OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization Data
Authors: Yiren Song
Cheng Liu
Mike Zheng Shou
Venue: NeurIPS 2025 poster ()
Keywords: Diffusion model
Conditional generation
Image generation
Style transfer
Primary area: deep_learning

Abstract: Diffusion models have advanced image stylization significantly, yet two core challenges persist: (1) maintaining consistent stylization in complex scenes, particularly identity, composition, and fine details, and (2) preventing style degradation in image-to-image pipelines with style LoRAs. GPT-4o's exceptional stylization consistency highlights the performance gap between open-source methods and proprietary models. To bridge this gap, we propose \textbf{OmniConsistency}, a universal consistency plugin leveraging large-scale Diffusion Transformers (DiTs). OmniConsistency contributes: (1) an in-context consistency learning framework trained on aligned image pairs for robust generalization; (2) a two-stage progressive learning strategy decoupling style learning from consistency preservation to mitigate style degradation; and (3) a fully plug-and-play design compatible with arbitrary style LoRAs under the Flux framework. Extensive experiments show that OmniConsistency significantly enhances visual coherence and aesthetic quality, achieving performance comparable to commercial state-of-the-art model GPT-4o.

OmniConsistency:
Learning Style-Agnostic Consistency from Paired Stylization Data
Yiren Song"
Cheng Liu"
Mike Zheng Shout
Show Lab, National University of Singapore
Problem definition
Text2image
Input
Image2imge|S0Edit)
TextZimage
Text2image
3D Character LORA
Style degradation & detail inconsistency
OminConsistency
teRiG TREBRY
BIG
BANG THEURT
Our results
Input
3D Chibi Style
Toys
Picasso Style
Input
American Cartoon Style
Clay Style
Vector Style
LEGO Style
Drigamn Style
Figure Our method achieves style-consistent and structure- peecuurepreeerving image stylization under diverse scenes and unseen style LoRAs, outperforming existing baselines without style degradation.
Abstract
Diffusion models have advanced image stylization significantly, yet two core challenges persist: (1) maintaining consistent stylization complex scenes, partic- ularly identity, composition, and fine details, and (2) preventing style degradation in image-to-image pipelines with style LoRAs. GPT-4o's exceptional stylization consistency highlights the performance gap between open-source methods and proprietary models. To bridge this gap, propose OmniConsistency. uni- versal consistency plugin leveraging large-scale Diffusion Transformers (DiTss. OmniConsistency contributes: (1) in-context consistency learning framework trained on aligned image pairs for robust generalization; (2) two-stage progres- sive learning strategy decoupling style learning from consistency preservation mitigate style degradation; and (3) fully plug-and-play design compatible with arbitrary style LoRAs under the Flux framework. Extensive experiments show that OmniConsistency significantly enhances visual coherence and aesthetic quality, achieving performance comparable commercial state-of-the-ar model GPT-4o. Code is released at http://ggghbbbcoooomooo/////oomn
"Equal contribution. Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Introduction
Image stylization aims transfer artistic styles target images. With the emergence of diffusion models, the mainstream approach has shifted toward fine-tuning pretrained models via Low-Rank Adaptation (LoRA) [77 coupled with image-to-image (121) inference pipelines and consistency modules (e.g.. ControlNet ) significantly enhancing stylization quality. Recently, open-source communities have released numerous stylization-orienteed LoRA modules. Additionally, methods like InstantStyle 4 and IPAdapter [48] enable tuning-free stylization via adapter modules pretrained on large-scale datasets, allowing efficient style transfer without sask-specific fine-tuning.
Despite recent progress, current image stylization methods face three key challenges: (1) Limited consistency between stylized outputs and inputs -aput--xistting modules (c.g., ControlNet) ensure global alignment but fail to preserve fine semantics and details in complex scenes. (2) Style degradation in image-to-image (I2I) settings- LoRA and IPAdapter often yield lower style fidelity than in text- to-image generation, as Figure. shown. S (3) Lack of flexibility in layout montrol-methods relying on rigid conditions (e.g., edges, sketches, poses) struggle support creative structure changes like chibi-style transformation.
These issues significantly restrict the practical performance of existing methods, motivating this research. To address these challenges, we propose OmniConsistency, a general consistency plugin based on the Diffusion Transformer architecture, combined with an in-context learning strategy, specifically designed for image stylization tasks. OmmiConsistency precisely preserves image semantics and details during style transfer in style-agnostic manner.
To effectively support model training. we meticulously constructed a high-quality, multi-source stylization dataset, covering 22 different styles and totaling 2,600 image pairs. Data sources include manually drawn illustrations and GPT-4o-guided [ generation of highly consistent stylized images. After rigorous manual selection, we obtained reliable paired dataset suitable for consistency model training.
To decouple style learning from consistency learning, we propose two-stage decoupled training framework along with rolling LoRA Bank Loader mechanism: In the first stage, we independently train LoRA models on style-specific data to build LoRA Bank; in the second stage, we attach the pretrained style LoRA modules onto Diffusion Transformer (29 backbone and train the consistency module using corresponding image pairs (original and stylized images). The second- stage training explicitly targets structural and semantic consistency, preventing the consistency module from absorbing any specific style features. To ensure style-agnostic capability, the LoRA modules and their corresponding data subsets are periodically switched during training iterations, ensuring stable consistency performance across diverse styles and achieving strong generalization, supporting plug-and-plat integration with arbitrary style LoRA modules.
Furthermore, to achieve more flexible layout control, we forego traditional explicit geometric con- straints (such as edges, sketches, poses) commonly used in previous methods. Instead, we adopt a more flexible implicit control strategy, utilizing only the original image itself as the conditioning input. This approach allows OmniConsistency to better balance style expression and structural consistency. especially suitable for tasks involving significant character proportion transformations, such as chibi- style generation. Through a data-driven approach, the model autonomously learns composition and semantic consistency mappings from paired data, further enhancing its generalization capabilities.
In summary, our key contributions
follows:
1. We propose OmniConsistency, a universal consistency plugin based on Diffusion Transform- ers with in-context learning, significantly enhancing visual consistency in 121 stylization tasks in a style-agnostic manner.
2. We design two-stage, style-consistency disentangled training strategy and innovatively introduce a rolling LoRA Bank loader mechanism. substantially improving consistency generalization across diverse styles. Moreover, we propose a lightweight Consistency LoRA Module and Conditional Token Mapping scheme, effectively improving computational efficiency.
3. We build and release a diverse stylization dataset and benchmark for image stylization consistency and introduce a standardized evaluation protocol based on GPT-4o, facilitating comprehensive performance assessments.

