX, a handful of hand-chosen hyperparameters, and the design decision that query-only modulation is sufficient. No new physical entities are postulated; the method is an engineering composition of existing components.

free parameters (4)
  • β_max / β_min = 0.7 / 0.3
    Linear schedule endpoints that set content-style trade-off; values 0.7/0.3 chosen for reported tables and shown to be sensitive in the β sweep.
  • style LoRA layer range = single-stream 11–38
    Blocks 11–38 selected from authors’ layer-specialization observation; not exhaustively validated.
  • LoRA rank = 64
    Capacity of the style adapter; standard but free choice.
  • DreamBooth steps / learning rate = 200 steps / 5e-5
    Fine-tuning schedule on a single style image.
assumptions (3)
  • domain assumption Early single-stream blocks of FLUX primarily encode low-level structure while later blocks encode higher-level style and semantics.
    Stated as experimental observation in §4.1 and used to justify restricting the style LoRA to blocks 11–38 and discarding a content LoRA.
  • ad hoc to paper Modulating only query tensors (leaving keys and values untouched) injects structural guidance without stylistic leakage from the content image.
    Design principle justified by the ablation in Tab. 4 / Fig. 10; treated as sufficient for the method.
  • domain assumption The rectified-flow ODE and DiT attention dynamics of the frozen FLUX backbone remain valid under the proposed LoRA and query blending.
    Background assumption taken from the FLUX / rectified-flow literature and used throughout §3–4.
invented entities (1)
  • AnyStyle single-adapter + attention-modulation pipeline
    purpose: Perform image-guided style transfer without dual LoRAs while retaining content structure.
    The overall method is the paper’s contribution; it has no independent falsifiable prediction outside the reported metrics on the authors’ 400-pair set.

how reviews work

0 comments
Cite this review

Pith. "Pith review of AnyStyle: A Single LoRA is Sufficient for Image-Guided Style Transfer." pith.science (2026). https://pith.science/paper/KO2UNTIJ

@misc{pith2026260704677,
  author       = {Pith},
  title        = {Pith review of: AnyStyle: A Single LoRA is Sufficient for Image-Guided Style Transfer},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KO2UNTIJ}},
  note         = {Machine review of arXiv:2607.04677}
}
read the original abstract

Image-guided style transfer aims to apply the artistic characteristics of a style image to a content image while preserving its semantic structure and layout. Despite advances in diffusion-based methods, existing approaches often face challenges in disentangling content and style, particularly when independently optimized adapters are naively combined, causing conflicts between adapters and limiting controllability over the content-style balance in inference. We further demonstrate that training-free structural guidance directly derived from the content image through the internal attention of pre-trained model outperforms a dedicated content LoRA adapter in terms of structural fidelity and computational efficiency. Building on these observations, we propose AnyStyle, a streamlined framework for image-guided style transfer. The framework adopts a unified single-adapter paradigm for coherent style capture from the style image and incorporates training-free structural guidance from the content image, thus avoiding complex entanglement between multiple adapters and improving controllability and stability. Extensive experiments show that our method delivers competitive quantitative performance and significantly improved perceptual quality. Code is available at https://github.com/Yvan1001/AnyStyle.

Figures

Figures reproduced from arXiv: 2607.04677 by the authors.

