USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

## Abstract

Abstract Existing literature typically treats style-driven and subject-driven generation as two disjoint tasks: the former prioritizes stylistic similarity, whereas the latter insists on subject consistency, resulting in an apparent antagonism. We argue that both objectives can be unified under a single framework because they ultimately concern the disentanglement and re-composition of content and style , a long-standing theme in style-driven research. To this end, we present USO , a U nified S tyle- S ubject O ptimized customization model. First, we construct a large-scale triplet dataset consisting of content images, style images, and their corresponding stylized content images. Second, we introduce a disentangled learning scheme that simultaneously aligns style features and disentangles content from style through two complementary objectives, style-alignment training and content–style disentanglement training. Third, we incorporate a style reward-learning paradigm denoted as SRL to further enhance the model’s performance. Finally, we release USO-Bench, the first benchmark that jointly evaluates style similarity and subject fidelity across multiple metrics. Extensive experiments demonstrate that USO achieves state-of-the-art performance among open-source models along both dimensions of subject consistency and style similarity. Code and model: https://github.com/bytedance/USO

## 1 Introduction

The significant advancements in image generation over the past years have greatly improved generative controllability, fundamentally changing how humans create images, i.e., whether through abstract textual descriptions, specific visual reference images, or both. Research on leveraging both textual and visual conditions has attracted increasing interest, giving rise to numerous real-world tasks such as style-driven generation and subject-driven generation. While textual conditions are typically explicit, visual conditions are inherently noisy, as images intrinsically embody a rich spectrum of features (e.g., style, appearance), of which only a specific one is relevant to a specific task.

Figure: Figure 1: Showcase of the versatile abilities of the USO model. Prompts are in [Table˜4](https://arxiv.org/html/2508.18966v1#Sx2.T4).
Refer to caption: x1.png

For instance, style-driven generation requires only the style feature from the reference images, whereas other features constitute noise. Therefore, a fundamental and long-standing challenge in these tasks is to accurately include all required features from the reference image while simultaneously excluding other noisy ones, e.g., including only the style in style-driven generation or only the subject’s appearance in subject-driven generation.

Figure: Figure 2: Illustration of our motivation. By jointly disentangling content and style across tasks, we unify style-driven and subject-driven generation within a single framework.
Refer to caption: x2.png

Extensive efforts in the literature have been dedicated to disentangling different features in visual conditions (i.e., reference images). On the one hand, in the realm of style-driven generation, DEADiff [23] employs QFormer to selectively query only the style features from reference images. CSGO [37] constructs content-style-stylized triplets to facilitate style-content decoupling during training. StyleStudio [16] introduces style-based classifier-free guidance (SCFG) to enable selective control over stylistic elements and to mitigate the influence of irrelevant features. On the other hand, subject-driven generation methods primarily focus on disentangling subject appearance features or constructing more effectively disentangled paired data. For example, RealCustom [10, 19] proposes a dual-inference framework that selectively incorporates subject-relevant features into subject-specific regions. UNO [34] leverages the in-context capabilities of DiT to progressively improve both the quality of paired data and the model itself. To conclude, existing methods primarily focus on task-specific disentanglement by designing tailored datasets or model architectures for each individual task, thereby performing disentanglement in an isolated, single-task context.

In this study, we argue that a more comprehensive and precise disentanglement approach should fully account for the coupling and complementarity between different generation tasks. Each task should not only learn which features to include, but, more importantly, also learn which features to exclude, i.e., features that are often required by other tasks. Therefore, learning to include certain features in one task inherently informs and enhances the process of learning to exclude those same features in a complementary task, and vice versa. For example, style-driven generation aims to incorporate stylistic features while excluding subject appearance features, whereas subject-driven generation does the exact opposite. The ability to learn and include subject appearance features in subject-driven generation can, in turn, help style-driven generation more effectively learn to exclude these features, thereby improving disentanglement for both tasks. In conclusion, we believe that jointly modeling complementary tasks enables a mutually reinforcing disentanglement process, leading to a more precise separation of relevant and irrelevant features for each task.

Based on the above motivation, we propose a novel cross-task co-disentanglement paradigm to unify subject-driven and style-driven generation, and, more importantly, to mutually enhance the performance of both tasks, as illustrated in  [Figure˜2](https://arxiv.org/html/2508.18966v1#S1.F2). Specifically, this co-disentanglement paradigm is implemented through a subject-for-style data curation framework and a style-for-subject model training framework. The subject-for-style framework first utilizes a state-of-the-art subject model to generate high-quality style data, while the style-for-subject framework subsequently trains a more effective subject model under the guidance of style rewards and disentangled training.
Technically, on the one hand, for the subject-for-style data curation framework, we build upon a state-of-the-art subject-driven model [34] and further develop both a stylization expert and a de-stylization expert to curate stylized and non-stylized images. This process ultimately constructs triplet data pairs in the form of $<$style reference, de-stylized subject reference, stylized subject result$>$ for subsequent model training. On the other hand, for the style-for-subject model training framework, we propose a Unified Style-Subject Optimized (USO) customization model, which introduces progressive style alignment and style-subject disentanglement training, both supervised by a style reward.

