CoCoDiff: Correspondence-Consistent Diffusion Model for Fine-grained Style Transfer
Authors: Wenbo Nie
Zixiang Li
Renshuai Tao
Bin WU
Yunchao Wei
Yao Zhao
Venue: ICLR 2026 Poster (2026)
Keywords: Style Transfer
Diffusion Model
Computer Vision
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Transferring visual style between images while preserving semantic correspondence between similar objects remains a central challenge in computer vision. While existing methods have made great strides, most of them operate at global level but overlook region-wise and even pixel-wise semantic correspondence. To address this, we propose **CoCoDiff**, a novel *training-free* and *low-cost* style transfer framework that leverages pretrained latent diffusion models to achieve fine-grained, semantically consistent stylization. We identify that correspondence cues within generative diffusion models are under-explored and that content consistency across semantically matched regions is often neglected. CoCoDiff introduces a pixel-wise semantic correspondence module that mines intermediate diffusion features to construct a dense alignment map between content and style images. Furthermore, a cycle-consistency module then enforces structural and perceptual alignment across iterations, yielding object and region level stylization that preserves geometry and detail. Despite requiring no additional training or supervision, CoCoDiff delivers state-of-the-art visual quality and strong quantitative results, outperforming methods that rely on extra training or annotations.

Published as a conference paper at ICLR 2026
COCODIFF: CORRESPONDENCE-CONSIIS SION MODEL FOR FINE- FINE-GRAINED STYLE TRANSFER
DIFFU
Wenbo N
Zixiang Li
Renshuai Tao
Bin Wu 12
Yunchao Wei!
Yao Zhao
Institute of Information Science, Beijing Jiaotong University Visual Intelligence X International Joint Laboratory of the Ministry of Education
ABSTRACT
Transferring visual style between images while preserving semantic correspon- dence between similar objects remains central challenge in computer vision. While existing methods have made great strides, most of them operate at global level but overlook region-wise and even pixel-wise semantic correspondence. To address this, we propose CofoDiff, novel training-free and low-cost style trans- fer framework that leverages pretrained latent diffusion models to achieve fine- grained, semantically consistent stylization. We identify that correspondence cues within generative diffusion models are under-explored and that content consis- tency across semantically matched regions often neglected. CoCoDiff intro- duces a pixel-wise semantic correspondence module that mines intermediate dif- fusion features to construct dense alignment map between content and style images. Furthermore, a sycle-consistency module then enforces structural and perceptual alignment across iterations, yielding object and region level stylization that preserves geometry and detail. Despite requiring no additional training or supervision, CoCoDiff delivers state-of-the-ar visual quality and strong quantíta- tive results, outperforming methods that rely on extra training or annotations. The source code is publicly available at: https: //g/thttbh////// com/ Beeboo-Nie
CoCoDiff
INTRODUCTION
Diffusion models have achieved remarkable success in the field of generative artificial intelligence. By performing the diffusion process within compressed latent space, Latent Diffusion Models (LDMs) Rombach et al. (002) achieve substantial advantages in both generation efficiency and image quality. This efficient and flexible architecture has established them
powerful backbone,
including text-to-image generation Nichol et al. (202T): Ramesh et al. (0022) : Saharia et al.) (0022),
(2023), ) image restoration Lin et al. (2024); Wang et al. (024b): Wu et al. (2024), and style transfer Wang et al. (2023 2024a);
image editing Meng et al. (202 Tumanyan et al. (2022); ;; Brooks
Chung et al. (024)). Although diffusion models exhibit significant potential, style transfer remains challenging when semantic alignment and fine mme-grained correspondence are required. Beyond global appearance changes, high-fidelity stylization must respect region- and object-level structure. This calls for principled way mine the correspondence signals already encoded in pretrained diffusion models, enabling structure-aware semantically consistent style transfer.
Neural style transfer Johnson et al. (2016); Zhang & Tang (2025); An et al. 2021) aims to render the content one image in the visual appearance of another. central challenge is to ensure that the stylistic features are applied in semantically consistent manner, particularly across corresponding regions or objects Jiang & Chen (0025). With the rise large-scale generative models, pre-trained latent diffusion models have become powerful foundation for this task. However, as shown in Fig. [bb,, representative method for training-free diffusion models suffers structural degradation and correspondence errors. In contrast, in Fig. [c), representative of another neural-based method fails to capture the style information of the target image effectively. Similarly, Fig. ((() and (e),
"Equal contribution; #Corresponding author (email: rstao eju...du.cn..

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Style
Contest
(a) Ours
(b) StyleD
SYYR-2
(d) SMS
(c) Instanct
Detnil
Figure I: Comparison of different style transfer methods. We compare our proposed CoCoDiff with three other representative methods with zoomed-in details.
both prompt-guided methods, provided with a clear style prompt "oil painting, Claude Monet's Impression, Sunrise", also fail capture the stylistic features. Almost all of these approaches treat the pre-trained model as a simple black-box generator. They either apply style globally(e.g.. StylelID) Chung et al.] (202)); Deng et al. (2022). which leads to structural inconsistencies and tent detail loss, or they build complex external modules that attempt to learn correspondences from scratch (e.g., SMS Diang et al. (2025). Such methods are inefficient and overlook the powerful alignment information already present within the diffusion backbone itself. This results in a critical gap: failure to directly mine and utilize the model's own semantic understanding for high- high-fidelity style transfer. Therefore, we argue that the full potential of these models for style transfer remains largely under-explored.
These issues point to more fundamental, two-fold challenge at the core of style transfer. The first challenge is finding dense, semantically meaningful correspondences between content and style images. This is inherently difficult because content and style images often have vast differences in color, texture, and geometry. An effective method must identify features that are robust enough to match high-level concepts (e.g., the structure of the ship, the texture of the waves in Fig. [1] across domains, yet precise enough to align fine- -ee-grained details (e.g., the color of the sky, and the subtle, almost elusive representation of the ship in Fig. []. The second, equally significant challenge is utilizing these correspondences to guide the stylization. Even with perfect alignment map, how to perform feature injection is also difficult problem. Therefore, method is required transfer local style characteristics onto the content's s structure without creating disharmony in the overall global style. Solving this dual problem of correspondence-aaart analysis and feature injection remains the primary barrier to achieving high-fidelity structure- structure-preserving style transfer.