Figure 1
Figure 1. Style transfer results of our method. In each set of images, the top-left image denotes the content reference, the bottom-left image denotes the style reference, and the image on the right presents the generated results. The results demonstrate that our method consistently achieves both content preservation and style alignment across diverse inputs, including real-world photographs and painted images. Abstract. Imag… view at source ↗
Figure 2
Figure 2. The first row shows the in￾put and the output of our method. Columns (a–f) present the results of the dual-LoRA approach under dif￾ferent fusion coefficients during infer￾ence(Adjust the coefficients to shift from style dominance to content dom￾inance). As illustrated, the results re￾main unsatisfactory across different co￾efficient settings, achieving a balanced trade-off with the dual-LoRA method is difficult. Ima… view at source ↗
Figure 3
Figure 3. Reconstruction. Attention￾based reconstruction preserves struc￾ture and perceptual quality with a single inference, while LoRA-based requires extra fine-tuning and yields lower fidelity. FLUX Architecture. FLUX [24] is a state-of-the-art text-conditional genera￾tive framework built upon the rectified flow paradigm and the DiT [39] backbone. Its core architecture comprises 19 double￾stream transformer blocks and 38 s… view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Overview of the AnyStyle framework. Given a content reference image and a style reference image, together with a prompt such as "A <c> in <s> style", AnyStyle produces stylized results that preserve the structural integrity of the original object while ensuring stylist…
Figure 5
Figure 5. Figure 5: Qualitative comparisons. The first and second columns correspond to the style reference image and the content reference image, respectively. The subsequent columns present the visual results obtained using different style transfer methods. image. Ablation studies confi…
Figure 6
Figure 6. Figure 6: Additional visual examples of style transfer of our method. Comparison with Existing Methods. We compare AnyStyle with recent state￾of-the-art methods for image-guided style transfer. The quantitative results pre￾sented in Tab. 2 demonstrate that our approach achieves …
Figure 7
Figure 7. Figure 7: User study. Our AnyStyle re￾ceives the highest number of user se￾lections. User Study. To complement quanti￾tative evaluation, we conduct a user study assessing the perceptual quality of AnyStyle against competing methods. A total of 70 participants are presented with …
Figure 8
Figure 8. Figure 8: Standard image generation vs. AnyStyle generation with style LoRA weights. The first column of each group is the style reference image. Under the same prompt "A <c> in <s> style", standard sampling without style LoRA (red boxes) yields diverse but stylistically inconsi…
Figure 9
Figure 9. Figure 9: Parametric ablation study of the structural guidance strength βmax and βmin. framework capable of modulating structural preservation boundaries according to diverse user preferences. Effect of Attention Modulation. To investigate the influence of different at￾tention m…
Figure 10
Figure 10. Figure 10: Visualization of style transfer under different attention modulations. Key-only modulation yields strong stylistic consistency but weakens content preservation. Mod￾ulating both query and key improves content preservation over the key-only variant, yet reduces stylist…
Figure 11
Figure 11. Figure 11: Typical failure cases visualization of our proposed method. descriptions. An illustrative example of this behavior is explicitly detailed in [PITH_FULL_IMAGE:figures/full_fig_p015_11.png]
Figure 1
Figure 1. Figure 1: Qualitative comparisons against contemporary and classical baseline frame￾works. E Computational Efficiency Profile We benchmark the computational overhead and inference times of AnyStyle against representative baselines. The performance evaluations are carried out on …
Figure 2
Figure 2. Figure 2: User study interface. Participants were shown the content image, style image, and outputs from various methods, and were asked to select the most successful result. F User Study Interface The user study interface is shown in [PITH_FULL_IMAGE:figures/full_fig_p023_2.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

63 extracted references · 7 linked inside Pith

  1. [1]

    In: Greeks in AI Symposium 2025 (2025)

    Aravanis, T., Filntisis, P., Maragos, P., Retsinas, G.: Only-Style: Stylistic Consis- tency in Image Generation without Content Leakage. In: Greeks in AI Symposium 2025 (2025)

    2025

  2. [2]

    arXiv preprint arXiv:2503.10614 (2025)

    Chen, B., Zhao, B., Xie, H., Cai, Y., Li, Q., Mao, X.: ConsisLoRA: Enhanc- ing content and style consistency for lora-based style transfer. arXiv preprint arXiv:2503.10614 (2025)

    arXiv 2025

  3. [3]

    In: Proceedings of the Conference on Computer Vision and Pattern Recognition

    Cho, H., Lee, J., Chang, S., Jeong, Y.: One-Shot Structure-Aware Stylized Image Synthesis. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 8302–8311 (2024)

    2024

  4. [4]

    arXiv preprint arXiv:2411.14793 (2024)

    Choi, J., Shin, C., Oh, Y., Kim, H., Lee, J., Yoon, S.: Style-friendly snr sampler for style-driven generation. arXiv preprint arXiv:2411.14793 (2024)

    arXiv 2024

  5. [5]

    In: Proceedings of the European Conference on Computer Vision

    Chong, M.J., Forsyth, D.: Jojogan: One shot face stylization. In: Proceedings of the European Conference on Computer Vision. pp. 128–152 (2022)

    2022

  6. [6]

    In: Proceed- ingsoftheConferenceonComputerVisionandPatternRecognition.pp.8795–8805 (2024)