Our contributions are summarized as follows:

Concepts: We point out that existing style-driven and subject-driven methods focus solely on isolated disentanglement within each task, neglecting their potential complementarity and thus leading to suboptimal disentanglement. For the first time, we propose a novel cross-task co-disentanglement paradigm that unifies style-driven and subject-driven tasks, enabling mutual enhancement and achieving significant performance improvements for both.

Technique: We present a novel cross-task triplet curation framework that bridges style-driven and subject-driven generation. Building on this, we introduce USO, a unified customization architecture that incorporates progressive style-alignment training, content–style disentanglement training, and a style reward learning paradigm to further promote cross-task disentanglement. We further release USO-Bench, to the best of our knowledge, the first benchmark tailored for evaluating cross-task customization.

Performance: Extensive evaluations on USO-Bench and DreamBench [25] show that USO achieves state-of-the-art results on subject-driven, style-driven, and joint style-subject-driven tasks, attaining the highest CLIP-T, DINO, and CSD scores. USO can handle individual tasks and their free-form combinations while exhibiting superior subject consistency, style fidelity, and text controllability as shown in  [Figure˜1](https://arxiv.org/html/2508.18966v1#S1.F1).

## 2 Related Work

### 2.1 Style Transfer

Style Transfer aims to apply the style in the reference image to the given content image or fully generated image.
Early work like adaptive instance normalization [11] achieved impressive style transfer results with layout-preserved results by simply using a pre-trained network as the style encoder and well-designed injection modules.

The recent powerful text-to-image generation base models, like Stable Diffusion [21, 4] and FLUX [14], along with style transfer plugins built upon them, have significantly improved the convenience and effectiveness of performing this task.
Several are even training-free, like StyleAlign [35] and StylePrompt [12] which transfer the style via simple query-key swapping in the specific self-attention layers.
Other training-based methods can theoretically achieve better fitting and style transfer performance, but they also raise concerns of content leakage.
IP-adapter [39] and DEADiff [23] demonstrate the style transfer ability with a new decoupled cross-attention layer trained with coupled data, and overcome the content leakage by decreasing the injection weights in inference-time.
InstanceStyle [28], StyleShot [7] and B-lora [5] provide more detailed time-aware and layer-aware injection strategies to disentangle the style and content feature injections.
However, those disentanglement analyses are tied to the specific model architecture and hard to migrate.

### 2.2 Subject-Driven generation

Subject-driven generation refers to generating images of the same subject conditioned on a text instruction and reference images of given subjects.
Dreambooth [25] and IP-Adapter [39] turn a UNet-based text-to-image model into a subject-driven model by parameter-efficient tuning or a newly introduced attention plug-in.
Recently, popular image-generation foundation models have shifted from UNet-based architectures to transformer-based ones.
The inherent in-context learning capabilities of transformers have greatly enriched research on subject-driven generation. ICLoRA [9], OmniControl [27], UNO [34], and FLUX.1 Kontext [15] use shared attention between the generated image and reference image to train a text-to-image DiT into a subject-driven variant.
It is worth noting that some of them have extended the reference subject to other types.
OmniControl [27] supports layout control image as a reference, UNO [34] supports multiple reference images input, and DreamO [20] can work for simple style transfer.
They have indicated that various types of reference-guided generation can be unified within the DiT in-context framework.
This further prompts the question of whether jointly addressing different tasks in this setting could lead to mutual benefits across them.

## 3 Methodology

### 3.1 Preliminary

Latent diffusion models [24, 21] have evolved from UNet-based architectures to DiT-based designs, with steadily improving foundational capabilities. MM-DiT [4, 14] further elevates image-generation quality, spawning numerous downstream applications and unlocking greater controllability in text-to-image generation [34]. It incorporates a multi-modal attention mechanism that can be seamlessly extended to an in-context generation framework: the conditioned tokens are directly concatenated with the text prompt and the noisy latent, yielding the formulation:

$$ $\operatorname{Attention}\left([c,z_{t},z_{c}]\right)=\operatorname{softmax}\left(\frac{\mathbf{Q}\mathbf{K}^{\top}}{\sqrt{d}}\right)\mathbf{V},$ (1) $$

where $Z=[c,z_{t},z_{c}]$ denotes the concatenation of text tokens, noisy latent, and condition tokens. This allows both representations to function within their own respective spaces while still taking the other into account.

Figure: Figure 3: Illustration of our proposed cross-task triplet curation framework, which systematically generates layout-preserved and layout-shifted triplets.
Refer to caption: x3.png

### 3.2 Cross-Task Triplet Curation Framework

This section details the construction of cross-task triplets for USO training. Although prior works [37, 29] have explored triplet generation, they retain the original layout, preventing any pose or spatial re-arrangement of the subject. To jointly enable subject-driven and style-driven generation beyond simple instruction-based edits, we curate a new USO dataset expressly designed for this unified objective.