To this end, we take a step forward by leveraging the implicit aligmment capabilities of pre-trained diffusion models and introducing cyclic optimization mechanism to enhance semantic consistency Seeppoodddne-Coonsistteeeeeee
across domains. We propose that achieves fine-grained feature correspondence and crucctre-pprseerving style transfer through adaptive, correspondence-avaaa guidance. Our approach is built upon the generative priors and semantic representation capabilities of pre-trained diffusion models, enabling structure-aware and semantically aligned stylization without the need for additional supervision or fine-tuning. Unlike existing methods that rely on coarse global matching expensive supervised learning. CoCoDiff explicitly models pixel-level correspondences between content and style images by mining inter- mediate features optimal denoising steps and network layers. Specifically, dimensional grid search over temporal and spatial resolutions of the diffusion backbone to identify the feature representations that best capture semantic structures. Using these features, we construct a dense correspondence map via cosine similarity, aligning each spatial location in the content im- age with its most semantically similar counterpart the style image. Based on this alignment, we design novel feature injection mechanism that selectively modulates the self-attention maps during the diffusion inversion process, transferring style information a spatially aware manner. Furthermore, we introduce cycle-consistency module that iteratively refines the stylized output by enforcing structural preservation and appearance fidelity across generations. The optimization pro- cess is guided by perceptual and style losses, well as statistical alignment via Adaptive Instance Normalization (AdaIN), ensuring that the final stylized image remains faithful to the original content while accurately reflecting the reference style. Our contributions be summarized follows:
Diffusion (CoCoDiff), a novel framework
perform two-
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We propose CoCoDiff, training-free free, diffusion-based framework for fine- tie-ggrined, structure-pin prrecrri--preeerving style transfer that operates directly on pretrained backbones without additional supervision fine-tuning.
We introduce a pixel-wise semantic correspondence module that mines intermediate fea- tures from pre-trained diffusion models build dense alignment maps between content and style images, enabling region- and object-level stylization.
We design a eyelic optimization method that integrates attention- attention-guided feature injection with consisteney constraints, enabling more stable and coherent stylization.
The remainder of the paper is organized as follows. Section 2 reviews prior work on diffusion models and style transfer. Section presents the proposed CoCoDiff framework, including the fine tie-gained feature matching module and fitting cycle and iterative control strategy. In Section 3 we describe the experimental setup, datasets, evaluation metrics, and provide a comprehensive analysis of the results. Finally, Section summarizes the contributions and discusses potential future directions.
2
RELATED WORK
2.1 DIFFUSION MODELS
Diffusion models have transformed generative modeling through iterative denoising, producing high-quality images, excelling in style transfer and similar applications. Starting with Denoising Diffusion Probabilistic Models (DDPM) Ho al. (2020), achieving superior sample quality com- pared to GANs Goodfellow et al. (2014); : Chen et al. (216): Karras et al. (2019). with high com- putational cost. Denoising Diffusion Implicit Models (DDIM) Song al. (2022) addressed this by using a non-Markovian sampling process, reducing inference steps. LDM Rombach et al. (2022), like Stable Diffusion, further improved efficiency by operating in a compressed latent space. The DALL-E2 Ramesh et al. (2022) and Imagen (Saharia al. (0222) integrate text-conditioned diffusion
with CLIP Radford et al. [2021) enabling photorealistic text-to-image generation.
In downstream tasks, diffusion models have shown remarkable versatility. They are used for image editing through techniques like Null-Text Inversion Mokady et al.] (2023) and ControlNet /Zhang et al. (2023a), and DiffusionCLIP Kim et al. (2022). DIFT Tang et al. (2023) enables feature cor- respondence by aligning object-specific features across domains. For super-resolution, SRDiff Li et al. (2021) reconstructs high-quality images from low-resolution inputs, while RePaint Lugmayr et al. 2022) advances inpainting by seamlessly reconstructing missing regions. Additionally, style transfer is enhanced through approaches like DiffStyle Jiang & Chen (2025); Li (2024) which in- tegrates text-guided diffusion with feature alignment for precise stylization. These advancements highlight diffusion models' fidelity and robustness across diverse generative tasks.
2.2
STYLE TRANSFER
The field of neural style transfer has evolved significantly since the seminal work of Gatys et al. Gatys et al. (2016). who showed that hierarchical layers CNNs can separate content structures from style textures, more efficient approaches like including AdaIN HHang & Belongie (2017) and WCT Li et al. (0017) soon followed. To better preserve structural details, researchers introduced transformer architectures, StyTR" Deng al. (2022) pioneered the first step and StyleFormer Wu ct al. (202T) incorporated transformer modules into CNN pipelines. Moreover, patch-based meth- Lia et al. (2017): Shang et al. (2025); Wang et al. (2022): Chen & Schmidt (2016): [i & Wand (016)) Sheng et al. (2018) provide ane-ggrined local constraints for style transfer and offer impor- tant insights into maintaining style stability for objects appearing at different positions or scales. Dif- fusion models have revolutionized this field through two main approaches: training-based based methods like StyleDiffusion Wang et al. (2023): Li 2024)), use neural flows, while training-free ffre approaches like DiffArtist Jiang & Chen (2025); Chung et al. (2024); Xu et al. (2024), manipulate attention in pre-trained models. Recent innovations include InstantStyle Style-PSyll-Plus Plus Wang et al. (2024a), which balances content and style using inverted noise, and FreeStyle He al. (2024) uses dual-stream encoder for text-gut text-guided transfer. However, these methods have largely overlooked the challenge of maintaining style consistency for identical objects in style transfer.
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FFM
Output
Images
Style 1
Style Ly
Input Images
Exchange
FIC
Late
Content cea
Content on
Reall
festii csf
Trcoder
Cycle
Lay
L
Lcom
Output
faatrectt
Fine-grained Feature Matching (FFM)
Fitting Cycle and Iterative Control (FIC)
Matching
Injection
Transfer
Fitling Cycle
Oulput
Figure 2: Framework Overview of our proposed Correpoodenee--onsiteen Diffusion (CoCoDifI).
METHODOLOGY
3.1 PRELIMINARY
Starting from pure noise æy ~ (),,,), the diffusion model predicts noise c0 t) at each timestep t and updates H; to 2t-1 via:
q(, x+-1) = =N (x1; v X,,,,1, 3.1)
where and x are the latent variables at timestep / and (-1 with / E (1...., T}. The parameter 3e (0, 1) defines the noise variance at each timestep, as determined by a predefined schedule Br.
Stable Diffusion (SD) has compressed latent space. It uses encoder to transform input images into a compact latent representation, and a decoder to reconstruct them. The U-Net architecture of Stable Diffusion generates images from latent representations through iterative denoising. A key point of this process self-attention (SA) blocks. For a feature Φ, the SA block computes as:
QKT
yout Attn(Q, K, V) softmax
V.
Vd
In our work, we perform style transfer by leveraging the self-attention (SA) mechanism of Stable Diffusion (SD) to inject features from style image 1, into content image 1c. We adopt the DDIM inversion procedure Rombach et al. (2022) to traverse the denoising trajectory from t=0 (clean image) f=T (Gaussian noise). each timestep f, we extract queries Qt from I, and keys and values Kt. V from I,. Stylization is effected by replacing the content keys/values Ky V2 with K3, Vt inside the SA blocks, and computing:
Hout Att K., V.).