    Chung, J., Hyun, S., Heo, J.P.: Style Injection in Diffusion: A Training-free Ap- proach for Adapting Large-scale Diffusion Models for Style Transfer. In: Proceed- ingsoftheConferenceonComputerVisionandPatternRecognition.pp.8795–8805 (2024)

    2024

  7. [7]

    In: Proceedings of the European Conference on Computer Vision

    Cui, X., Li, Z., Li, P., Huang, H., Liu, X., He, Z.: InstaStyle: Inversion noise of a stylized image is secretly a style adviser. In: Proceedings of the European Conference on Computer Vision. pp. 455–472 (2024)

    2024

  8. [8]

    In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques

    Efros, A.A., Freeman, W.T.: Image quilting for texture synthesis and transfer. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques. p. 341–346 (2001)

    2001

Show all 63 references
  1. [9]

    In: Proceedings of the International Conference on Machine Learning (2024)

    Esser, P., Kulal, S., Blattmann, A., Entezari, R., Müller, J., Saini, H., Levi, Y., Lorenz, D., Sauer, A., Boesel, F., et al.: Scaling rectified flow transformers for high-resolution image synthesis. In: Proceedings of the International Conference on Machine Learning (2024)

    2024

  2. [10]

    In: Proceedings of the European Conference on Computer Vision

    Frenkel, Y., Vinker, Y., Shamir, A., Cohen-Or, D.: Implicit style-content separation using b-lora. In: Proceedings of the European Conference on Computer Vision. pp. 181–198 (2024)

    2024

  3. [11]

    In: Advances in Neural Information Processing Systems

    Fu, S., Tamir, N., Sundaram, S., Chai, L., Zhang, R., Dekel, T., Isola, P.: Dream- Sim: Learning New Dimensions of Human Visual Similarity using Synthetic Data. In: Advances in Neural Information Processing Systems. pp. 50742–50768 (2023)

    2023

  4. [12]

    ACM Trans- actions on Graphics pp

    Gal, R., Patashnik, O., Maron, H., Bermano, A.H., Chechik, G., Cohen-Or, D.: Stylegan-nada: Clip-guided domain adaptation of image generators. ACM Trans- actions on Graphics pp. 1–13 (2022)

    2022

  5. [13]

    In: Proceedings of the Conference on Computer Vision and Pat- tern Recognition (2016)

    Gatys, L.A., Ecker, A.S., Bethge, M.: Image Style Transfer Using Convolutional Neural Networks. In: Proceedings of the Conference on Computer Vision and Pat- tern Recognition (2016)

    2016

  6. [14]

    In: Proceedings of the International Conference on Learning Representations (2023)

    Hertz, A., Mokady, R., Tenenbaum, J., Aberman, K., Pritch, Y., Cohen-or, D.: Prompt-to-prompt image editing with cross-attention control. In: Proceedings of the International Conference on Learning Representations (2023)

    2023

  7. [15]

    In: Proceedings of the Conference on Computer Vision and Pattern Recognition

    Hertz, A., Voynov, A., Fruchter, S., Cohen-Or, D.: Style aligned image generation via shared attention. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 4775–4785 (2024)

    2024

  8. [16]

    In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques

    Hertzmann, A., Jacobs, C.E., Oliver, N., Curless, B., Salesin, D.H.: Image analo- gies. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques. p. 327–340 (2001) AnyStyle 17

    2001

  9. [17]

    In: Advances in Neural Information Processing Systems

    Ho, J., Jain, A., Abbeel, P.: Denoising Diffusion Probabilistic Models. In: Advances in Neural Information Processing Systems. pp. 6840–6851 (2020)

    2020

  10. [18]

    In: Proceedings of the International Conference on Learning Representations (2022)

    Hu, E.J., yelong shen, Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: LoRA: Low-rank adaptation of large language models. In: Proceedings of the International Conference on Learning Representations (2022)

    2022

  11. [19]

    In: Proceedings of the International Conference on Com- puter Vision (2017)

    Huang, X., Belongie, S.: Arbitrary Style Transfer in Real-Time With Adaptive Instance Normalization. In: Proceedings of the International Conference on Com- puter Vision (2017)

    2017

  12. [20]