[Figure˜3](https://arxiv.org/html/2508.18966v1#S3.F3) provides an overview of USO dataset. Our co-disentanglement paradigm starts from a subject-for-style data curation framework. Among many possible tasks, subject-driven (i.e., UNO-1M [34]) and instruction-based editing (i.e., X2I2 [32]) datasets are comparatively easy to collect at scale, enabling targeted task-specific corpora. In particular, subject-driven data emphasizes learning from content cues while preserving subject identity and consistency; instruction-based editing bridges styles by preserving spatial layout and transferring appearance between realistic and stylized domains in both directions. These resources naturally support training domain-specialist models and, through deliberate dataset design, induce the capabilities we care about (e.g., extracting task-relevant features conditioned on image type). Guided by these insights, we curate $200k$ stylized image pairs sourced from publicly licensed datasets and augmented with samples synthesized by state-of-the-art text-to-image models. Using these data, we trained two complementary experts on top of the leading customization framework UNO [34]:
(1) a stylized expert model that performs style-driven generation conditioned on a style-reference image, producing a new subject rendered in the target style ($I_{ref}^{s}$ from $I_{tgt}$), and (2) a de-stylization expert model that inverts a stylized image to a photorealistic counterpart, allowing either flexible layout shifts or preservation ($I_{ref}^{c}$ from $I_{tgt}$).

Each curated stylized image serves as the target $I_{tgt}$. We synthesize its style reference $I^{s}_{ref}$ via the stylization expert and its content reference $I^{c}_{ref}$ via the de-stylization expert. Following [34], a VLM-based filter enforces style similarity between $I_{tgt}$ and $I^{s}_{ref}$ and subject consistency between $I_{tgt}$ and $I^{c}_{ref}$. This yields two kinds of triplets, shown in  [Figure˜3](https://arxiv.org/html/2508.18966v1#S3.F3): layout-preserved and layout-shifted. Unlike prior work [37, 29], which focuses solely on style-driven generation and confines itself to layout-preserved triplets, our cross-task triplets achieve deeper content–style disentanglement across tasks and are used to train USO.

Figure: Figure 4: Illustration of the training framework of USO. USO unifies subject-driven and style-driven generation in two stages: Stage 1 aligns SigLIP embeddings via style-alignment training to yield a style-capable model; Stage 2 disentangles the conditional encoders and trains on triplets to enable the joint conditional generation. Finally, a style-reward learning paradigm supervises both stages to yield a stronger unified model.
Refer to caption: x4.png

### 3.3 Unified Customization Framework (USO)

In this section, we describe how we unify two tasks that have traditionally been treated separately, style-driven and subject-driven generation, into a single model. Each task demands the model to master distinct knowledge: the former emphasizes style similarity, while the latter insists on subject consistency. By excelling at both simultaneously, the model naturally disentangles content from style, a long-standing focus of style-driven generation, which in turn boosts the quality of stylization and customization. Beyond merely preserving layout during style-driven generation, the model can now freely recombine any subject with any style.

#### 3.3.1 Style Alignment Training.

As illustrated in  [Figure˜4](https://arxiv.org/html/2508.18966v1#S3.F4), We start from a pre-trained text-to-image (T2I) model and fine-tune it into a stylized variant via our proposed style-alignment training. Unlike prior in-context generation approaches that rely solely on a VAE $\mathcal{E}(\cdot)$ to encode the conditioned image $I_{ref}$, we argue that style is a more abstract cue demanding richer semantic information. Therefore, we employ the semantic encoder SigLIP instead of the VAE to process the reference style image $I^{s}_{ref}$. While subject-driven or identity-preserving tasks typically emphasize high-level semantics, style-driven tasks must simultaneously handle two extremes: high-level semantics to accommodate large geometric deformations (e.g., 3-D cartoon styles) and low-level details to reproduce subtle brushstrokes (e.g., pencil sketches). Following recent works like [41], we introduce a lightweight Hierarchical Projector $\mathcal{M}_{\text{Proj}}(\cdot)$ to project multi-scale, fine-grained visual features $z_{s}$ from the extracted SigLIP embeddings $\{c_{i}\}_{i=1}^{N}$, where $N$ represents the layer indices of SigLIP. This process can be formulated as:

$$ $z_{s}=\operatorname{Concatenate}(\mathcal{M}_{\text{Proj}}(\{c_{i}\}_{i=1}^{N})),$ (2) $$

Specifically, we assign the style tokens $z_{s}$ the same positional indices as the text tokens $c$, then feed the fused multimodal sequence $z_{1}$ into the DiT model as its input:

$$ $z_{1}=\operatorname{Concatenate}(z_{s},c,z_{t}),$ (3) $$

In this stage, we freeze all parameters except the Hierarchical Projector, enabling the extracted style features to be rapidly aligned with the native textual distribution. Consequently, the pretrained T2I model is converted into a stylized variant capable of accepting style-reference images as conditional input.

#### 3.3.2 Content-Style Disentanglement Training.

Building on Stage 1, we introduce subject conditioning in Stage 2 as shown in [Figure˜4](https://arxiv.org/html/2508.18966v1#S3.F4). Following recent paradigms [27, 34], the content image $I^{c}_{ref}$ is encoded into pure conditional tokens $z_{c}$ by a frozen VAE encoder $\mathcal{E}(\cdot)$. We formulate USO as a multi-image conditioned model, yet explicitly disentangle content and style features via separate encoders. This design alleviates content leakage, where extraneous style-image details undesirably appear in the output, and also helps the model learn to exclude undesired features for the specific task, as introduced in [Section˜1](https://arxiv.org/html/2508.18966v1#S1).