(3)
This process forms the foundation for injecting style features.
3.2 FRAMEWORK OVERVIEW
As illustrated in Fig. and Algorithm I our method consists of two key stages: Fine- -raiigd Feature Matching that mines correspondences content and style; Fitting Cycle and Iteration Control that ensures consistency in the generative process. We will detail them the following part.
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3.3 FINE-GRAINED FEATURE MATCHING
Diffusion features for correspondence. Pretrained diffusion U-Nets have the ability to encode semantic cues across timesteps and network layers Tang et al. (202)). We treat intermediate acti- vations as per-pixel descriptors for dense correspondence. For notational simplicity, throughout this subsection we use [P() and I (p=) to denote the pixels of content and style images, respectively.
Dense correspondence by diffusion features. Just as a painter maintains stylistic consistency when depicting the same object, our method prioritizes stylistic coherence across semantically cor- responding regions to ensure entire visual consistency. Exploiting the semantic encoding of diffusion features, we extract feature maps and, for each spatial location Pe in the content image, identify the most semantically similar location p, in the style image through cosine similarity measurements of normalized diffusion features:
Ia(p)) I.(p.)
cos Po)
(4)
[a(D)) (p.)]]
This process yields a dense semantic correspondence map that guides style transfer with improved accuracy and structure preservation.
Selecting optimal combination. We employ a two-dimensional grid search determine the opti- mal combination of timestep and network layer I, aiming to balance semantic representation and low-level detail. The optimal pair (t", ,1') is selected by maximizing a correspondence quality metric M(t, 1) over predefined candidate sets T and L:
(t",1") arga, max M). IET,
(5)
Here, M(I, evaluates the alignment quality based on the extracted feature maps at timestep / and layer 1. To clarify how (Mt, is defined and quantified, we follow the standard semantic correspondence protocol and extract diffusion features from image pairs on benehmark datasets such as SPair-71k Min et al. (019).. For each content keypoint, we identify the location in the style image that yields the highest cosine similarity, and adopt the Percentage of Correct Keypoints (PCK) as the evaluation criterion to determine whether the predicted correspondence falls within an acceptable distance of the ground-truth keypoint. The metric M(t, 1) is thus defined as the average PCK score over all evaluated samples, reflecting the reliability of semantic correspondence within the feature space (t, 1). Higher values indicate that the feature configuration provides a more reliable semantic alignment foundation for subsequent style transfer.
We fix (t", (*) and use the resulting correspondence map to steer correspondence-wware feature injection in the subsequent feature injection stage. The final spatial location P is:
p-= = arg,, max cos Px). p.El,
(6)
3.4 FITTING CYCLE AND ITERATIVE CONTROL
We then use the semantic correspondence map mentioned before to guide style injection. To further enhance the semantic consistency of the feature maps, we adjust attention weights based on the cosine similarity at corresponding positions P. in I,:
feat = attn [k] [p:]]] + feat(k|[pa]
where denotes the feature channel. Pe is the spatial location in content image, Pt the corre- sponding location in stylized content image and w is weighting factor controlling the contribution of the attention features. This update mechanism ensures precise injection of style features into semantically aligned regions while maximally preserving the structural information of the content image, resulting in high-quality style transfer. However, feature-based stylization is fragile when the statisties of the style and content images vary widely. Directly matching features across these disparate distributions can lead to imprecise or semantically misaligned results.
Closed-loop refinement. To progressively enhance correspondence while preserving content struc- ture and improving visual fidelity, we introduce a cycle-consisten refinement with closed-loop fea- ture fusion. In each fitting cycle, we first identify a of images T, and J,. We use the previously
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AotAeia
last
SCSAS SOAID
Figure 3: Qualitative comparison. We compare CoCoDiff (Ours) with seven representative methods, selected from eeffusiio-bbsede patch-based, CNN- transformer-basee and other approaches, to provide a comprehensive evaluation.
Algorithm 1: Correspondence--Guicee
Style Transfer
Input: le: content image: I:: style image; Istge: style image with content features; fer pretrained diffusion U-Net; T: candidate timesteps; L: candidate layers; wt injection weight; Te: Tat
stopping thresholds; z: max iterations
Output: Igon
// Stage A: correspondence
Iatuo < Attn(Qu, Ke,
// equation
Extract (F2.), }. (F for LET, LEL; (1,1)) arg max M(L,
// equation
for each Pe: Phtze arg arrgmam,, maxpsre cos (os(F!" (pe), Ftr (potze)) // equation equation
// Stage B: fitting & control
for 1 to Z do
Ycs (ly.) Me=r(ye) + m(v=)
// equation
11
feat[k] + - attn|k]] + feat[k][pa]]
// equation
Lcontent [[Sobel( Sobel(Le)ll, Latyle E, [[G(1( -GL))) // equation 10
if Leontent and Latyle T, then break
return (e)
mentioned Feature Injection method to enable the style image to learn the style features of the con- tent image, updating its attention map to obtain the reverse style transfer image Tst..- Then, using the Feature Matching method, we build correspondences between le and Lsty. to obtain coordinate pairs (po. P.ty.). ). For Le and Ls, we perform feature injection again, leveraging the coordinate pairs to inject attention values of corresponding features, achieving a corespondencce-cossiseet cycle. The updated formulation as follows:
[eat alln + feat(kk]pp)]
(8)
Optimal objectives. In each iteration, we execute one fitting cycle to progressively optimize the generated image. Specifically, after each iterative generation, update the feature map via Eq. Eq. achieving semantically consistent fusion of style and content features. To evaluate the quality of the feature map generated in each cycle and enable adaptive iterative optimization, the following objective functions are to measure content and style fidelity concerning two aspects. Here, the
image generated iteration is denoted ( The content perceptual loss Ccontent is defined as:
Lcontent |Soeel ((bb)) Sobel(T.)]l:

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where S-bel(-) computes edge maps to capture structural information. Meanwhile, the style percep- tual loss Letyle can defined as:
Estyle
Σ
G(1))
(10)
1Elayers
where G' denotes the Gram Gatys et al. (2015 2016) matrix capturing textural style properties across selected feature layers.
The iterative process terminates when both Leontent To and Lstyle Tsx where and are predefined thresholds. This ensures the generated image prevents over-stylization or structural dis- tortion. We describe in detail how choose these hyperparameters in the next section.
Tone barmonization. In the process of artistic style transfer, the harmonization of color and tone plays a pivotal role achieving effective stylization. To this end, we employ AdaIN Huuan & Belongie| (2017) to modulate statistical information during the style transfer process. Specifically, we leverage the latent variables of the content image and style image ya. to facilitate tone transfer through statistical alignment, as expressed in the following formula:
Yes (y.) Me ((Ve)
p(ys),
(11)
(ye)
where v(-) and (-)) denote the channel-wise mean and standard deviation, respectively. This ap- proach ensures that the tone information of the style image is effectively integrated while preserving the structural integrity of the content image, thereby enhancing the quality of the stylized output.