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2 Related Works
2.1 Diffusion Models
Image generation has experienced a major paradigm shift recent years, with diffusion models [6] increasingly surpassing GANs [TO] the dominant approach, thanks to their superior image quality and training stability. Diffusion models are widely applied in areas such as image synthesis [32 ].
19 2 video gneration [ 5 12 and process generation Harly successes in this field primarily relied on U-Net- denoising architectures. Representative works include Stable Diffusion (SD) 32 its improved variant SDXL (00, and several other foundational models, all of which demonstrated the strong potential of diffusion models for high-fidelity image synthesis. More recently, the field has evolved toward transformer-based architectures, most notably through the emergence of the Diffusion Transformer (DiT) framework. State-of-the-ard models such as SD3 61. FLUX 21,, and HunyuanDiT [X] leverage the scalability and representation power of transformers to push generation quality even further. Compared with their U-Net-based predecessors, DiT models exhibit markedly better output fidelity and prompt alignment, setting a new standard for diffusion-be bassdd generation.
image editing B 13
[5T
53
47
[8
38
39 37]
2.2 Stylized Image Generation
Recent diffusion-basee methods have enabled efficient style transfer via tuning-free adapters such as IP-Adapter [8]]. Style-Adapter [44], and StyleAlign 45 These approaches extract style embeddings from a single reference image and inject them into the generation process using cross-attention layers. However, many visual styles cannot be fully captured by a single image. For instance, the Ghibli aesthetic involves consistent design across characters, environments, and objects. In practice, training style-specific LoRA modules on multiple examples remains the most effective and widely adopted approach (33 20 34 7, offering stronger generalization and stylization quality in text-to-image generation. Yet, when these LoRA modules are applied to image-to-image translation or editing tasks, they often suffer from style degradation due to structural constraints imposed by modules like ControlNet [49]. This results in diminished style expressiveness and visual inconsistency. To resolve this, we propose OmniConsisteney, under structural guidance. Rather than replacing LoRA, method augments it, ensuring faithful style preservation even in controlled editing scenarios.
plug-and-play consistency module that enhances style retention
2.3 Condition-guided Diffusion Models
Conditional diffusion models have rapidly evolved, with increasingly refined mechanisms for control- lable image generation. Broadly, conditioning signals fall into two categories: semantic conditions, which guide high-level content (c.g., reference images of subjects objects), and spatial condi- tions, which constrain structural layout (c.g., edge maps, depth cues, or human poses). Earlier approaches, typically built. U-Net backhones, adopted two main paradigms: attention-based (50) focused on integrating semantic information, while residual- based methods like ControlNet (49 and T2I-Adapter [27] were designed to maintain spatial fidelity. With the emergence of transformer- based diffusion architectures (e.g.. DiT (2)).
modules such as IP-Adapter [88) and SSR-Encoder
conditioning strategies have shifted toward more unified and efficient token-based designs. Recent methods like OminiControl (40) and EasyControl (52) treat both semantic and spatial conditions as token sequences, enabling seamless integration with transformer blocks, and inspired subsequent approaches [36 25 35). This transition simplifies the overall design, improves scalability, and facili- tates more effective handling of multimodal inputs. The shift from U-Net to DiT-based conditioning reflects broader trend in generative modeling: moving toward more modular, generalizable, and coapuuattio-efffcciee frameworks for controlled generation.
3 Methods
In Sec. 3.1 we introduce the overall architecture of our proposed method; in Sec. 3.2 we present the
decoupled training strategy for dlyle-consistency learning; in Sec. 3.3 we describe the consistency LoRA Module; in Sec. 3.4 we detail the position encoding interpolation; and in Sec. 3.5 the composition and collection process of the paired dataset.
we explain
3

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Rolling LoRA Bank
Text Branch
Denoise Branch
Condition Branch
Legends
Prompt
Learnable
30 chibi style
Frezon
Style
Encoder
Text Thoees Oy
AmericanCartoon
Patred/Data
Nosed Latant Talans Z
Candition Tokans Cj
2
Style
...
DIT Block
Causal Attention
style
Switch every"
Style LoRA
%
Style LoRA 3
steps.
Self-Attention
Consistency LoRA
LEGO style
Feed Forward
Quary
Style
(a) Style LoRA Training
(b) Consistency LoRA Module Training
Illustration of OmniConsisteney, consisting of style learning and consistency learning phases. (a) In the style learning phase, individual LoRA modules are trained on dedicated datasets to capture unique stylistic details. (b) The subsequent consistency learning phase optimizes consistency LoRA for structural and detail coherence across diverse stylizations, integrating pre-trained style LoRA dynamically.
Figure 2:
3.1 Overall Architecture
The OmniConsistency framework is designed achieve robust style-agnostic consistency in image stylization. As shown in Fig. 2 the method is composed of two coordinated components: a two-stage training pipeline and several plug-and-pl architectural modules that enhance controllability and generalization.
In the training pipeline, we first build style LoRA bank by independently fine-tuning LoRA modules for 22 styles. In the second stage, we train a consistency control module, referred to as consistency LoRA, on the same paired data while dynamically switching the style LoRA module in alignment with the training instance. This strategy decouples stylization from consistency and improves generalization across styles.
Beyond the training design, our framework introduces two architectural components to enhance achieve style-consistency disentanglement and efficien consistency control: (1) A Consistency LoRA Module, which injects condition-specife information through a dedicated low-rank adaptation path applied only to conditional branches; (2) A Position-Aware Interpolation and Feature Reuse enables the use of low-resolution condition images to guide high-resolution generation while strictly preserving spatial alignment. This design improves both training and inference efficiency. Together, these designs allow OnmiConsistency to preserve semantic structure and fine details across diverse stylizations, while supporting flexible control and efficient computation.
3.2 Style-Consistency Decoupled Training
To address the limitations described above and further enhance OmniConsistency's robustness and flexibility, we introduce a novel two-stage decoupled training strategy that explicitly separates style learning from consistency preservation. This method contrasts conventional that simultaneously optimize both style and consistency components, potentially causing conflicts and suboptimal convergence.
approaches
Stage 1:
Style Learning. In this initial phase, we independently train multiple style-specific LoRA modules dedicated datasets, each corresponding to one particular style (c.g., anime, oil painting, photorealism). These datasets consist of paired stylized images and their original counterparts. During training, each LoRA module is fine-tuned from the pretrained Diffusion Transformer backbone with fixed learning rate of x 10-3 for 6,000 iterations. The primary objective at this stage to accurately capture distinctive artistic elements, textures, color palettes, and stylistic details associated uniquely with each style. By isolating this process, we prevent interference from structural consistency constraints and create style LoRA bank.
Stage 2: Consistency Learning. the subsequent stage, we aim to learn a style-agnostic consistency module that can effectively preserve structural, semantie, and detailed consistency regardless of the

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applied style. Specifically, we introduce lightweight Consistency LoRA Module, which integrates seamlessly with pretrained style LoRA modules. During this phase, style LoRA modules from the first stage are dynamically loaded in Rolling LoRA Bank, periodically switching between different style LoRAs along with their corresponding paired datasets during training iterations. This approach ensures the consistency module optimizes exclusively for preserving input content integrity, actively avoiding the absorption of specific stylistic traits.
Through this explicit decoupling of style and consistency training objectives and the introduction of novel techniques such as the rolling LoRA bank loader, our approach ensures both superior stylization quality and robust content preservation across diverse stylistic transformations.
3.3 Consistency LoRA Module
LoRA Design for Consistency. To efficiently incorporate conditional signals while preserving the stylization capacity of the diffusion backbone, we extend the FLUX [2] architecture with dedicated consistency LoRA module applied only to the condition branch.
Conventional methods apply control modules to the main network layers 40 which disrupt style representation. contrast, our design isolates consistency learning from the stylization pathway to ensure compatibility. Specifically, we leave the LoRA attachment points on the main diffusion transformer unoccupied, allowing arbitrary style LoRAs to be mounted independently. This branch- isolated design ensures compatibility between consistency learning and stylization, enabling both modules to operate without conflict or parameter entanglement.
Formally, given input features Zr, Zn, Ze for the text, noise, and condition branches, we define the standard QKV projections as:
Qi Woz,
K, WKZ, V WyZ, iE nt,
where Wo, WK, Wy E Rddd are shared projection matrices across branches. To inject conditional information more effectively, we apply LoRA transformations solely to the condition branch:
AQ. BoA AK, BxAK
AV. Av
where AK Av E Rrdd and Bo, BK, By E gdxr are low-rank adaptation matrices with The updated QKV for the condition branch becomes:
<
a' = Q. + AQe:
K' Ke + AKe,
= V.
(3)
Meanwhile, the text and noise branches remain unaltered:
0 =i,
K- K2 V Vi, E (t,
(4)
This design ensures that consistency-related adaptation is introduced in an isolated manner, without interfering with the backbone's 's stylization capacity or other conditioning paths.
Causal Attention. Unlike Flux and prior controllable generation methods, we replace the original bidirectional attention with causal attention, setting that follows EasyControl [52). As shown in Fig. [] we design a structured attention mask where condition tokens can only attend to each other and are blocked from accessing noise/text tokens, while the main branch (noise and text tokens) follows standard causal attention and can attend to the condition tokens. This design offers two key advantages: (1) the main branch maintains clean causal modeling during inference, avoiding interference from condition tokens; and (2) no additional LoRA parameters are introduced to the noise/text branch, preserving all tunable capacity for style LoRA and preventing conflicts between stylization and consistency. By enforcing this read-only conditioning mechanism, we improve editing controllability while maintaining clear separation between style and structure.
3.4 Designs for Efficient and Sealable Conditioning
To improve the computational efficiency of transformer- based diffusion models, introduce two complementary techniques: (1) Conditional Token Mapping for low-resolution conditional guidance, and (2) Feature Reuse for eliminating redundant computation across denoising steps.
Conditional Token Mapping (CTM). Concatenating full-resolution condition tokens with denoising tokens leads to high memory usage and inference latency. To address this, use a oowressolution
5