During training, the Hierarchical Projector remains frozen while the DiT parameters are unfrozen. Content tokens receive positional indices via UnoPE [34] in its diagonal layout. The final multimodal input sequence $z_{2}$ is thus expressed as:

$$ $z_{2}=\operatorname{Concatenate}(z_{s},c,z_{t},z_{c}),$ (4) $$

Consequently, USO can directly handle both subject-driven and style-driven tasks on the proposed triplet dataset.

Compared with prior open-source style-driven methods, most of which either (i) rigidly preserve the content layout while altering its style [29] or (ii) retain layout via an external ControlNet at the cost of subject consistency with the content image [23, 28]-USO removes these constraints. Trained on our triplet data, it freely re-positions the subject from the content image into any scene while re-rendering it in the style of the reference image.

#### 3.3.3 Style Reward Learning

Beyond the standard flow-matching objective, we introduce Style Reward Learning (SRL) to explicitly disentangle style from content during optimization. Flow-matching pre-trains the model by minimizing the L2 distance between the predicted velocity $\bm{v}_{\theta}(\bm{x}_{t},t)$ and the true velocity $\bm{v}_{t}=\frac{d\alpha_{t}}{dt}\bm{x}_{0}+\frac{d\sigma_{t}}{dt}\epsilon$. Building on this, we denote the training objective as $\mathcal{L}_{\text{Pre}}$, which can be computed as:

$$ $\mathcal{L}_{\text{Pre}}=\mathbb{E}_{\bm{x}_{0},t,\epsilon}[w(t)\|\bm{v}_{\theta}-\bm{v}_{t}\|^{2}]$ (5) $$

where $w(t)$ is a weighting function, $\bm{v}_{\theta}$ is a neural network parameterized by $\theta$, and $\alpha_{t}=1-t,\sigma_{t}=t$ are continuous-time coefficients with $t\in[0,T=1]$. The sampling process is from $t=T$ with $\bm{x}_{T}\sim\mathcal{N}(\bm{0},\bm{I})$ and stops at $t=0$, solving the PF-ODE via $d\bm{x}_{t}=\bm{v}_{\theta}(\bm{x}_{t},t)dt$.

Extending ReFL [38] from T2I generation to the reference-to-image setting, where generation is conditioned on both an image reference and and its corresponding text prompt, SRL alternates between computing a reward score and back-propagating the reward signal. As shown in  [Figure˜4](https://arxiv.org/html/2508.18966v1#S3.F4), we define the reward score as the style similarity between the reference style image $I^{s}_{ref}$ and the generated stylized image $I_{\theta}$, measured by either a VLM-based filter or the CSD model $\mathcal{M}_{\text{RM}}(\cdot)$ [26, 37]. The reward loss is defined as:

$$ $\mathcal{L}_{\text{SRL}}=\mathbb{E}_{y_{i}\sim\mathcal{Y}}[\phi(\mathcal{M}_{\text{RM}}(y_{i},I_{\theta}(y_{i})))]$ (6) $$

where $\mathcal{Y}=\{y_{i}\}^{n}_{i=1}$ is the prompt set, $\phi$ maps reward scores to per-sample loss values, and $I_{\theta}$ denotes the image generated by the diffusion model with parameters $\theta$ corresponding to prompt $y$.

The final objective combines both losses:

$$ $\mathcal{L}=\mathcal{L}_{\text{Pre}}+\lambda\mathcal{L}_{\text{SRL}},\quad\lambda=0\text{ before step }S,\ \lambda=1\text{ thereafter}.$ (7) $$

As shown in  [Algorithm˜1](https://arxiv.org/html/2508.18966v1#alg1), we present the detailed SRL algorithm. The entire process comprises gradient-free inference followed by a reward-backward step.

Figure: Algorithm 1 Style Reward Learning (SRL) with Flow Matching

Figure: Figure 5: Qualitative comparison with different methods on subject-driven generation.
Refer to caption: x5.png

Figure: Figure 6: Qualitative comparison with different methods on style-driven generation.
Refer to caption: x6.png

Figure: Figure 7: Qualitative comparison with different methods on identity-driven generation.
Refer to caption: x7.png

Figure: Figure 8: Qualitative comparison with different methods on style-subject-driven generation.
Refer to caption: x8.png

## 4 Experiments

### 4.1 Experiments Setting

USO Unified Benchmark. To enable a comprehensive evaluation, we introduce USO-Bench, a unified benchmark built from $50$ content images ($20$ human-centric, $30$ object-centric) paired with $50$ style references. We further craft $30$ subject-driven prompts that span pose variation, descriptive stylization, and instructive stylization, along with $30$ style-driven prompts. We generate four images per prompt for both subject-driven and style-driven tasks, and a single image for the combined style-subject-driven task. This yields $6000$ samples for subject-driven generation, $7040$ for style-driven generation, and $29500$ for the combined task; full construction details are provided in the supplementary material.