Ours
StyleID
StyTR-2
Style
style
S%y
Content
Content
Style
(A)
(B)
Figure 4: (A) Qualitative comparison with additional zoomed-in details. We compare method with StyleID and StyTR² as baseline approaches, highlighting the differences through zoomed-in details. (B) The illustration of cycle-based image style transfer. (a) Direct feature matching between the style image and the content image often results in low matching accuracy and feature correspondence failure. (b) By first transforming the style image adopt the content image's 's style before performing fcature matching, the matching accuracy is significantly improved. (c) Direct correspondence result. (d) Indirect correspondence result.
4 EXPERIMENTS
Datasets and Settings We use MS-COCO Lin et al. (2015) as the content dataset and WikiArt Phillips & Mackintosh (2TT)) the style dataset. We conduct all experiments on single NVIDIA RTX 4090 GPU using the pre- treitrained Stable Diffusion V1.4 model as it allows for fair comparison, with DDIM Rombach al. (2022) sampling over 50 timesteps (t (4....,55)) and the attention temperature scaling parameter 7 0.7.
Evaluation Protocol We compare our method against nine style transfer methods: StylelD Chung et al. (2024), AesPA-Net Hong et al. (2023), Sty Deng et al. (0022). InST Zhang et al. (023b), InstantStyle-Plus Wang et al. (2024a), AdaIN [Huang & Belongie (2017). SCSA Shang al. FreeStylee He et al, (2024), SMS Jiang al. (2025). Experiments are conducted by configur- ing these methods according to their publicly available codes and settings. To quantitatively evaluate the style transfer model, conduct experiments across 13 distinct artistic styles, including oil paint- ing, kids' illustration, watercolor, Ghibli, landscape woodblock printing, etc. For quantitative com- parison purposes, adopt methodology similar to StyleID Chung et al. (204)) and StyTR² Deng
StylelD(Chung
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Metric
Ours
StyleID
AcsPA
StyTR'
InST
Instant+
AdaIN
SCSA
PreeStyle
SMS
FID 1
18.432
21.010
19.645
18.886
21.541
20.982
18.672
20.835
23.654
31.266
IPIPS
0.549
0.565
0,586
0.587
0.785
0.584
0.612
0.562
0.689
0.821
ArFiD
30.100
34,446
32.124
31,559
40.235
34.820
31.711
34,106
41.640
58.756
CFSD
0.609
0.619
0.632
0.687
0.881
0.710
0.642
0.612
0.660
0.704
Table 1: Quantitative evaluation results. We compare every methods across multiple metries.
Columns 2nd_g'h are reference-guided methods while columns 10th-11th are prompt-based meth- ods.
al. 2022), randomly selecting 30 content images and 30 style images from each dataset to create a comprehensive evaluation suite. We employ four established metrics: FID Heusel et al. (0018), LPIPS Zhang et al. (2018). ArtFID Wright & Ommer (2022), and CFSD(Chung et al. (2024). Lower FID scores indicate greater similarity between stylized images and the target style domain. LPIPS quantifies perceptual similarity between stylized and original images. ArtFlID measures alignment with human aesthetic preferences, with lower values reflecting superior performance. CFSD is content-focused metric that evaluates the preservation of the original image's s structural integrity. Together, these metrics provide a comprehensive evaluation of style transfer quality and content preservation.
4.1 QUANTITATIVE COMPARISON
Table reports the quantitative comparison four metries: FID, LPIPS, ArtFID, and CFSD (1 represents lower is better). Our method showcases a comprehensive quantitative comparison with baseline approaches, highlighting its superior efficacy in striking a robust balance between vivid style transfer quality and content preservation.
4.2 QUALITATIVE COMPARISON
As shown Fig. 3 CoCoDiff achieves superior performance in style transfer, producing visually compelling results with precise feature alignment. For example, in the first row, the car exhibits consistent and vibrant coloring, while the background mountains acquire deeper hues. Similarly, in the third row, the boat's hull seamlessly shifts to the target red color. These results highlight our approach's ability to achieve fine-grained stylization while preserving content integrity, significantly outperforming baseline methods in both style expressiveness and visual coherence. Additionally, provide a qualitative comparison with zoomed-in details in Fig. 4[A) A). For the painting The Scream, characterized by its distorted forms, vibrant colors, and dynamic lines, CoCoDiff more effectively preserves stylistic fluidity and intricate details compared to other approaches. This reinforces the precision of our method in achieving advanced feature alignment and stylistic fidelity.
4.3 DIFFUSION MODEL SCALING AND CORRESPONDENCH PERFORMANCE
In order to demonstrate the -odel-agnostic nature of CoCoDiff, we conduct experiments using SD vl.4, SD v1.5. SD v2.1, and SDXL with identical configurations, as shown in the Tab. The results indicate that CoCoDiff adapts well to different scales of diffusion models, delivering stable perfor- mance. However, significant differences arise terms of semantic consistency and fine-grained style transfer across these models. While SDXL shows strong performance in style transfer quality, its stability in fine-grained semantic aligmment decreases. This is due to the need for fine-tuning hyperparameters such y and temperature for different models to optimize visual performance.
Metric
v1.
SD v1.5
SD v2.1
SD XL
Sobel Gram
FID
LPIPS
CFSD
FID I
18.432
17.995
18,252
21.112
26.513
0.697
0.804
LPIPS
0.549
0.525
0.621
0.635
29.845
0.506
0.631
AnFID
30.1
28.94
31.18
36.69
23.471
0.753
0.761
CFSD +
0.609
0.589
0.766
0.714
18.432
0.549
0.609
Table 2: Comparison across different diffu- sion models.
Table 3: Results for Sobel and Gram combi- nations.
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4.4 EFFECTIVENESS OF FEATURE EXTRACTION
To demonstrate the critical limitations, we present our comparative results in Fig. 4 The left side of the figure displays the original content image and the target style image, which share similar wave patterns. We perform style transfer using three methods: a baseline direct style transfer method, a direct correspondence method, and our indirect correspondence method, which relies on Laty..
Difference heatmaps are obtained by subtracting the results of the two correspon- dence methods from the baseline output; brighter colors indicate more pronounced differences. As shown in Fig. B..c), the direct correspondence method pro- duces global differences, revealing compared to the baseline. In contrast,
failure to maintain a consistent stylized output
the heatmap (B.d) for our indirect method shows strong differences lo-
CPSD
622
calized to the wave patterns, indicat- ing that it successfully preserves the elephant's contour while applying a high-fide fighffideiity style transfer. These ob- servations underscore the inadequacy of direct approach and validates our indirect Seature-based strategy.
ham2
Bend
Ours
1LPIPS
IIter
(h)
Figure 5: Quantitative comparison of the cycle module. (a) Balance between LPIPS and FID metrics across itera- tions. (b) CFSD variations across iterations.