    Transactions on Pattern Analysis and Machine Intelligence pp

    Huang, Y., Huang, J., Liu, Y., Yan, M., Lv, J., Liu, J., Xiong, W., Zhang, H., Cao, L., Chen, S.: Diffusion Model-Based Image Editing: A Survey. Transactions on Pattern Analysis and Machine Intelligence pp. 4409–4437 (2025)

    2025

  13. [21]

    In: Proceedings of the European Conference on Computer Vision

    Jia, Y., Hoyer, L., Huang, S., Wang, T., Van Gool, L., Schindler, K., Obukhov, A.: Dginstyle: Domain-generalizable semantic segmentation with image diffusion models and stylized semantic control. In: Proceedings of the European Conference on Computer Vision. pp. 91–109 (2024)

    2024

  14. [22]

    Transactions on Visualization and Computer Graphics pp

    Jing, Y., Yang, Y., Feng, Z., Ye, J., Yu, Y., Song, M.: Neural Style Transfer: A Review. Transactions on Visualization and Computer Graphics pp. 3365–3385 (2020)

    2020

  15. [23]

    In: Proceedings of the European Conference on Computer Vision

    Johnson, J., Alahi, A., Fei-Fei, L.: Perceptual losses for real-time style transfer and super-resolution. In: Proceedings of the European Conference on Computer Vision. pp. 694–711 (2016)

    2016

  16. [24]

    Labs, B.F.: FLUX.https://github.com/black-forest-labs/flux(2024)

    2024

  17. [25]

    In: Proceedings of the Conference on Computer Vision and Pattern Recognition (2017)

    Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution. In: Proceedings of the Conference on Computer Vision and Pattern Recognition (2017)

    2017

  18. [26]

    In: Proceedings of the European Conference on Computer Vision

    Li, W., Fang, M., Zou, C., Gong, B., Zheng, R., Wang, M., Chen, J., Yang, M.: StyleTokenizer: Defining image style by a single instance for controlling diffusion models. In: Proceedings of the European Conference on Computer Vision. pp. 110– 126 (2024)

    2024

  19. [27]

    In: Advances in Neural Information Processing Systems (2017)

    Li, Y., Fang, C., Yang, J., Wang, Z., Lu, X., Yang, M.H.: Universal Style Transfer via Feature Transforms. In: Advances in Neural Information Processing Systems (2017)

    2017

  20. [28]

    In: Proceedings of the European Conference on Computer Vision (2018)

    Li,Y.,Liu,M.Y.,Li,X.,Yang,M.H.,Kautz,J.:AClosed-formSolutiontoPhotore- alistic Image Stylization. In: Proceedings of the European Conference on Computer Vision (2018)

    2018

  21. [29]

    In: Proceedings of the Conference on Computer Vision and Pattern Recognition

    Li, Y., Liu, H., Wu, Q., Mu, F., Yang, J., Gao, J., Li, C., Lee, Y.J.: Gligen: Open-set grounded text-to-image generation. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 22511–22521 (2023)

    2023

  22. [30]

    In: Advances in Neural Information Processing Systems (2025)

    Lin, J., Chen, X., Wu, S., Zhang, Z., Zhang, J., Wang, Y., Tang, Q., qian Wang, Yang, J., Yi, Z.: FreeControl: Efficient, Training-Free Structural Control via One- Step Attention Extraction. In: Advances in Neural Information Processing Systems (2025)

    2025

  23. [31]

    In: Advances in Neural Information Processing Systems

    Lin, K.H., Mo, S., Klingher, B., Mu, F., Zhou, B.: Ctrl-X: Controlling Structure and Appearance for Text-To-Image Generation Without Guidance. In: Advances in Neural Information Processing Systems. pp. 128911–128939 (2024)

    2024

  24. [32]

    In: Proceedings of the International Conference on Computer Vision

    Liu, C., Shah, V., Cui, A., Lazebnik, S.: Unziplora: Separating content and style from a single image. In: Proceedings of the International Conference on Computer Vision. pp. 16776–16785 (2025)

    2025

  25. [33]

    In: Proceedings of the International Conference on Learning Representations

    Liu, X., Gong, C., Liu, Q.: Flow straight and fast: Learning to generate and trans- fer data with rectified flow. In: Proceedings of the International Conference on Learning Representations. pp. 1–15 (2023) 18 Yongwen Lai, Chaoqun Wang