**Table 1: Quantitative results for subject-driven and style-driven generation on USO-Bench.**
| Model | Subject-driven generation | Style-driven generation |  |  |  |
| --- | --- | --- | --- | --- | --- |
|  | CLIP-I$\uparrow$ | DINO$\uparrow$ | CLIP-T$\uparrow$ | CSD$\uparrow$ | CLIP-T$\uparrow$ |
| RealCustom++ [10] | 0.314 | 0.615 | 0.303 | - | - |
| RealGeneral [18] | 0.485 | 0.732 | 0.275 | - | - |
| UNO [34] | 0.605 | 0.789 | 0.264 | - | - |
| BAGEL [3] | 0.516 | 0.741 | 0.298 | - | - |
| OmniGen2 [32] | 0.475 | 0.723 | 0.302 | - | - |
| FLUX.1 Kontext dev [15] | 0.579 | 0.775 | 0.287 | - | - |
| Qwen-Image Edit [31] | 0.544 | 0.756 | 0.302 | - | - |
| DEADiff [23] | - | - | - | 0.462 | 0.274 |
| InstantStyle-XL [28] | - | - | - | 0.540 | 0.276 |
| CSGO [37] | - | - | - | 0.452 | 0.272 |
| StyleStudio [16] | - | - | - | 0.348 | 0.282 |
| DreamO [20] | 0.588 | 0.787 | 0.280 | 0.454 | 0.278 |
| USO (Ours) | 0.623 | 0.793 | 0.288 | 0.557 | 0.282 |

**Table 2: Quantitative results for style-subject-driven driven generation on USO-Bench.**
| Model | CSD$\uparrow$ | CLIP-T$\uparrow$ |
| --- | --- | --- |
| StyleID [2] | 0.407 | 0.230 |
| OmniStyle [29] | 0.365 | 0.229 |
| USO (Ours) | 0.495 | 0.283 |

Evaluation Metrics. For quantitative evaluation, we assess each task along three dimensions: (1) subject consistency, measured by the cosine similarity of CLIP-I and DINO embeddings following [34]; (2) style similarity, reported via the CSD score [26] for both style-driven and style-subject-driven generation, following [37]; and (3) text–image alignment, evaluated with CLIP-T across all three tasks.

Comparative Methods. As a unified customization framework, USO is evaluated against both task-specific and unified baselines. For subject-driven generation, we benchmark RealCustom++ [19], RealGeneral [18], UNO [34], OmniGen2 [32], BAGEL [3], FLUX.1 Kontext dev [15], and Qwen-Image Edit [31]. For style-driven generation, we compare StyleStudio [16], DreamO [20], CSGO [37], InstantStyle [28], and DEADiff [23]. For the joint style-subject-driven setting with dual conditioning, we compare OmniStyle [29] and StyleID [2]. We also compared with InfiniteYou [13] to further demonstrate the positive effect of our proposed method on identity tasks.

### 4.2 Experimental Results

Subject-Driven Generation. As shown in  [Figure˜5](https://arxiv.org/html/2508.18966v1#S3.F5), the first two columns demonstrate that USO simultaneously satisfies both descriptive and instructive style edits while maintaining high subject consistency. In contrast, competing methods either fail to apply the style or lose the subject. The last two columns further illustrate USO’s strength in preserving human appearance and identity; it adheres strictly to the textual prompt and almost perfectly retains facial and bodily features, whereas other approaches fall short. When the prompt is “The man is reading a book in a cafe”, FLUX.1 Kontext dev [15] achieves decent facial similarity but carries copy-paste risks. In [Figure˜7](https://arxiv.org/html/2508.18966v1#S3.F7) we compare with task-specific identity-preserving methods; USO produces more realistic, non-plastic results with higher identity consistency. As reported in [Table˜1](https://arxiv.org/html/2508.18966v1#S4.T1), USO significantly outperforms prior work, achieving the highest DINO and CLIP-I scores and a leading CLIP-T score.

Style-Driven Generation.  [Figure˜6](https://arxiv.org/html/2508.18966v1#S3.F6) shows that USO outperforms task-specific baselines in preserving the original style, including global color palettes and painterly brushwork. In the last two columns, given highly abstract references such as material textures or Pixar-style renderings, USO handles them almost flawlessly while prior methods struggle, demonstrating the generalization power of our cross-task co-disentanglement. Quantitatively,  [Table˜1](https://arxiv.org/html/2508.18966v1#S4.T1) confirms that USO achieves the highest CSD and CLIP-T scores among all style-driven approaches.

Style-Subject-Driven Generation. As illustrated in  [Figure˜8](https://arxiv.org/html/2508.18966v1#S3.F8), we evaluate USO on both layout-preserved and layout-shifted scenarios. When the input prompt is empty, USO not only preserves the original layout of the content reference but also delivers the strongest style adherence. In the last two columns, under a more complex prompt, USO simultaneously preserves the subject and identity consistency, matches the reference style, and aligns with the text, while other methods lag markedly and merely adhere to the text. [Table˜2](https://arxiv.org/html/2508.18966v1#S4.T2) corroborates these observations, showing USO achieves the highest CSD and CLIP-T scores and substantially outperforms all baselines.