4.5 EFFECTIVENESS OF CYCLE MODULE
Recognizing the limitations of fixed it- eration counts in style transfer, we in- vestigate the crucial role of adaptive it- eration within our CoCoDiff method. We conduct experiments comparing style transfer across 10 content and 10 style images, evaluating fixed iter- ations (ranging from to 5) against an adaptive gating strategy with early stopping. Performance evaluation em- ploys FID, CFSD, and LPIPS. As shown in Fig. Sta, our adaptive gating approach achieves an optimal trade- off between style transfer and content preservation, significantly surpassing fixed iteration counts in both LPIPS and FID metrics. Fig. 5] further confirms this advantage through CFSD scores, demonstrating content preservation outcomes. Additionally, Fig. T presents two sets of visualized iteration results, showcasing optimal visual qual- ity with a red line for adaptive iteration selection.
Cnt/ 'Syy
Iter-1 1
Iter=2
Iter=3
Iter-d
Figure 6: Visual results of the iteration process. The group with the best quality is highlighted by red line.
4.6 ABLATION STUDY
To optimize the attention injection weight w, we introduce a scaling factor to modulate the intensity of style feature injection based on cosine similarity. We conduct an ablation study, evaluating the visual quality of the generated images with w set 0.3, 0.6. 1.8, 2.4 and 3.0. The results are presented in the Tab 4al The results indicate that w 0.6 strikes an optimal balance between content preservation and style fidelity, maintaining semantic consistency and structural integrity. Lower values (e.g., = 0.3) may lead to insufficient stylization, while higher values (e.g., = 3.0) risk introducing structural distortions. These results underscore the critical role of carefully tuning to achieve high-quality, visually coherent style transfer outcomes. Tab, 4b validates AdaIN Huang & Belongic 20177's 's effectiveness in harmonizing color and tone during the style transfer process.
Table 3 reports an ablation on the Sobel and Gram. Without either component, the model produces the weakest results across all metrics. Adding Sobel alone significantly improves LPIPS and CFSD by enforcing clearer structural alignment, while adding Gram alone yields better FID by enhancing global style coherence but tends to distort local perceptual details. When combined, Sobel and Gram provide complementary benefits, achieving the best overall performance with the lowest FII)

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W
0.3
0.6
1.8
2.4
AdalN
w/o AdaIN
FID
23.717
18.432
21.911
33.980
35.937
FID LPIPS CFSD
18.432
20.515
LPIPS
0.681
0.549
0.588
0.656
0.667
0.549
0.762
CFSD
0.642
0.609
0.746
0.735
0.791
0.609
0.646
(a) Fffect feature injection weight (1e).
(b) Effect of AdaIN on model performance metrics.
Table 4: Ablation study analyzing the impact of various design choices on model performance.
and strongest content-style balance. This demonstrates that integrating structural cues with style correlation statistics leads to more stable optimization and higher-quality stylization.
4.7
USER STUDY
We conduct a user study to evaluate the performance of our proposed style transfer method in terms of semantic consistency and style fidelity. Participants view content and style images alongside randomized style transfer results from various methods to ensure unbiased comparisons.
We collected comparisons from 25
participants (aged 19-45) across
Method
Style
Content
Avg
10 distinct content images and 8 style images. Table [5] summarizes the preference rates, with CoCoDiff achieving the highest scores across all criteria, thereby substantiating its effectiivnness. These findings not only further substantiate the effi- eacy of approach in producing high-quality stylizations but also provide compelling evidence of consistent user preference for re- sults that maintain strong feature
StyleID
Chung et al. 2224
0.212
0.135
0.174
AesPA
Hong ct al
2023)
0.033
0.134
0.084
StyTR2
Deng al.
2202
0.132
0.036
0.084
InST Zhang
200.3b
0.004
0.025
0.015
Instant+
Wang al. (2024a)
0.002
0.174
0.088
AdaIN
Huang & Belongie (017
0.038
0.013
0.026
FreeStyle
He et al. 2024
0.021
0.013
0,017
SMS Jiang al. (202
0.012
0.032
0.022
CoCoDiff(Ours)
0.546
0.438
0.492
Table 5: User study results consistency and quality. alignment while exhibiting expressive and visually appealing stylistic characteristics.
5 CONCLUSIONS
In this paper, we introduce Correspondence-Comee Consssstet Diffusion (CoCoDiff), a novel training-free framework that utilizes pre-trained latent diffusion models achieve high-fidelity style transfer. By extracting intermediate features to establish pixel-wise correspondences and applying cyclic optimization techniques, CoCoDiff ensures robust semantic consistency and preserves structural integrity between the content and the transferred style. Through extensive experiments across var- ious benchmarks, we demonstrate that CoCoDiff significantly outperforms current state-of-the-art models, not only in terms of style fidelity but also in content alignment. These results reveal the potential of CoCoDiff unlock new possibilities for diffusion- based generative tasks, paving the way for more effective and flexible style transfer solutions in generative modeling.
ACKNOWLEDGE
This work was supported by National Natural Science Foundation of China (No. 62506030, U24B20179, 92470203), Beijing Natural Science Foundation (No. L242021, L242022) and the Fundamental Research Funds for the Central Universities (2024XKRC082).
ETHICS STATEMENT
This work focuses developing training-free style transfer framework, CoCoDiff, built upon pre-trained diffusion models. Our research does not involve the collection of personal data, human subjects, sensitive information, and all datasets used publicly available under appropriate licenses. We encourage responsible use CoCoDiff within creative, educational, and research contexts, and emphasize that any deployment of this method should adhere to ethical guidelines
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and legal standards. We hope work can inspire further research and contribute to advancing the positive impact of generative modeling in both academic and real-world contexts.
REPRODUCIBILITY STATEMENT
The code is available in the supplementary materials. For full reproducibility. we have detailed datasets used for testing are also provided as described in Sec. Appendix [D.1] The experimen- tal hyperparameters and model selections in the Sec. and the Appendix D.2
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A OVERVIEW
This supplementary material supports the main paper with:
The of Large Language Models (Section
B
Evaluation metrics (Section
Experiment details (Section
D
Visual comparisons results (Section
Impact (Section G
B USE OF LLMS
In accordance with ICLR 2026 policy Al assistance disclosure, acknowledge the of large language models in our paper preparation process. Our usage limits to language polishing and grammatical improvements of the final manuscript. The language models do not involve experimen- tal design, data analysis, result interpretation, or the generation of substantive content. They serve solely as writing assistance tools to improve clarity and readability of text already authored by the human authors listed on this paper. The scientific contributions, methodology, experiments, results, and conclusions belong entirely to the work of the human authors.