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condition image to guide high-resolution generation, with spatial alignment ensured via CTM. Given
original resolution NM, and condition resolution (H, W), we define scaling factors:
M H
N
S
S.
(5)
W
Each token (i,j) in the downsampled condition maps position (P.. in the high-resolution grid: P. = 1==i-p Sn Pj = S
(6)
This mapping preserves pixel-level correspondence between condition and output features, enabling structurally coherent guidance under significant resolution mismatch.
Feature Reuse. During standard diffusion, condition tokens remain fixed across all denoising steps. while latent tokens evolve. To reduce repeated computation, we cache the intermediate features of condition tokens- specifically their key-value projections in attention and reuse them throughout inference 28 41 This optimization significantly lowers inference time and GPU memory without
sacrificing generation quality.
3.5 Dataset Collection
We construct a high-quality paired dataset entirely through GPT-4o-driven generation D. Specifically, leverage GPT-4o synthesize stylized versions of input images across 22 diverse artistic styles, as well as generate corresponding descriptive text annotations for both source and stylized images.
The input images collected from publicly available internet sources and carefully curated to ensure legal compliance. To ensure semantic and structural consistency, we apply a comman-n--the-lee filtering pipeline. Annotators review each generated image pair and remove those with issues such as gender mismatches, incorrect age or skin tone, detail distortions, pose discrepancies, inconsistent styles, misaligned layouts. This rigorous filtering process is applied over 5,000 candidate pairs, from which we curate 80-150 high-quality pairs per style, resulting in a total of 2,600 verified image pairs.
To promote diversity, the input images for each style are mutually exclusive, with complex scenes such multi-person portraits. The dataset spans wide range of chibi, pixel-art, watercolor, oil painting, and cyberpunk- and will be publicly released to support future research in stylization and consistency modeling.
anime, sketch,
4 Experiments
4.1 Experiments Details
Set up. We adopt Flux 1.0 dev 21 as the pre-trained model. The dataset resolution 1024x 1024, while condition images downsampled to 512x512 to reduce memory and computation. with high-resolution control achieved via conditional token mapping. The training is conducted in two stages: the first stage fine-tunes the style LoRA for 6,000 steps of 1 x 10 and a batch size of 1. The second stage trains the consistency module from scratch for 9,000 steps 4 GPUs, with a per-GPU batch size of I (total batch size 4) and the same learning rate. In this stage, every 50 steps, a style LoRA and its corresponding data are loaded from the LoRA bank to encourage multi-style generalization.
single GPU, using a learning rate
Benchmark. To evaluate our method against baseline approaches, we propose a new image-to-image benchmark consisting of 100 images with complex visual compositions, including group portraits, animals, architectural scenes, and natural landscapes. For fair comparison, we selected 5 style LoRA models from the LibLibAI (24) website for stylization and quantitative evaluation. These styles were not included in the LoRA Bank used during training. The five styles are comic, oil painting, PVC toys, sketch, and vector style.
Baseline Methods. In this section, we introduce the baseline methods. The compared approaches include: 1. Flux mmage-to-imane pipeline (based on SDEdit) [26]: 2. Flux amage-to-image pipeline with Redux [22 Flux text-to-image pipeline with Redux; 4. Flux image-to-image pipeline with ControlNet 49 5. Flux text-to-image pipeline with ControlNet; 6. GPT-4o (1. the most advanced commercial image stylization API. For ControlNet baselines, canny and depth maps are jointly used for conditioning, with each modality weighted at 0.5 and early stopping applied at 0.5.

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Seen style LoRA
Input
AmericanCartoon Style
Input
Clay Style
Input
Ghibli style
Input
Paper Cutting Style
Input
3D Macaron style
Input
Irasutoya style
Unseen style LoRA
Input
Comics Style
Input
Oil Painting Style
Input
Doll style
Input
Sketch Style
Input
Flat Vector Style
Input
Minimalism style
Input
Futuristic Style
Fuzzy style be combined with both seen and unseen style LoRA modules to
Input
Wasteland Style
Input
Figure 3: OmniConsistency achieve high-quality image stylization consistency, effectively preserving the semantics, structure, and fine details of the original image.
4.2 Evaluation Metrics
evaluate our method from three aspects: style consistency, content consistency, and text-image alignment, using benchmark of 100 test images with captions generated by GPT-4o. All image similarity metrics are computed using DreamSim [8]. CLIP Image Score [31], and GPT--o Score. For style consistency, same LoRA to the same prompt and seed. We also compute FID 15) and CMMD [46] over 1,000 samples (generated by repeating the benchmark 10 times with different seeds) assess the impact of OmniConsistency on the style distribution. For content consistency, we measure similarity between the stylized image and the input image. For text-image alignment, we use the standard CLIP Score [4] to evaluate how well the output aligns with the input prompt.
compare the stylized result with reference generated by applying the
4.3 Quantitative Evaluation
As shown in Table I our method achieves the best performance across five style consistency metrics and ranks among the top in content consistency. It also obtains the highest CLIP Score, indicating superior text-image alignment. These results demonstrate that our consistency-awar framework effectively balances stylization fidelity, semantic preservation, and prompt alignment. In terms of content consistency, Flux 12I Redux achieves the highest CLIP Image Score; however, this advantage largely stems from its limited stylization strength and minimal visual transformation.
4.4 Qualitative Evaluation
As shown in Fig. g the T2I baseline reflects the expected stylization effect of the LoRA. The Redux method achieves reasonable stylization but suffers from poor content and structural consistency. The