User Study. We further conduct an online user-study questionnaire to compare state-of-the-art subject-driven and style-driven methods. Questionnaires were distributed to both domain experts and non-experts, who ranked the best results for each task. (1) Subject-driven tasks were evaluated on text fidelity, visual appeal, subject consistency, and overall quality. (2) Style-driven tasks were judged on text fidelity, visual appeal, style similarity, and overall quality.
As shown in  [Figure˜9](https://arxiv.org/html/2508.18966v1#S4.F9), our USO achieves top performance on both tasks, validating the effectiveness of our cross-task co-disentanglement and showcasing its capability to deliver state-of-the-art results.

Figure: Figure 9: Radar charts of user evaluation of methods for subject-driven and style-driven generation on different dimensions.
Refer to caption: x9.png

## 5 Ablation Study

### 5.1 Effect of Style Reward Learning (SRL).

For style-driven task: As shown in [Figure˜10](https://arxiv.org/html/2508.18966v1#S5.F10), the last three columns reveal a clear boost in style similarity for both style-driven and style-subject-driven tasks; the stroke textures and painting style closely match the reference images, confirming the effectiveness of our style reward learning.

For subject-driven task: In the first three and final columns of [Figure˜10](https://arxiv.org/html/2508.18966v1#S5.F10), we observe a notable improvement in subject and identity consistency, with more uniform details and higher facial similarity.

As shown in [Figure˜12](https://arxiv.org/html/2508.18966v1#S5.F12), removing SRL leads to a sharp drop in the CSD score and simultaneous declines in CLIP-I and CLIP-T. Notably, we rely solely on style reward and introduce no identity-specific data; nevertheless, the unified model benefits in content consistency. By sharpening the model’s ability to extract and retain desired features, SRL yields an overall improvement across all tasks, strongly validating our motivation. Beyond gains in subject and identity fidelity, we observe a noticeable enhancement in aesthetic quality (e.g., texture as in VMix [33]) and a marked reduction in the plastic artifact, which is an issue that has long challenged text-to-image generation [33]. Through SRL training, the model exhibits emerging properties even in tasks not explicitly targeted during training.

Figure: Figure 10: Ablation study of SRL. The blue boxes denote content reference and the purple boxes denote style reference. Prompts are "A toy with a mountain in the background.", "The man on the beach.", "The woman is skateboarding on the street.", "A beautiful woman.", "A beautiful woman.", "The woman gave an impassioned speech on the podium." from left to right.
Refer to caption: x10.png

Figure: Figure 11: Ablation study of different components proposed in USO.

### 5.2 Effect of Style Alignment Training (SAT).

Removing SAT and instead jointly fine-tuning both SigLIP and DiT from scratch degrades CLIP-T on subject-driven tasks and lowers CSD on style-subject-driven tasks ( [Figure˜12](https://arxiv.org/html/2508.18966v1#S5.F12)). Qualitatively,  [Figure˜13](https://arxiv.org/html/2508.18966v1#S5.F13) shows that the oil-painting style of the “cheetah” example becomes noticeably weaker.

Figure: Figure 13: Ablation study of USO. Zoom in for details.
Refer to caption: x11.png

### 5.3 Effect of Disentangled Encoder (DE).

Replacing the disentangled encoders with a single VAE for both style and content images harms nearly every metric ( [Figure˜12](https://arxiv.org/html/2508.18966v1#S5.F12)). Visually, the “cheetah” reverts to a more photorealistic appearance, while the man’s identity suffers a marked loss ( [Figure˜13](https://arxiv.org/html/2508.18966v1#S5.F13)).

### 5.4 Effect of Hierarchical Projector.

[Figure˜12](https://arxiv.org/html/2508.18966v1#S5.F12) shows that the hierarchical projector yields the highest CSD and a leading CLIP-T score, substantially benefiting style-alignment training.

## 6 Conclusion

In this paper, we present USO, a unified framework capable of subject-driven, style-driven, and joint style-subject-driven generation. We introduce a cross-task co-disentanglement paradigm that first constructs a systematic triplet-curation pipeline, then applies progressive style-alignment and content–style disentanglement training on the curated triplets. Additionally, we propose a style-reward learning paradigm to further boost performance. To comprehensively evaluate our method, we construct USO-Bench, a unified benchmark that provides both task-specific and joint evaluation for existing approaches. Finally, extensive experiments demonstrate that USO sets new state-of-the-art results on subject-driven, style-driven, and their joint style-subject-driven tasks, exhibiting superior subject consistency, style fidelity, and text controllability.

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## USO: Unified Style and Subject-Driven Generation via Disentangled and Reward Learning

## Appendix

### F.1 Experiments Setting

#### F.1.1 Implementation Details.

We begin with FLUX.1 dev [14] and the SigLIP [40] pretrained model. For style alignment stage, we train on pairs $\{I^{s}_{ref},I_{tgt}\}$ for $23,000$ steps at batch size $16$, learning rate $8e-5$, resolution $768$ and reward steps $S=16,000$. For content-style disentanglement stage, we train on triplets $\{I^{c}_{ref},I^{s}_{ref},I_{tgt}\}$ for $21,000$ steps at batch size $64$, learning rate $8e-5$, resolution $1024$ and reward steps $S=18,000$. LoRA [8] rank $128$ is used throughout.