C EVALUATION METRICS
C.1 LPIPS
LPIPS Zhang al. (2018) perceptual metric designed mimic how humans perceive image differences. Instead of comparing pixels directly, it measures the distance between images in the feature space of a deep network that has been trained on a perceptual similarity task. To calculate LPIPS, reference image and generated image are fed into pre-trained network, and feature maps are extracted from several of its layers. These feature maps are 1.2-normalized, and weighted L.2 distance is computed between the corresponding features of the two images at each layer. The final LPIPS score is the sum these weighted distances. The formula is given by:
d(r, 20)
Σ H.W,
Σ wi (1b1(())). (20)),,
(12)
A lower LPIPS score indicates higher perceptual similarity between the generated and real images, making it valuable metric for tasks like image reconstruction and super-resolution.
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Primitive art
Romanticism
Impressionism
Post Impressooiism
Carioon
Sketch
Style
Consent
Figure 7: Additional classic visual outcomes in diverse artistic styles.
C.2 FID
FID Heusel et al. (2018) assesses the quality and realism of an entire set of generated images from statistical perspective. doesn't compare individual images but rather measures the distance between the feature distributions of generated image set and a real image set. The calculation relies on pre-trained Inception-v3 network. Feature vectors are extracted from the last pooling layer for both the real and generated image sets. 'he mean vectors (μ4 and covariance matrices (E) are then computed for both sets. FID is the Fréchet distance between these two multivariate Gaussian distributions, calculated using the formula:
FID ||,, Mo 12 Tr(E, + -
(13)
lower FID score indicates that the generated image distribution closer to the real image distri- bution, which implies better diversity and realism. FID is widely considered the standard metric for evaluating Generative Adversarial Networks (GANs).
C.3 ARTFID
ArtFID Wright & Ommer (2022) is composite metric specifically designed for evaluating neural style transfer models, aiming to balance content fidelity and style fidelity in a way that aligns better with human subjective judgment.. It combines LPIPS, which measures perceptual content similarity, with FID, which assesses style distribution realism, through a multiplicative formula to penalize deviations in either aspect. To calculate ArtFID, first compute the average LPIPS score between the stylized images and the original content images, and the FID score between the stylized images and the reference style images, using pre-trained networks like VGG for LPIPS and Inception-v3 for FID. The final ArtFID score is then obtained by the formula:
ArtFID (1 + LPIPS) (1 FID).
(14)
lower ArtFID score indicates superior style transfer performance, where both content preservation and style matching are optimized, making it particularly useful for comparing methods in artistic image generation tasks.
C.4
CFSD
CFSD Chung al. (024) is content-focused metric that evaluates the structural similarity in neural style transfer by emphasizing spatial relationships between image patches, addressing limi- tations in metrics like I.PISS that may be influenced by style elements. operates feature maps
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extracted from pre-trained VGG19 network's conv3 layer to capture mid-level structural details. To calculate CFSD, extract feature maps F from both the content image Le and stylized image Less compute self-correlation matrices M = F x F", and normalize each row via softmax to form prob- ability distributions S. The CFSD score is the average Kullback-Leibler divergence across these distributions:
Aw (Sc..)
CFSD
(15)
lower CFSD score signifies better preservation of the content's structural integrity, such as edges and patch interrelations, independent of stylistic changes, rendering it effective complement to perceptual metrics in style transfer evaluations.
D EXPERIMENT DETAILS
D. DATASET
Our work utilizes two primary datasets: the MS-COCO 2017 Lin al. (2015) dataset for content and the WikiArt Phillips & Mackintosh| (01I)) dataset for artistic styles. We use the 1 118,287 images from the MS MS-COCO 2017 training set as our content source, leveraging its rich variety of everyday scenes and objects. For our style library, we meticulously select 13 distinet artistic styles from WikiArt, which include: Oil painting, Kids' illustration, Watercolor, Ghibli, Landscape woodblock printing, Chinese Ink, Sketch, Pop art, Impressionism, Cubism, Cyberpunk, Pointillism, and Crayon.
To ensure experimental fairness across different style transfer expressions, we adopted specific strategies: 1) For exemplar-guided generation(e.g. StyleID Chung al. (2024)). carefully lected paired images from the dataset, using style images as guidance: 2) For text-guided genera- tion(e.g., SMS Diang al. (2255) ), crafted appropriate prompts that accurately describe the same style images used in the exemplar approach, thereby facilitating high-quality generation. This dual approach allows for comprehensive evaluation of our method's 's versatility across different guidance modalities.
D.2 IMPLEMENTATION DETAILS
We conduct experiments on a single NVIDIA RTX 4090 GPU with 24GB of VRAM. The soft- ware environment is built on Python 3.9, utilizing PyTorch 1.13.1 and CUDA 12.5 to leverage the GPU's computational power for accelerated processing. Crucially, our feature injection technique applied starting from the 49th timestep. This strategic timing allows the model to first establish a strong content structure before introducing detailed style information, preventing the style from overwhelming the original content. Our method can be applied to various diffusion-based models; however, for fair comparison, we chose to use v1.4.
D.3 OPTIMAL OBJECTIVES
D.3.1 GRAM MATRIX
In style transfer tasks, central challenge lies in accurately capturing the style characteristics of an image, particularly global features such as texture and color. Traditional pixel-level operations fail to capture these global statistics and cannot disregard spatial information. To solve this problem, the Gram matrix Gatys al. (2015) is introduced. The Gram matrix extracts statistical information by calculating the inner product between every pair of feature maps. Specifically, the Gram matrix is defined
Gig = (fi, f1) Σ (k),
(16)
effectively captures the second-order statistical information of feature vectors, encapsulating global distribution patterns such as texture and color while disregarding spatial positions.
The Gram matrix be considered as two-dimensional covariance matrix, capturing the corre- lations between different feature channels. In their seminal work, Gatys et al. demonstrated that two-dimensional covariance is particularly well-suited for style transfer tasks. They showed that
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Skyle
Content
Figure 8: Visualization results of our proposed methods.
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Conteal
Style
Clurs
StsleID
AcsPA
StyTR-2
IIST
Figure 9: Comparison of style transfer results. Visual results our CoCoDiff method compared against four baseline approaches across diverse artistic styles: Ghibli Style, Pixel Art, Chinese Ink, Kid Illustration, and Vintage Style.
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among different types of covariance, two-dimensional covariance, represented by the Gram ma- trix, the most effective for describing style.
In style transfer applications, the Gram matrix facilitates optimization by minimizing distributional differences in feature spaces, enabling target images to emulate the distinctive patterns present in style reference images. The scale-invariance and robustness of Gram matrices establish them as ideal tools for style description, allowing for effective style transfer across diverse visual domains regardless of content structure dimensional variations.