    2023

  26. [34]

    In: Proceedings of the International Conference on Computer Vision (October 2019)

    Lu, M., Zhao, H., Yao, A., Chen, Y., Xu, F., Zhang, L.: A Closed-Form Solution to Universal Style Transfer. In: Proceedings of the International Conference on Computer Vision (October 2019)

    2019

  27. [35]

    In: Proceedings of the AAAI Conference on Artificial Intelligence

    Mou, C., Wang, X., Xie, L., Wu, Y., Zhang, J., Qi, Z., Shan, Y.: T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. In: Proceedings of the AAAI Conference on Artificial Intelligence. pp. 4296–4304 (2024)

    2024

  28. [36]

    In: Proceedings of the European Conference on Computer Vision

    Nikolaidou, K., Retsinas, G., Sfikas, G., Liwicki, M.: DiffusionPen: towards con- trolling the style of handwritten text generation. In: Proceedings of the European Conference on Computer Vision. pp. 417–434 (2024)

    2024

  29. [37]

    In: Proceedings of the Conference on Computer Vision and Pattern Recognition

    Ojha, U., Li, Y., Lu, J., Efros, A.A., Lee, Y.J., Shechtman, E., Zhang, R.: Few- Shot Image Generation via Cross-Domain Correspondence. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 10743–10752 (2021)

    2021

  30. [38]

    Transactions on Machine Learning Research Journal (2024)

    Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., Assran, M., Ballas, N., Galuba, W.,Howes,R.,Huang,P.Y.,Li,S.W.,Misra,I.,Rabbat,M.,Sharma,V.,Synnaeve, G., Xu, H., Jegou, H., Mairal, J., Labatut, P....

    2024

  31. [39]

    In: Proceedings of the International Conference on Computer Vision

    Peebles, W., Xie, S.: Scalable Diffusion Models with Transformers. In: Proceedings of the International Conference on Computer Vision. pp. 4195–4205 (2023)

    2023

  32. [40]

    In: ICLR (2024)

    Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., Rombach, R.: SDXL: Improving latent diffusion models for high-resolution im- age synthesis. In: ICLR (2024)

    2024

  33. [41]

    In: Proceedings of the International Conference on Machine Learning

    Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: Proceedings of the International Conference on Machine Learning. pp. 8748–8...

    2021

  34. [42]

    Journal of Machine Learning Research pp

    Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research pp. 1–67 (2020)

    2020

  35. [43]

    In: Proceedings of the Conference on Computer Vision and Pattern Recognition

    Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-Resolution Image Synthesis With Latent Diffusion Models. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 10684–10695 (2022)

    2022

  36. [44]

    Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedi- calimagesegmentation.In:ProceedingsoftheInternationalConferenceonMedical Image Computing and Computer-Assisted Intervention. pp. 234–241 (2015)

    2015

  37. [45]

    In: Proceedings of the Conference on Computer Vision and Pattern Recogni- tion

    Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., Aberman, K.: Dream- Booth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Genera- tion. In: Proceedings of the Conference on Computer Vision and Pattern Recogni- tion. pp. 22500–22510 (2023)

    2023

  38. [46]

    In: Proceedings of the European Conference on Computer Vision

    Shah, V., Ruiz, N., Cole, F., Lu, E., Lazebnik, S., Li, Y., Jampani, V.: Ziplora: Any subject in any style by effectively merging loras. In: Proceedings of the European Conference on Computer Vision. pp. 422–438 (2024)

    2024

  39. [47]

    Advances in Neural Information Processing Systems pp

    Sohn, K., Jiang, L., Barber, J., Lee, K., Ruiz, N., Krishnan, D., Chang, H., Li, Y., Essa, I., Rubinstein, M., et al.: Styledrop: Text-to-image synthesis of any style. Advances in Neural Information Processing Systems pp. 66860–66889 (2023)

    2023

  40. [48]

    arXiv preprint arXiv:2010.02502 (2020) AnyStyle 19

    Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020) AnyStyle 19

    2010 arXiv

  41. [49]

    In: Proceedings of the Conference on Computer Vision and Pattern Recognition

    Tumanyan, N., Geyer, M., Bagon, S., Dekel, T.: Plug-and-play diffusion features for text-driven image-to-image translation. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 1921–1930 (2023)

    1921

  42. [50]