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Vector styl
PVC Toys
Sketch style
Input
T21
121
121 Controinet 121 + Redux
T21 + Redux
T21 + Controinet
GPT-4o
Ours
Figure 4: Comparation results of OmniConsistency and baseline methods.
ControlNet approach preserves structural alignment well, but introduces significant style degradation. In contrast, our method simultaneously achieves high style fidelity and content consistency, producing results comparable to the state-of- oftte-of-the-rr the--ar GPT-4o.
Table I: Grouped quantitative results on style, content, and text-image consistency.
Style Consistency
Content Consistency DreamSim
Text-Img Align
Method
FID
CMMD | DreamSim
CLIP-I
GPT-4o
CLIP-I f GPT-4o
CLIP-S f
Flux 121
44.4
0.168
0.236
0.783
4.38
0.307
0.704
4.27
0.277
Flux + Redux
44.3
0.221
0.213
0.810
4,33
0.284
0.749
4.36
0.280
Flux T21 Redux
39.4
0.186
0.871
0.320
0.707
4,40
Flux 121 CN
70.0
0.736
0.265
0.761
4.14
0.315
0.742
4.48
0.290
Flux T21 CN
60.2
0.556
0.247
0.801
4.37
0.322
0.738
4.44
0.297
GPT-4o
Ours
39.2
0.145
0.181
0.875
4.64
0.278
0.741
4.52
0.321
4.5 Ablation Study
Ablation Study. We conduct ablation experiments two key design choices: (1) rolling training with multiple style LoRAs and (2) decoupled training of style and consistency. As shown in Fig. when remove rolling training and instead use single LoRA trained on mixed-style data, the generated results maintain reasonable content consistency, but show a significant degradation in stylization quality on unseen styles. Moreover, when we remove the decoupled training strategy and directly train the consistency module together with style LoRA, both stylization capability and content consistency degrade notably, indicating strong entanglement between style and structure that harms overall performance.
Table 2: Ablation study with comprehensive metrics. Metrics are grouped by style consistency, content consistency, and text-image alignment.
Style Consistency
Content Consistency
Text-Img Align CLIP-Score 1
Variant
FID
CMMD I DreamSim CLIP-I1 GPT-4o
DreamSim
CLIP-1
GPT-4o
Full Model (Ours)
39,2
0.145
0.181
0.875
4.64
0.278
0.741
4.52
0.321
Rolling LoRA
47.5
0.266
24.98
0.849
0.322
0.762
wio Decoupled Training
0.320
21.06
0.857
4.36
0.317
4.6 Discussion
We discuss the practicality and generality of OmniConsistency across three key aspects.
Plug-and-Play Integration. OmniConsistency is designed as modular, plug-and-ple component for maintaining consistency in taage-to-immage stylization. As shown in Fig, it can be seamlessly combined with text-guided stylization, community LoRAs, or snterence-basee methods like IP. Adapter.
Generalization to Unseen Styles. Thanks to the decoupled training style and consistency, along with the rolling LoRA Bank mechanism, OmniConsistency generalizes effectively to unseen style

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(a) Text-guided Image Stylization
Anime
Sevale los offeaen one the other..
On Pairting Style
Prompt
Input
Text2image
Ours
(b) Image Stylization IP-Adapter
Wector Style
Input
Full
Bolling LoRA Bank
W/o Decoupled Training
Style Image
Input
TextZinaage
Ours
Figure 5: Ablation shows that full settings ensure strong stylization and consistency, while removals degrade performance.
Figure 6: OmniConsistency is plug-and and-play and readily compatible with existing pipelines and tools like Ad-pter.
Table 3: FID and CMMD scores across 10 styles (5 seen and 5 unseen).
Metric
Styles
Styles
American Cartoon
Clay
Ghibli
Paper Cut
Van Gogh
Avg.
Comics
Oil Paint
Doll
Sketch
Voctor
Avg.
FID 1
37.6 0,220
37,9
42.2
36.4
31.3
37.08
41.3
41,8
35.9
39.9
37.0
39.18
CMMD
0.077
0.210
0.220
0.104
0.166
0.249
0.132
0.101
0.074
0.169
0.145
LoRA modules not seen during training. Fig. 3 shows qualitative examples, and Table 3 reports quantitative results (FID/CMMD) for both seen and unseen settings. Notably, there is no significant performance drop unseen LoRAs compared to seen ones, indicating that OmniConsistency is style-agnostic and maintains strong generalization across diverse styles.
High Efficiency. Under the joint effect of several optimization strategies, OmniConsistency intro- duces only marginal overhead compared the base Flux Text-to-Image pipeline, incurring just 4.6% increase in GPU memory usage and 5.3% increase in inference time at 1024 x 1024 resolution with 24 sampling steps.
Limitation
We present several failure cases in the supplementary material. Specifically, our method has difficulty preserving non-English text due to limitations the FLUX backbone, and may occasionally produce artifacts in small facial and hand regions.
6 Conclusion
hou We propose OmniConsistency, plug-and-play consistency plugin for diffusion- based stylization that achieves full decoupling between style learning and consistency learning via a two-stage training strategy. Our method preserves identity, composition, and fine- fine-grained details while generalizing well to unseen styles. It offers key advantages in plug-and-play compatibility, strong generalization, and high efficiency, making it suitable for integration with arbitrary LoRA styles without retraining. We also introduce a high-quality dataset across 22 diverse styles. Extensive evaluations demonstrate that OmniConsistency delivers state-of-the-aur performance in both consistency and stylish quality, laying a solid foundation for controllable and high-fidelity image stylization.
Acknowledgement
This project is supported by the National University of Singapore, under the Tier FY2023 Reimagine Research Scheme (RRS).

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References
[1] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Igge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint Sati:::333008774,,, 2023.
[2] Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. Stable video diffusion: Scaling latent video diffusion models to large datasets. arXiv preprint ar A.iv:2311.111222 2023.
[3] Tim Brooks, Aleksander Holynski, and Alexci A Efros. Instr Learning to follow image editing instructions. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattem Recognition. pages 18392-18402, 2023.
[4] Bolin Chen, Baoquan Zhao, Haoran Xie, Cai, Qing Li, and Xudong Mao. Consislora: Enhancing content and style consistency for lora-base bosa-based style transfer. arXiv preprint arXiv:2503. 10614, 2025.
[5] Xuewei Chen, Zhimin Chen, and Yiren Song. Transanimate: Taming layer diffusion to generate rgba video. arXiv preprint arXiv:2503.1 179344 2025.
[6] Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. In Forty-first international conference on machine leaming, 2024.
[7] Yarden Frenkel, Yael Vinker, Ariel Shamir, and Daniel Cohen-Or. Implicit style-content separation using b-lora, In European Conference on Computer Vision, pages 181-198. Springer. 2024.
[8] Stephanie Fu, Netanel Tamir, Shobhita Sundaram, Lucy Chai, Richard Zhang. Tali Dekel, and Phillip Isola. Dreamsim: Learning new dimensions of human visual similarity using synthetic data. arXiv preprint aXiv:2306.09344, 2023.
[9] Yan Gong, Yiren Song, Yicheng Li, Chenglin Li, and Yin Zhang. Relationadapter: Learning and transferring visual relation with diffusion transformers. arXiv preprint arXiv:2506.02555 2025.
[10] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63(11):139- 144, 2020.
[11] Hailong Guo, Bohan Zeng, Yiren Song. Wentao Zhang, Chuang Zhang, and Jiaming Liu. Any2anytryon: Leveraging adaptive position embeddings for versatile virtual clothing tasks. arXiv preprint 11X11 2025.
[12] Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, and Bo Dai. Animated- iff: Animate your personalized text-to-image diffusion models without specific tuning. arXiv preprint arXiv:2307004725 2023.
[13] Amir Hertz, Ron Mokady, Jay Tenenhaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to- prompt image editing with cross attention control. arXiv preprint arXiv:2208.01e 011666, 2022.
[14] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: reference-free evaluation metric for image captioning, arXiv preprint arXiv:2104.08711: 18, 2021.
[15] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems. 30, 2017.
[16] Jonathan Ho, Ajay Jain, and Picter Abbecl. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6855 1654006651,220 2020.
[17] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang. La Wang, Weizhu Chen, et al. LoRA: Low-rank adaptation of large language models. ICLR, 2022.
[18] Shijie Huang, Yiren Song. Yuxuan Zhang, Hailong Guo, Xueyin Wang. Mike Zheng Shou, and Ji- aming Liu. PhotoDoodle: Learning artistic image editing from few-shot pairwise data. arXiv preprint 143997 2025.
[19] Yuxin Jiang, Yuchao Gu, Yiren Song. Ivor Tsang, and Mike Zheng Shou, Personalized vision via visual in-context learning, arXiv preprint arXiv:2509.25172. 2025.
10