D.3.2 SOBEL
A critical balance must be maintained in style transfer: hand, the algorithm must effectively leam and incorporate the distinctive feature information from the style reference image, while on the other hand, must preserve the structural integrity of the content image without distortion. This dual objective presents fundamental challenge in the field.
The Sobel operator, defined by convolving an image with kernels for vertical edges, effectively detects intensity gradients to highlight edges and line textures in generated images. By computing
the gradient magnitude, typically as v/ G + G2 ), emphasizes regions of rapid intensity change, which correspond to line structures and textures. This edge-enhancing capability allows the Sobel operator to control and refine the linear patterns and textural details in image generation, ensuring that stylistic elements like contours and boundaries are preserved or accentuated.
E MORE COMPARISONS WITH PATCHED-BASED METHODS
We include representative methods such as CNNMRF Li Wand (2016), Style-Swap Chen & Schmidt (2016), etc. and conduct systematic quantitative comparisons. As shown in Tab. 6 and Fig. 66 CoCoDiff (Ours) achieves superior or highly competitive performance across all metrics. These results indicate that method not only captures meaningful correspondences but also ben- efits from diffusion- semantic alignment, enabling performance beyond conventional patch- level approaches. Importantly, our method is also training-free.
Metric
Ours
CNNMRF
Style- Swap
DIA
Avatar-Nee Net
SCSA+StyleID
FID (L)
18.432
27.872
35.642
31,933
22.356
20.835
LPIPS (1)
0.549
0.672
0.793
0,661
0.641
0.562
CFSD (L)
0.609
0.844
0.761
0.649
0.753
0.612
Table 6: Comparison of metrics across methods.
F VISUAL COMPARISONS RESULTS
We present additional experimental results in Fig. 7 that showcase classic visual outcomes across diverse range of artistic styles. Our method, CoCoDiff, effectively transfers styles from several artis- tic movements, including Primitive Art, Romanticism, Impressionism, Post-mmpresssoniss Cartoon, and Sketch. More results can be found in Fig.
Further experiments confirm the robustness and versatility of our proposed method. The results demonstrate ability to produce visually compelling stylized images while preserving high content fidelity. CoCoDiff maintains superior balance between expressive styling and content preservation across a wide range of artistic scenarios.
Additionally, we present comprehensive evaluation of our style transfer method (CoCoDiff) by comparing with four established baseline approaches: StyleID Chung et al. (024). AesPA- Net Hong al. (2023). StyTR2 Deng et al. (2022) and InST Zhang et al. (2023b). We test these methods across diverse artistic styles, including Ghibli Style, Art, Chinese Ink, Kid Illustration, and Vintage Style shown in Fig. Our experiments show that CoCoDiff consistently outperforms these basclines, achieving an outstanding balance of vivid stylistic rendering and accurate content preservation in each style. For example, in Ghibli Style, CoCoDiff captures the fluid, whimsical
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Conaat
Style
Oue
Style Shap
StylED
Figure 10: Additional comparisons with patch-based methods.
aesthetic more effectively than others, while in Chinese Ink, preserves intricate brushstroke details with greater fidelity than StyleID. Similarly, for Pixel Art and Kid Illustration, CoCoDiff produces sharp, stylized images with minimal structural distortion compared StyTR² and InST. These find- ings underscore CoCoDiff's adaptability and precision, ensuring visually striking and structurally coherent outputs across broad spectrum artistic romnains.einforciing its effectiveness across diverse artistic contexts.
F.1 FINE-GRAINED FEATURE CORRESPONDENCE
key contribution of our approach is the implementation of fine-grained feature correspondence. To clearly demonstrate this capability, we have included detailed style transfer results on three distinct subjects: flower, cow and house. These images in Fig. Fig. 12 and Fig. 13 effectively illustrate how our method precisely aligns and transfers stylistic elements while meticulously preserving the unique content of each subject.
G IMPACT
We believe our proposed training-free style transfer matching module achieves remarkable results through its novel approach. The concept cyclic consistency can be readily integrated to vari- ous other style transfer methods to achieve high-fidelity feature correspondence. From societal perspective, our work brings positive implications for entertainment devices, animation media, and related fields, while simultaneously raising important considerations regarding copyright protection and intellectual property rights.
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Style
Content
Figure 11: Fine-grained style transfer visual results: Flower.
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Style
Content
Figure 12: Fine-grained style transfer visual results: Cow.
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Style
Content
DAn
Figure 13: Fine- Fine-grained style transfer visual results: House.
23


Reviews:
[{"review_id": "xQrQpIs8jo", "reviewer": "Reviewer_hz3B", "summary": "This paper proposes CoCoDiff, a training-free fine-grained style transfer framework based on pretrained latent diffusion models. The method establishes dense pixel-level semantic correspondences by mining intermediate diffusion features and introduces a cycle-consistency module to enforce structural and perceptual alignment. The approach achieves good visual quality and quantitative results.", "questions": "1. The grid search for finding optimal (t*, l*) lacks theoretical guidance.\n\n2. Why does \"first converting the style image to content style\" improve matching? Please provide theoretical analysis or more detailed mechanism explanation.\n\n3. The authors need to provide sufficient validation on different diffusion models.\n\n4. The authors need to report runtime and computational resource consumption.\n\n5. How are the candidate sets T and L for grid search selected?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The method demonstrates certain innovation by mining intermediate diffusion features to construct dense pixel-level semantic correspondences between content and style images, and introducing a cycle-consistency module to enhance structural and perceptual alignment.\n\n2. The paper achieves superior quantitative and qualitative results compared to existing methods across multiple datasets.", "weaknesses": "1. Why does cycle consistency improve semantic correspondence? The paper lacks theoretical analysis or deeper explanation.\n\n2. The correspondence quality metric M(t,l) in Equation (5) is not clearly defined. How is \"correspondence quality\" quantified?\n\n3. Inconsistent notation usage: symbols such as p_c, p_s, p*_s are defined inconsistently across different sections.\n\n4. Figure 8 has low comparison quality with text labels that are too small.\n\n4. Insufficient experiments. Table 2 shows that on Mip-NeRF 360, GENIE's PSNR is 5-7 dB lower than Mip-NeRF, with even larger gaps in SSIM and LPIPS metrics. The authors attribute this to Gaussian volume density, but lack systematic analysis. The authors need to strengthen the persuasiveness.\n\n5. Missing training time comparisons.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "bHVlMvDZb3", "reviewer": "Reviewer_gHaT", "summary": "This paper  proposes a training-free, diffusion-based framework for fine-grained, structure-preserving style transfer that operates directly on pretrained backbones without additional supervision or fine-tuning. The propose method delivers state-of-the-art visual quality and strong quantitative results, outperforming methods that rely on extra training or annotations.", "questions": "Please refer to the weakness.