    In: Advances in Neural Information Processing Systems (2017)

    Vaswani,A.,Shazeer,N.,Parmar,N.,Uszkoreit,J.,Jones,L.,Gomez,A.N.,Kaiser, L.u., Polosukhin, I.: Attention is All you Need. In: Advances in Neural Information Processing Systems (2017)

    2017

  43. [51]

    arXiv preprint arXiv:2404.02733 (2024)

    Wang, H., Spinelli, M., Wang, Q., Bai, X., Qin, Z., Chen, A.: Instantstyle: Free lunch towards style-preserving in text-to-image generation. arXiv preprint arXiv:2404.02733 (2024)

    2024 arXiv

  44. [52]

    arXiv preprint arXiv:2407.00788 (2024)

    Wang, H., Xing, P., Huang, R., Ai, H., Wang, Q., Bai, X.: Instantstyle-plus: Style transfer with content-preserving in text-to-image generation. arXiv preprint arXiv:2407.00788 (2024)

    2024 arXiv

  45. [53]

    In: Proceedings of the International Conference on Machine Learning

    Wang, J., Pu, J., Qi, Z., Guo, J., Ma, Y., Huang, N., Chen, Y., Li, X., Shan, Y.: Taming rectified flow for inversion and editing. In: Proceedings of the International Conference on Machine Learning. pp. 1–11 (2025)

    2025

  46. [54]

    In: Proceedings of the International Conference on Computer Vision

    Wang, Z., Zhao, L., Xing, W.: StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Models. In: Proceedings of the International Conference on Computer Vision. pp. 7677–7689 (2023)

    2023

  47. [55]

    arXiv preprint arXiv:2405.14201 (2024)

    Xu, Y., Wang, Z., Xiao, J., Liu, W., Chen, L.: Freetuner: Any subject in any style with training-free diffusion. arXiv preprint arXiv:2405.14201 (2024)

    2024 arXiv

  48. [56]

    arXiv preprint arXiv:2511.15258 (2025)

    Yang, Y., Wang, Y., Wang, C., Zhang, Y., Chen, Z., He, S.: SplitFlux: Learning to Decouple Content and Style from a Single Image. arXiv preprint arXiv:2511.15258 (2025)

    2025

  49. [57]

    arXiv preprint arXiv:2308.06721 (2023)

    Ye, H., Zhang, J., Liu, S., Han, X., Yang, W.: Ip-adapter: Text compati- ble image prompt adapter for text-to-image diffusion models. arXiv preprint arXiv:2308.06721 (2023)

    2023 arXiv

  50. [58]

    In: AAAI (2024)

    Zhang, C., Xu, X., Wang, L., Dai, Z., Yang, J.: S2wat: Image style transfer via hierarchical vision transformer using strips window attention. In: AAAI (2024)

    2024

  51. [59]

    In: Proceedings of the International Conference on Computer Vision

    Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the International Conference on Computer Vision. pp. 3836–3847 (2023)

    2023

  52. [60]

    In: Proceedings of the conference on computer vision and pattern recognition

    Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effec- tiveness of deep features as a perceptual metric. In: Proceedings of the conference on computer vision and pattern recognition. pp. 586–595 (2018)

    2018

  53. [61]

    In: Proceedings of the Conference on Computer Vision and Pattern Recognition

    Zhang, Y., Huang, N., Tang, F., Huang, H., Ma, C., Dong, W., Xu, C.: Inversion- Based Style Transfer With Diffusion Models. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. pp. 10146–10156 (2023)

    2023

  54. [62]

    In: ACM SIG- GRAPH (2022)

    Zhang, Y., Tang, F., Dong, W., Huang, H., Ma, C., Lee, T.Y., Xu, C.: Domain enhanced arbitrary image style transfer via contrastive learning. In: ACM SIG- GRAPH (2022)

    2022

  55. [63]

    Not Sure

    Zhang, Z., Zhang, Q., Xing, W., Li, G., Zhao, L., Sun, J., Lan, Z., Luan, J., Huang, Y., Lin, H.: Artbank: Artistic style transfer with pre-trained diffusion model and implicit style prompt bank. In: Proceedings of the AAAI Conference on Artificial Intelligence. pp. 7396–7404 ...

    2024

Pith tools

Reviewed July 11, 2026 · model on record in the stance chip above.