---

[20] Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman. and Jun- Yan Zhu. Multi-concept customization of text-to-image diffuusion. In Proceedings of the IEEE/CVF conference computer vision and pallern recognition, pages 1931-1941, 2023.
2024. [22] Black Forest Labs. Flux-redux. httpss//huggiinffaccccc//// foresttt-forrt-t--. -oaseeo-ttstststsosseee
[21] Black Forest Labs. Flux. http://s//gtttbb...o//aaaaa.fsse
1-Redux-dae 2024.
[23] Zhimin Li. Jianwei Zhang, Qin Lin. Jiangfeng Xiong, Yanxin Long, Xinchi Deng, Yingfang Zhang, Xingchao Liu, Minbin Huang, Zedong Xiao, et al. Hunyuan-dit: A powerful multi-rcsolution diffusion transformer with fine ine-grained chinese understanding, arXiv preprint arXiv:2405. 087488 2024.
[24] LiblibAI. Liblibai, 2025.
[25] Rumnan Lu, Yuxuan Zhang, Jiaming Liu, Haofan Wang, and Yiren Song. Easytext: Controllable diffusion transformer for multilingual text rendering. arXiv preprint axivv2505.244111 2025.
[26] Chenlin Meng, Yutong He, Yang Song. Jiaming Song, Jiajun Wa, Yan Zhu, and Stefano Ermon. Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.0101
2021.
[27] Chong Mou, Xintao Wang, Liangbin Xie. Yanze Wu, Jian Zhang, Zhongang Qi, and Ying Shan. 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, volume 38, pages 4296-4304. 2024.
[28] Zexu Pan, Zhaojie Luo, Jichen Yang, and Haizhou Li. Multi-modal attention for speech emotion recognition. arXiv preprint htt::2009004100, 2020.
[29] William Peebles and Saining Xie. Scalable diffusion models with transformers. In ICCV, 2023.
[30] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna. and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:230700192. 2023.
[31] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, al. Learning transferable visual models from natural language supervision. In ICML, 2021.
[32] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High-resolution image synthesis with latent diffusion models. CVPR, 2022.
[33] Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yacl Pritch, Michael Rubinstein, and Kfir Aberman. Dream- booth: Fine tuning text-to-image diffussin models for subject-driven generation. Proceedings of IEEE/CVF conference on computer vision and pattern recognition, pages 22500-22510, 2023.
[34] Viraj Shah, Nataniel Ruiz, Forrester Cole, Erika Lu, Svetlana Lazebnik, Yuanzhen Li, and Varun Jampani. Ziplora: Any subject in any style by effectively merging loras. In European Conference on Computer Vision, pages 422-438. Springer, 2024.
[35] Wenda Shi, Yiren Song, Zihan Rao, Dengming Zhang, Jiaming Liu, and Xingxing Zou. Wordcon: Word-level typography control in scene text rendering. arXiv preprint arXiv:2506.21276 2025.
[36] Wenda Shi, Yiren Song, Dengming Zhang. Jiaming Liu, and Xingxing Zou, Fonts: Text rendering with typography and style controls. arXiv preprint arXiv:2412.00133. 2024.
[37] Yiren Song, Danze Chen, and Mike Zheng Shou. Layertracer: Cognitive-aligned layered svg synthesis via diffusion transformer. arXiv preprint arXiv:2502.01 105, 2025.
[38] Yiren Song. Shijie Huang, Chen Yao, Xiaojun Ye, Hai Ci. Jiaming Liu, Yuxuan Zhang, and Mike Zheng Shou. Processpainter: Learn painting process from sequence data. arXiv preprint arXiv:2406. 2024.
[39] Yiren Song. Cheng Liu, and Mike Zheng Shou. Makeanything: Harnessing diffusion transformers for multi-domain procedural sequence generation. arXiv preprint arXiv:2502.011 01552, 2025.
[40] Zhenxiong Tan, Songhua Liu, Xingyi Yang, Qiaochu Xue, and Xinchao Wang. Ominicontrol: Minimal and universal control for difffusion transformer. arXiv preprint arXiv:241 15098, 2024.
[41] Zhenxiong Tan, Qiaochu Xue, Xingyi Yang. Songhua Liu, and Xinchao Wang. Ominicontrol2; Efficient conditioning for diffusion transformers. arXiv preprint 2.xv:2503.08280. 2025.
11