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "(1) The proposed training-free style transfer method, which ensures style consistency, represents a valuable contribution to the field.\n(2) A clear algorithm is presented for better understanding.\n(3) CoCoDiff outperforms the six representative methods to some extend.", "weaknesses": "(1) Line 252 says M(t, l) that evaluates the alignment quality based on the extracted feature maps at timestep t and layer l. What exactly is M(t, l)?\n\n(2) Fig. 2 requires significant refinement for improved clarity. FFM appears to have two inputs, but only I_sty_c is explicitly shown in the main framework. It seems that I_sty is inputted and reconstructed within the U-Net. If this is used for self-attention extraction, I suggest that the output should also be included. If this is the case, the self-attention section should be moved from Section 3.1 to Section 3.2 for better understanding. Besides, FIC also has two inputs where I can see only one.\n\nThe structure of Fig.2 should be better organized. The input images are put in the center, which is not easy to understand\n\n(3) The used SDv1.4 is a very old version. Do the authors try newer diffusion models like SD2.1, SDXL or FLUX.\n\n(4) The inference time should be listed as it consists of many complex steps like feature exchange, fitting cycle, iterative control.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "e9OFQLeQGP", "reviewer": "Reviewer_yGas", "summary": "This paper focuses on enhancing the performance of artistic style transfer between images with similar semantics. A key limitation of most existing works is that they operate at the global level, while overlooking region-wise and even pixel-wise semantic correspondence. To address this gap, the authors propose CoCoDiff, a training-free diffusion-based framework. This framework extracts intermediate diffusion features to establish pixel-wise correspondences and leverages cyclic optimization techniques, achieving fine-grained stylization with semantic consistency.", "questions": "Please see weaknesses above.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "+ The integration of semantic consistency into diffusion-based style transfer is interesting and beneficial for stylization tasks between images with similar semantics.\n\n+ The proposed method is training-free.", "weaknesses": "- My primary concern is that this paper appears to overlook a crucial research direction in style transfer, namely patch-based style transfer. Aligned with the motivation of this paper, works in this direction primarily leverage semantic correspondence between features to perform style transfer between objects with similar semantics. Representative methods include CNNMRF [A], Style-Swap [B], DIA [C], DivSwapper [D], Avatar-Net [E], and SCSA [F]. This paper neither discusses these methods in the related work section nor compares with them in experiments. Consequently, it is hard to effectively evaluate the technical innovation and performance of this work.\n\n[A] Chuan Li and Michael Wand. Combining markov random fields and convolutional neural networks for image synthesis. In CVPR, pages 2479–2486, 2016.\n\n[B] Tian Qi Chen and Mark Schmidt. Fast patch-based style transfer of arbitrary style. arXiv preprint\narXiv:1612.04337, 2016.\n\n[C] Jing Liao, Yuan Yao, Lu Yuan, Gang Hua, and Sing Bing Kang. Visual attribute transfer through deep image analogy. TOG, 2017.\n\n[D] Zhizhong Wang, Lei Zhao, Haibo Chen, Zhiwen Zuo, Ailin Li, Wei Xing, and Dongming Lu. Divswapper: Towards diversified patch-based arbitrary style transfer. In IJCAI, pages 4980–4987, 2022.\n\n[E] Lu Sheng, Ziyi Lin, Jing Shao, and Xiaogang Wang. Avatarnet: Multi-scale zero-shot style transfer by feature decoration. In CVPR, pages 8242–8250, 2018.\n\n[F] Chunnan Shang, Zhizhong Wang, Hongwei Wang, Xiangming Meng. SCSA: A Plug-and-Play Semantic Continuous-Sparse Attention for Arbitrary Semantic Style Transfer. In CVPR, pages 13051–13060, 2025.\n\n- In L247–248, the authors state: “The optimal pair $(t^∗, l^∗)$ is selected by maximizing a correspondence quality metric $\\mathcal{M}(t, l)$ over predefined candidate sets $\\mathcal{T}$ and $\\mathcal{L}$” How are $\\mathcal{T}$ and $\\mathcal{L}$ determined? Are they set based on empirical values?\n\n- It seems that the reason that the proposed method can perform style transfer in a training-free manner mainly relies on the adjustment of attention weights in Eq. (7). Why can this adjustment effectively enhance the semantic consistency of the feature maps? Some necessary theoretical explanations are lacking.\n\n- In L314–316, it is mentioned that “the fitting cycle's iteration process terminates when both ... are predefined thresholds.” It is unclear what kind of constraint a content loss ($\\mathcal{L}_{content}$) greater than a threshold ($\\tau_c$) can impose. What is the difference between this and constraining only the style loss?\n\n- Section 4.6 (User Study) lacks necessary details. For example: what were the scoring instructions provided to users? What were the scoring rules? Which specific aspects were evaluated under the “Style” and “Content” dimensions in Table 3?\n\n- Some minor detail issues: In L144–145, ArtFlow does not belong to the diffusion-based methods. In Eq. (6), $F_s$ is not defined.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "P7GEqqSlKO", "reviewer": "Reviewer_1orb", "summary": "This work proposed a training-free diffusion-based framework for fine-grained, correspondence-consistent style transfer. It leverages pretrained diffusion models to extract pixel-level semantic correspondences between content and style images, enabling region- and object-aware stylization.\n\nThe main contribution includes 1. Pixel-Wise Semantic Correspondence Module to extract intermediate diffusion features to build dense, semantically meaningful alignment maps between content and style images; and 2. Cycle-Consistency Optimization which \nintegrates attention-guided feature injection with iterative refinement to enhance structural stability and stylization coherence.", "questions": "1. I hope the author can provide the specific time and memory consumption of the method.\n2. The work lacks ablation experiments. I hope the authors can demonstrate the role of each module they proposed. And provide more credible theoretical explanations for sobel and g network.\n3. I hope the author can add some newer baselines. Or provide some objective theoretical analysis to explain the similarities and differences.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. This work has enough metrics to prove that the solution is effective, across FID, LPIPS, ArtFID, and CFSD. The ablation and user studies further validate design choices such as the feature injection weight and AdaIN harmonization.\n\n2. This paper is well written and easy to follow.", "weaknesses": "1. This work claimed training free but it still need iteration during sampling. the complexity is not stated in the work\n2. Some innovations were introduced rather suddenly, lacking sufficient theoretical foundation and clear explanations.\n3. Why sobel and g works for style and context similarity. I look forward to a more reasonable explanation\n4. This work lacks many recent references. I hope the author can include it as a baseline for comparison. e.g. [1] [2]\n\n\n[1] Ahn, Namhyuk, et al. \"Dreamstyler: Paint by style inversion with text-to-image diffusion models.\" aaai24\n\n[2] He, Huiang, et al. \"Semantix: An Energy Guided Sampler for Semantic Style Transfer.\" iclr25", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)