Figure: Figure 14: Examples of USO-Bench.
Refer to caption: x12.png

#### F.1.2 Details of USO-Bench.

USO-Bench is built to evaluate subject-driven, style-driven, and joint style-subject-driven generation. As shown in  [Figure˜14](https://arxiv.org/html/2508.18966v1#Sx2.F14), each subject-driven sample uses three prompt types: descriptive, instructive-stylization, and descriptive-stylization. By pairing these prompts with style-reference images from style-driven tasks, we obtain style-subject-driven samples via their Cartesian product. The resulting prompts are further split into layout-shifted and layout-preserved variants.

**Table 3: Quantitative results for single-subject driven generation on Dreambench [25].**
| Method | DINO $\uparrow$ | CLIP-I $\uparrow$ | CLIP-T $\uparrow$ |
| --- | --- | --- | --- |
| Oracle(reference images) | 0.774 | 0.885 | - |
| Textual Inversion [6] | 0.569 | 0.780 | 0.255 |
| DreamBooth [25] | 0.668 | 0.803 | 0.305 |
| BLIP-Diffusion [17] | 0.670 | 0.805 | 0.302 |
| ELITE [30] | 0.647 | 0.772 | 0.296 |
| Re-Imagen [1] | 0.600 | 0.740 | 0.270 |
| BootPIG[22] | 0.674 | 0.797 | 0.311 |
| SSR-Encoder[41] | 0.612 | 0.821 | 0.308 |
| RealCustom++ [10, 19] | 0.702 | 0.794 | 0.318 |
| OmniGen [36] | 0.693 | 0.801 | 0.315 |
| OminiControl [27] | 0.684 | 0.799 | 0.312 |
| FLUX.1 IP-Adapter | 0.582 | 0.820 | 0.288 |
| UNO [34] | 0.760 | 0.835 | 0.304 |
| USO (Ours) | 0.777 | 0.838 | 0.317 |

### F.2 More Results

#### F.2.1 Quantitative Evaluation on DreamBench [ 25 ] .

To further assess USO, we evaluate it on DreamBench [25] in addition to USO-Bench. Following UNO [34], we generate six images per prompt, yielding 4,500 image groups across all subjects. As shown in  [Table˜3](https://arxiv.org/html/2508.18966v1#Sx2.T3), USO achieves the highest CLIP-I and DINO scores, and with a CLIP-T score of 0.317, it trails the top result (0.318) by only a narrow margin. These results demonstrate USO’s superior subject consistency among state-of-the-art methods.

**Table 4: Text prompts used in [Figure˜1](https://arxiv.org/html/2508.18966v1#S1.F1).**
| Scenarios | Prompt |
| --- | --- |
| Subject/Identity Driven Generation | (1) "The girl is riding a bike in the street." |
|  | (2) "The man is driving a car in the street." |
|  | (3) "A sophisticated gentleman exuding confidence. He is dressed in a 1990s brown plaid jacket with a |
|  | high collar, paired with a dark grey turtleneck. His trousers are tailored and charcoal in color, |
|  | complemented by a sleek leather belt. The background showcases an elegant library with |
|  | bookshelves, a marble fireplace, and warm lighting, creating a refined and cozy atmosphere. |
|  | His relaxed posture and casual hand-in-pocket stance add to his composed and stylish demeanor" |
|  | (4) "The woman is reading a book in a cafe." |
| Subject/Identity Driven Stylization | (1) "Sketch style, a bowl with a mountain in the background." |
|  | (2) "Illustration style, a dog on the beach." |
|  | (3) "Transform to Picasso’s style of work, Cubism." |
|  | (4) "Ghibli style, The woman rides a deer in the forest." |
| Style Driven Generation | (1)"A shark." |
|  | (2) "Small boat in the lake." |
|  | (3) "A beautiful woman." |
|  | (4) "The top chef is stir-frying in the kitchen." |
| Multi-style Driven Generation | (1) "A beautiful woman." |
|  | (2) "A duck, with words read "USO", "inspires creativity"." |
|  | (3) "A man." |
| Style-subject Driven Generation | (1) "" |
| (Layout-preserved) | (2) "" |
|  | (3) "" |
| Style-subject Driven Generation | (1) "A toy in the jungle." |
| (Layout-shifted) | (2) "A cat on the beach." |
|  | (3) "The woman gave an impassioned speech on the podium." |

Figure: Figure 15: More results on subject-driven generation.
Refer to caption: x13.png

Figure: Figure 16: More results on subject-driven generation.
Refer to caption: x14.png

Figure: Figure 17: More results on identity-driven generation.
Refer to caption: x15.png

Figure: Figure 18: More results on identity-driven generation.
Refer to caption: x16.png

Figure: Figure 19: More results on style-driven generation.
Refer to caption: x17.png