---

[42] Cong Wan, Xiangyang Luo, Zijian Cai, Yiren Song. Yunlong Zhao, Yifan Bai. Yuhang He, and Yihong Gong. Grid: Visual layout generation. arXiv preprint arXiv:2412.10718, 2024.
[43]
Haofan Wang. Matteo Spinelli, Qixun Wang, Xu Bai, Zekui Qin, and Anthony Chen. Instantstyle: Free lunch towards syyle-preserving in text-to-image generation. arXiv preprint arXiv:2404.02733, 2024.
[44] Zhouxia Wang, Xintao Wang, Liangbin Xie, Zhongang Qi, Ying Shan, Wenping Wang, and Ping Luo. Styleadapter: A unified stylized image generation model. arXiv preprint arXiv:2309.01770, 2023.
[45] Zongze Wu, Yotam Nizan, Eli Shechtman, and Dani Lischinski. Stylealign: Analysis and applications of aligned stylegan models. arXiv preprint urXiv:21100 10. 1323, 2021.
[46] Shifu Yan, Caihua Shan, Wenyi Yang, Bixiong Xu, Dongsheng Li, Lili Qiu, Jie Tong, and Qi Zhang. Cmmd: Cross-metric multi-dimensional root cause analysis. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 4310 11200-442.2200 2022.
[47] Ling Yang, Bohan Zeng, Jiaming Liu, Hong Li, Minghao Xu. Wentao Zhang, and Shuicheng Yan. Editworld: Simulating world dynamics for instruction-follooing image editing. arXiv preprint arXiv:2405. 14785, 2024.
[48] Ye, Zhang. Sibo Liu, Xiao Han, and Wei Yang. Ip-adapter: Text compatible image prompt adapter for text-to-image diffusion models. arXiv preprint arXiv:2308.0672) 2023.
[49] Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control text-to-image diffusion models. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3836-3847, 2023.
[50] Yuxuan Zhang, Yiren Song, Jiaming Liu, Rui Wang, Jinpeng Yu, Hao Tang, Huaxia Li, Xu Tang, Yao Hu, Han Pan, et al. Ssr-encoder: Encoding selective subject representation for subject-driven generation. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 8069-
8078, 2024.
[51] Yuxuan Zhang. Lifu Wei. Qing Zhang, Yiren Song, Jiaming Liu, Huaxia Li. Xu Tang, Yao Hu, and Haibo Zhao. Stable-makeup: When real-world makeup transfer meets diffusion model. arXiv preprint arXiv:2403.07764 2024.
[52] Yuxuan Zhang, Yirui Yuan, Yiren Song, Haofan Wang, Jiaming Liu. Easycontrol: Adding efficient and flexible control for diffusion transformer. arXiv preprint arXiv:2503.070 2025.
[53] Yuxuan Zhang, Qing Zhang, Yiren Song, and Jiaming Liu. Stable-hair: Real-world hair transfer diffusion model. arXiv preprint arXiv:2407.1400 2024.
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7 Appendix
7.1
Implementation Details of the GPT-4o Evaluation
In the GPT-4o evaluation process, establish specific metrics assess various aspects of image generation tasks. These metrics are tailored ensure comprehensive evaluation, capturing both objective scoring and comparative analysis for different types of tasks.
7.1.1 Direct Scoring Evaluation (for Style Transfer and Content Consistency Assessment)
evaluation involves assessing the quality the image generated through style transfer, considering both the consistency of the artistic style and the alignment with the original content. The scoring metrics used in this context include:
Style Consistency: This measures how well generated image reflects the artistic style reference images. The rating is provided on a scale from (highly inconsistent) to 5 (extremely consistent).
This evaluates how closely the generated image mirrors content original image, focusing on key elements such as facial features and overall layout. The scale ranges from (highly inconsistent) to 5 (highly consistent).
Content Consistency:
each aspect, assistant provides score based on a careful analysis the image characteristics. The scores are then outputted in JSON format as follows:
"style_consif wowssistnnnsnt".. {
"score": 5, "reason": "xxx"
"content consistency";
"score": 4, "reason"; "xxx"
7.1.2 Example of Task Prompt and Evaluation
Task Prompt: "Evaluate the style transfer of an image based the provided reference style images and the original content image." "
Images: [Upload images of original content image, reference style images, and generated images]
Evaluation: The assistant evaluates the generated image both Style Consistency and Content Consistency, using the following criteria:
Style Consistency: How well does generated image reflect the artistic sty le and overall atmosphere of reference style images? The rating is given on a scale from 1 (highly inconsistent) to 5 (extremely consistent).
Content Consistency: How closely does the generated image resemble the content of the original image, including key elements like facial features and the overall layout? The rating is given scale from I (highly inconsistent) to S (extremely consistent).
This dual evaluation approach, focusing both Style Consistency and Content Consistency, ensures detailed and effective assessment of quality of style transfer images generated by GPT-4o models.
7.2
User Study
7.2.1
Implementation Details
We conducted user study through questionnaire to evaluate the performance of different models in terms of style consistency and content consistency. A total of 30 questionnaires were distributed, each containing 30 questions. terms style consistency, we not directly compare with GPT-4o because does not support style LoRA injection. Instead, we approximated the desired style effects by carefully adjusting the prompts.
For each question, participants were provided with a reference image and the original image. They were then asked to select the best outputs for style consistency and content consistency from the results generated by different models (multiple selections allowed). During the analysis, each selection made for particular model
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(e) User Preference of Style Consistency
(b) User Preference of Content Consistency
28
40
90
23
13
121-Contrllnet
121-Redux
T2I+Controlnet
T2l+Redux
Ours
GPT-4a
Figure 7: User study: Preference rates for style and content consistency across methods.
(a) Stylization images containing non-English text (b) Stylization of complex scenes with multiple people
B EE
AR AF
BE
E XX
NOKIES FAMIL
NOKISS FAMIL
75:38
JTEM
Input
GPT-4o
Ours
Input
GPT-4o
Ours
Figure 8: Failure cases.
was counted one point, and the percentage score for each model was calculated based on the total number of selections. As shown in Fig. 7 our results received higher preference terms of both style consistency and content consistency.
7.2.2 Example of User Study
Question: Given the reference image and the original image, select the best outputs in terms of style consistency and content consistency from the provided options.
Style Consistency: How well does the generated image reflect artistic style and overall atmosphere of reference style images? Choose the best options from the provided images.
Content Consistency: How closely does the generated image resemble the content of the original image, including key elements such as facial features and overall layout? Choose the best options from the provided images.
7.3 Limitations and Failure Cases
We present several limitations and failure cases in Fig. S Specifically, Fig. (a) illustrates stylization results images containing Chinese text. While GPT-4o largely preserves the shape and legibility of the characters. our method struggles
maintaining the integrity of non-English text, likely backbone. Fig. 8 (b) shows stylization outcomes on group photos and complex scenes. Both our method and GPT-4o occasionally exhibit inconsistencies in the number of people depicted, often omitting individuals who occupy smaller portions of the image. Additionally, artifacts may appear small facial hand regions.
limitations
FLUX
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Comic
Oil Painting
PVC Toys
Sketch
vector
Input
121
121
121 + Redux
T21 Redux
121 + Controinet T2 - Controinet
GPT-4e
Ours
Figure 9: More Comparation results.
7.4 More Results
We present additional experimental results in this section. Fig. shows the comparative results, while Fig. 10 and Fig. demonsttaatss our method applied to wider range of styles.
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American cartoon
Clay
Ghibli
Paper cutting
Van Gogh
Input
Result
Input
Result
Input
Result
Figure 10: More image stylization results of OmniConsistency.
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3D Chibi
Chinese painting
Fabric
Irasutoya
LEGO
Clay
Origami
Home
Pixel
Low poly
Pop art
Input
Result
Input
Result
Input
Result
Figure 11: More image stylization results of OmniConsistency.
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Reviews:
[{"review_id": "PKtE3YlBaP", "reviewer": "Reviewer_bE8p", "summary": "This work achieves style transfer tasks by constructing LoRAs for 22 style types and implementing consistency-style LoRAs in a two-stage approach. First, the authors construct and collect a style LoRA bank, then train consistency LoRAs through two-stage decoupling of style and memory. The authors claim it is a plug-and-play framework, with performance comparable to GPT4O.", "questions": "1. How is the model's generalization verified, and does it essentially depend on the capabilities of the trained LoRAs?\n\n2. What is the significant difference between this approach and directly training LoRAs? How is it ensured that the consistency LoRA does not contain style information? How is this decoupling guaranteed?\n\n3. Does it support generalization to some plugins?\n\n4. The comparison with many recent style transfer methods is lacking.\n\n5. How does it support arbitrary styles?\n\n6. Is the content scoring reliable? Validation is missing.", "limitations": "See weaknesses", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength：\n\n1. The authors trained 22 LoRAs for different styles. The workload of data collection and independent fine-tuning is enormous.\n\n2. The approach proposed by the authors is reasonable, and this method is beneficial to the community.\n\n3. The article adopts a straightforward approach.\n\nWeaknesses: \n\nThis article leans more toward engineering applications, and I believe there should be some good cases. However, the challenge of style transfer lies in generalization. Beyond these common and popular styles, how to generalize to any image should be the key focus. For example, the article does not actually showcase stylization cases in natural scenes. Regarding generalization to arbitrary images, this work tends to first train a style LoRA and then a consistency LoRA. However, questions remain: how does the consistency LoRA ensure it has learned consistency? Does it truly exclude style information? The quantitative comparison lacks benchmarks against many recent style transfer works.", "was_revised": true, "final_justification": "The response addressed my concerns. Therefore, I have decided to raise my score.", "rebuttal": "# **Weaknesses**\n\n1. **“This paper is more of an engineering application; generalization and natural-scene cases are insufficient.”**  \n   **Response.** Our contribution is not an engineering workaround but an architectural solution to the long-standing consistency problem in stylization. OmniConsistency introduces a **plug-and-play, style-agnostic Consistency-LoRA** that preserves semantics, structure, and fine details for **any image × any style**. As evidence, **Figure 3** already includes group portraits, animals, architecture, vehicles, and products, demonstrating broad coverage of natural scenes. We added additional natural-scene results (e.g., street scenes, indoor scenes, and landscapes)  in the supplementary.\n\n2. **“Is the Consistency-LoRA truly style-agnostic? How do you know it doesn’t encode style?”**  \n   **Response.** Two experiments directly support the **style-agnostic** claim:  \n   - **Figure 3 (Seen vs. Unseen styles).** OmniConsistency works equally well with seen and unseen style LoRAs, producing high-quality stylization while consistently preserving content and structure.  \n   - **Table 3 (FID & CMMD across 10 styles: 5 seen / 5 unseen).** Scores on unseen styles do not degrade, indicating that the Consistency-LoRA does not entangle style and generalizes beyond the styles used during training.  \n\n---\n\n# **Questions**\n\n1. **How is the model’s generalization verified? Does it essentially depend on the capabilities of trained LoRAs?**  \n   **Response.** We evaluate on **styles and inputs that are unseen during training**. Both **Figure 3** and **Table 3** show consistent performance on unseen styles and unseen images. OmniConsistency does not rely on any particular style LoRA’s capacity; instead, the Consistency-LoRA **decouples style from consistency**, providing stable content/structure preservation regardless of which style LoRA is used.\n\n2. **What is the significant difference from directly training LoRAs? How do you ensure the Consistency-LoRA carries no style information (how is decoupling guaranteed)?**  \n   **Response.** Our goal is not to train more style LoRAs. We propose a **general consistency plugin** that can be combined with any Flux style LoRA to improve image-to-image consistency.  \n   - **Two-stage decoupling.**  \n     - **Stage I:** Train a bank of style LoRAs (22 styles). These LoRAs are then frozen.  \n     - **Stage II:** Freeze the base model and all style LoRAs, and train only the Consistency-LoRA. During training, we randomly sample styles and inputs while optimizing content/structure-preservation objectives. The Consistency-LoRA cannot encode a fixed style, and instead learns style-agnostic consistency.  \n   - **Empirical evidence.** **Figure 3** (seen/unseen styles) and **Table 3** (unseen FID/CMMD not degrading) show that the Consistency-LoRA remains style-agnostic in practice.\n\n3. **Does it support generalization to plugins?**  \n   **Response.** **Yes.** OmniConsistency is plug-and-play and is compatible with commonly used control plugins such as IP-Adapter (as shown in Figure 6) and EasyControl. We will add more combined results and analyses in the revision/supplementary.\n\n4. **The comparison with many recent style transfer methods is lacking.**  \n   **Response.** The **task setting differs**: classical “style transfer” takes a single style reference image plus a content image; our setting assumes style is provided by a style LoRA (trained on a set of images) and focuses on consistency enhancement under that usage mode. Within our setting, we already compare against **seven strong baselines**, including a **GPT-4o-based** assessment, which is sufficient to demonstrate effectiveness and superiority. To further bridge the gap, we will add **“bridging” comparisons** in the supplementary, adapting recent style-transfer methods to a comparable setup (e.g., few-shot reference to train a small style LoRA, then applying Consistency-LoRA).\n\n5. **How does it support arbitrary styles?**  \n   **Response.** Because the Consistency-LoRA is **style-agnostic** and only enforces content/structure consistency, it can be paired with any style module (e.g., any style LoRA, including newly trained few-shot/one-shot styles). Results in **Figure 3** and **Table 3** show no degradation for unseen styles.\n\n6. **Is the content scoring reliable? Validation is missing.**  \n   **Response.** We use CLIP-Image for content/structure alignment, a widely used metric for consistency/fidelity, and include a **GPT-4o evaluation** with fully reproducible prompts and settings (provided in the supplementary)."}, {"review_id": "Pa3LNtvUee", "reviewer": "Reviewer_xC1j", "summary": "The authors address the issues of content inconsistency and style degradation in style transfer by proposing a two-stage training approach, while enhancing inference efficiency through parameter reduction and feature reuse. In the first stage, they exclusively train style LoRA to capture features such as color characteristics. The second stage focuses solely on training consistency LoRA to improve content coherence. Additionally, the authors have collected an extensive dataset of style-paired images to enhance the model's robustness.", "questions": "1. The style LoRA was trained in the first stage, which means the first-stage LoRA inevitably contains content information. The second-stage consistency LoRA feels more like a correction—I wonder if removing the style LoRA would still produce highly consistent results?\n2. The content in sections 3.3 and 3.4 is very similar to EasyControl. What are the key differences?", "limitations": "1) The comparison with advanced methods, such as EasyControl, is lacking. \n2) The ablation experiments are insufficient. For example, what would happen if Style LoRA were completely removed?  \n3) The content in Sections 3.3 and 3.4 is very similar to EasyControl, and the differences need to be clarified.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths: 1) The explicit separation of style and consistency learning, which simplifies the learning process and enhances model performance; 2) Improved inference efficiency through reference image resolution reduction and feature reuse.\n\nWeaknesses: 1) Insufficient ablation studies, such as investigating the effects of removing style LoRA; 2) The content in Section 3.3 closely resembles EasyControl, which should be properly cited; 3) Lack of comparison with state of the art methods including EasyControl, In-Context LoRA, etc.", "was_revised": true, "final_justification": "The author explains the difference between the proposed method and EasyControl, and I believe its general style transfer approach is valuable for the community. Additionally, the author has addressed the concerns regarding the ablation study. Therefore, I have decided to raise my score to 4.", "rebuttal": "We thank the reviewer for the thoughtful questions and will address your concerns comprehensively.\n\n## **1) Ablation**\n\nUsing **text + Consistency-LoRA** alone already yields high-consistency stylization (see Figure 6). Figure 3 and Table 3 can also be viewed as an ablation of Style LoRA: *OmniConsistency can be combined with both seen and unseen style LoRA modules to achieve high-quality image stylization consistency, effectively preserving the semantics, structure, and fine details of the original image.* In Table 3, the FID and CMMD across 10 styles (5 seen / 5 unseen) show no degradation on unseen styles, supporting that the Consistency-LoRA is **style-agnostic** rather than tied to any specific trained style.\n\n---\n\n## **2) Differences from EasyControl**\n\n Both our method and EasyControl are built on Diffusion Transformer-based controllable generation, and we both use causal attention and positional-encoding interpolation. Concurrent work OmniControl V2 likewise adopts positional-encoding interpolation. These components are increasingly standard in DiT-based controllable pipelines; our contribution lies in **two-stage decoupling** and a **style-agnostic** consistency module that is **plug-and-play** with style LoRAs. We cite EasyControl in the main text (concurrent work), but there are clear differences:\n\n\n- Task focus. EasyControl studies DiT controllable generation with layout/subject images as conditions; it does not present a style-transfer task in the paper. Our goal is a general consistency plugin that stabilizes content/structure across any image × any style LoRA.\n- Two-stage decoupling and data requirement. Recent attempts train Ghibli style transfer with EasyControl on paired data, or build Simpsons style transfer via OmniControl—but these require collecting paired data and retraining a LoRA for each new style. In contrast, our method is plug-and-play with community Flux style models and **does not require per-style retraining** of the consistency module.\n- Efficiency. With additional inference-time optimizations, our method shows a **~40% reduction in inference time** compared to EasyControl and uses less GPU memory.\n\n---\n\n## **3) Comparison with In-Context LoRA**\n\nIn-Context LoRA targets image generation and multi-frame consistency, but **does not provide image-conditioning** (upload an image and obtain a consistent stylized output). The settings differ substantially, so a direct comparison is not applicable.\n\n---\n\n## **4) Other Questions**\n\n- “Does Stage I Style LoRA carry content information, and is Stage II just a correction?”  \n  We mitigate potential content leakage in Stage I via a rolling loading strategy and by ensuring broad category coverage when collecting training data. Stage II’s Consistency-LoRA is **not a mere correction**; the two-stage training paradigm and decoupling design are crucial to learning style-agnostic consistency.\n\n- “What happens if the Style LoRA is removed at inference?”  \n  At inference, we can **completely remove the Style LoRA** and use a text prompt to specify the style, still producing highly consistent results (see Figure 6)."}, {"review_id": "JnVhwGZnFt", "reviewer": "Reviewer_EMru", "summary": "To maintain consistent stylization across diverse image domains, this paper proposes OmniConsistency, a two-stage training approach. In the first stage, multiple style-specific LoRA modules are individually trained and stored. In the second stage, a Consistency LoRA is trained to effectively integrate these modules, ensuring the preservation of the input content.", "questions": "- What are the detailed settings for the ablations in Table 2 and Figure 5? The ablated models seem to underfit heavily.\n- I don’t understand why causal attention is better than bidirectional attention. Could you clarify the reason or show experimental comparisons?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 1, "strengths_and_weaknesses": "### Strengths\n- They propose a novel training pipeline that first trains individual style LoRAs and then trains an additional module to adapt to various styles based on conditional inputs.\n- Comprehensive comparisons are provided, and the quality of the results is impressive. Especially, the comparison with GPT-4o is interesting.\n\n### Weaknesses\n- The authors emphasize decoupled training for style consistency; however, they don't clearly justify why decoupled training is necessary compared to joint training.\n- Contributions such as the LoRA design and feature reuse lack novelty, as the proposed consistency LoRA closely resembles typical LoRA methods, and the feature reuse approach is similar to conventional KV caching.", "was_revised": true, "final_justification": "After reviewing the other reviewers’ comments and the authors’ rebuttal, I decided to raise my score to 4. I encourage the authors to include an ablation study of the consistency-LoRA method in both LoRA and full-tuning settings.", "rebuttal": "We thank the reviewer for the thoughtful feedback and will address your concerns comprehensively.\n\n## **1) Decoupled training vs. joint training**\nOur goal is to build a plug-and-play, style-agnostic consistency module that—once trained—works with any community Flux style LoRA without retraining. Decoupled training follows a first-principles design:\n- Stage I learns style in independent style LoRAs (frozen thereafter).\n- Stage II learns consistency only while style modules remain frozen and randomized, which forces the Consistency-LoRA to encode style-agnostic content/structure preservation.\n\nA joint scheme inevitably entangles consistency with specific styles and must be re-optimized when styles change, which defeats the plug-and-play objective. Our ablations confirm that decoupling yields stronger generalization to unseen styles and inputs, whereas joint training tends to overfit the seen style set.\n\n## **2) LoRA “novelty” (design and objective)**\nLoRA is a parameter-efficient fine-tuning tool, and many methods implement it similarly. The novelty in our work lies in how LoRA is trained and placed:\n- We train on paired images to learn consistency (rather than learning a specific style or subject from exemplars).\n- We introduce a two-stage decoupled pipeline that first learns style LoRAs, then learns a Consistency-LoRA with styles frozen and sampled across steps.\n- We mount the Consistency-LoRA only on the condition branch, avoiding interference with the style LoRAs at inference time and preserving each style module’s identity while enforcing content/structure stability.\n\n## **3) Feature reuse is more than KV caching**\nWhile the core mechanism leverages KV cache, the efficiency gains arise from the combined design of Conditional Token Mapping and Feature Reuse. In practice, GPU memory and latency overheads remain under 10% over standard text-to-image for our image-to-image stylization, which is substantially more efficient than OmniControl and EasyControl in comparable settings.\n\n## **4) Ablation settings (Table 2, Figure 5)**\nAll ablation experiments use exactly the same training setup and hyperparameters as the main model (training steps, batch size, optimizer, learning rate, etc.); there is no underfitting caused by configuration, so our ablations are fully fair and sound. The underfitting-like behavior observed is more likely due to joint training, which tends to learn an averaged style.\n\n## **5) Why causal attention over bidirectional attention**\n- Cacheability: with a causal mask on the conditional path, conditional keys/values remain static across diffusion steps, enabling KV caching and lowering latency/memory. Under bidirectional attention (naïve concatenation), conditional tokens attend back to evolving denoising tokens, so their states change each step and cache reuse breaks.\n- Reduced cross-condition interference: masking restricts condition↔condition attention while allowing denoiser←condition aggregation, stabilizing control when multiple conditions are present.\n- Empirical: we observe faster inference and lower memory with causal attention, while maintaining or improving quality relative to bidirectional attention."}, {"review_id": "UlQQKO8ATo", "reviewer": "Reviewer_859r", "summary": "This paper introduce a two-stage training pipeline based on DiT for image stylization. The authors first collect paired dataset with GPT-4o and train style LoRA for each artistic style. To inject the pretrained style LoRA into DiT, the authors introduce consistency LoRA module and causal attention for condition branch. A conditional token mapping and feature reuse strategy are proposed to improve the inference efficiency.", "questions": "1. Please clarify the originality of two-stage disentangled training strategy.\n2. More diverse styles would further prove the effectiveness of the method.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. The writing of this paper is good.\n2. The method achieves better results in terms of content and style consistency.\n3. The model shows good generalization on unseen LoRAs.\n\n\nWeakness:\n1. The idea of collecting dataset and training style LoRAs has been proposed in CSGO [1].  Thus, I feel that the innovation of this method is limited.\n2. The evaluation is conducted on only 100 curated images and 5 styles, many hand-filtered, which has limited diversity.\n3. The paper propose many parts (e.g., dataset, LoRA design, efficient condition) which are more or less independent. It is better to improve the integration.\n\n[1] Xing P, Wang H, Sun Y, et al. Csgo: Content-style composition in text-to-image generation[J]. arXiv preprint arXiv:2408.16766, 2024.", "was_revised": true, "final_justification": "The rebuttal addresses most of my concerns. I keep the score.", "rebuttal": "We thank the reviewer for the thoughtful feedback and will address your concerns concisely.\n\n## **Q1. Our contribution is not “building a dataset and training Style LoRAs.”**\nThe core contribution is a two-stage, style–consistency disentanglement that yields a plug-and-play, **style-agnostic** Consistency-LoRA for image-to-image stylization. To our knowledge, we are the first to explicitly target stylization consistency (content/structure preservation across styles) as a stand-alone, reusable module that attaches only to the condition branch and works with any Flux style LoRA without modifying or retraining those style LoRAs. While collecting data and training style LoRAs are common practices (e.g., CSGO), our task differs: we focus on improving i2i consistency under style LoRA usage, not on proposing another style-transfer pipeline.\n\n## **Q2. Evaluation scale and diversity**\nThe five test styles are outside the training set, and the benchmark contains 100 images, so each reported metric is computed over 500 images (100 × 5). This scale is sufficient to reduce randomness and demonstrate effectiveness. “Curated” does not imply poor diversity; when constructing the benchmark we intentionally covered varied categories (people, animals, buildings, vehicles, products, indoor/outdoor scenes) to ensure diverse content and structures.\n\n## **Q3. Originality of the two-stage disentangled training**\nOur strategy is motivated by first principles:\n- **Stage I (style):** train per-style LoRAs and then freeze them.\n- **Stage II (consistency):** train a style-agnostic Consistency-LoRA with the style LoRAs frozen and randomly sampled, encouraging content/structure preservation independent of any single style.\n\nAblations in **Table 2** and **Figure 3** show strong generalization to styles unseen during training. Our approach explicitly decouples style learning from consistency learning: in Stage II, we freeze all style LoRAs and randomly sample them while training a single Consistency-LoRA mounted only on the condition branch; causal attention, conditional token mapping, and feature reuse provide efficient conditioning. Style-swap and zero-style ablations indicate that the Consistency-LoRA does not encode a specific style, and performance on unseen styles does not degrade. In contrast, joint training tends to average styles, which weakens content/structure fidelity and reduces generalization.\n\nWe surveyed extensive related work and found no prior paper that adopts this two-stage design—freezing per-style LoRAs and training a separate style-agnostic Consistency-LoRA mounted only on the condition branch—to improve i2i stylization consistency across seen and unseen styles.\n\n## **Q4. More diverse styles / additional evidence**\nBeyond Figure 2 and the quantitative results in the supplement, we will add more styles and scenes to further stress-test diversity. We also note that the model has been downloaded thousands of times on Hugging Face, indicating practical utility and community interest; this community feedback complements our quantitative analyses."}]

Author rebuttal:

Meta-review: This paper proposes a two-stage LoRA training framework (OmniConsistency) for image generation. Most reviewers initially leaned towards a borderline rating, acknowledging the substantial engineering effort but expressing concerns about novelty and evaluation. After the rebuttal, three reviewers raised their scores, satisfied with the authors' responses regarding experimental comparisons and methodological clarification. The Area Chair thus recommends to accept the paper, but strongly encourages the authors to take the reviewers' comments into consideration when preparing their final manuscript.
Decision: Accept (poster)