Figure: Figure 20: More results on style-driven generation.
Refer to caption: x18.png

Figure: Figure 21: More results on style-subject-driven generation. We set prompt to empty for layout-preserved generation.
Refer to caption: x19.png

Figure: Figure 22: More results on style-subject-driven generation. USO supports any subject combined with any style in any scenario.
Refer to caption: x20.png

#### F.2.2 Additional Results.

We present additional qualitative results from USO:

- •
From [Figures˜15](https://arxiv.org/html/2508.18966v1#Sx2.F15), [16](https://arxiv.org/html/2508.18966v1#Sx2.F16), [17](https://arxiv.org/html/2508.18966v1#Sx2.F17) and [18](https://arxiv.org/html/2508.18966v1#Sx2.F18), USO demonstrates the ability to extract task-relevant content features while maintaining subject consistency across diverse textual prompts—capabilities that prior work typically treats as isolated tasks (e.g., subject-driven generation, instruction-based stylized editing, and identity preservation).
- •
In [Figures˜19](https://arxiv.org/html/2508.18966v1#Sx2.F19) and [20](https://arxiv.org/html/2508.18966v1#Sx2.F20), USO exhibits high stylistic fidelity, capturing both fine-grained characteristics (e.g., brushwork and material textures) and abstract artistic styles—far beyond simple color transfer.
- •
In [Figures˜21](https://arxiv.org/html/2508.18966v1#Sx2.F21) and [22](https://arxiv.org/html/2508.18966v1#Sx2.F22), USO freely combines arbitrary subjects with arbitrary styles, supporting both layout-preserving and layout-shifting generations.

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 6
presentation: 3
questions: 1. Could you clarify whether the input text prompts contain explicit style descriptions? The understanding of what constitutes the "style" source is crucial for interpreting the results.

2. What specific measures are implemented in the model architecture or training objective to ensure content consistency, especially for cases involving significant layout changes between the content and target images?

3. Given the critical influence of base models, could you provide details on the specific base models used for each compared method in your experiments? Furthermore, have you conducted any ablation studies to isolate the contribution of your proposed algorithm from the choice of the base model?
soundness: 2
strengths: The core innovations of the described work include:
1. Unification of style and subject-driven generation under a single framework by addressing the disentanglement and recombination of content and style.
2. Construction of a large-scale triplet dataset with content images, style images, and stylized content images.
3. Implementation of a content-style disentangled learning scheme 
4. Integration of a style reward-learning paradigm (SRL) to enhance model performance.

The expression in this paper is relatively clear.
summary: This paper proposed a Unified Style-Subject Optimized customization model, which treats style-driven and subject-driven generation as a unified task.
weaknesses: 1. Content Leakage in Style Encoder: The use of SigLIP as the style encoder is a notable weakness, as SigLIP features inherently encapsulate semantic information alongside style. This leads to potential content leakage, undermining the core objective of content-style disentanglement. A more dedicated style encoder should be considered to purify the style representation.

2. Lack of Explicit Content Consistency Constraint: The mechanism for preserving content consistency, particularly against layout shifts, is underspecified. Without an explicit constraint to align the target and content images, the model's reliance on the content input is unregulated. This results in unpredictable and potentially insufficient content fidelity in the outputs.

3. Uncontrolled Variable in Baseline Comparisons: The comparative analysis lacks discussion on the base models used by different methods. Since the performance of these generative models is heavily dependent on the capability of their underlying base models, the validity of the comparisons is compromised unless this variable is controlled for or thoroughly analyzed.

---

confidence: 5
contribution: 3
initial_rating: 4
presentation: 2
questions: see Weaknesses
soundness: 2
strengths: 1.The results look good. 

2.The synthesis pipeline is relatively clear. 

3.When compared with recent methods, the advancement of USO is evident. 

4.It can handle various style control tasks.
summary: This article mainly addresses the issue of style transfer while preserving both ID and style. It points out that previous methods could only maintain the layout and style of the subject, but failed to achieve adjustable ID. To solve this problem, the article proposes a data synthesis pipeline and common training methods.
weaknesses: The training details of the expert models in the two synthesis pipelines are not mentioned. Figure 2 is not clear.

The authors implemented USO based on UNO; to what extent does the achieved ID preservation capability stem from UNO?

When using CSD as the style reward, will it affect or even compromise the evaluation?

The authors did not measure the copy-paste phenomenon.

In some cases, the facial pose does not appear to change significantly. More reasonable supplementary cases may be needed.

The model parameters are not mentioned. What would the results be if the SDXL architecture is used? Most of the compared methods adopt the SDXL architecture.

---

confidence: 5
contribution: 2
initial_rating: 4
presentation: 2
questions: 1.Could you provide more details about two experts models?
soundness: 2
strengths: 1.USO is the first framework to unify subject-driven generation and style transfer. 

2.USO achieves state-of-the-art subject fidelity and visual consistency on benchmark datasets.
summary: USO is a unified customization framework that jointly tackles style-driven and subject-driven image generation by disentangling and recombining content and style features. It builds cross-task triplets via stylization and de-stylization experts, then trains a DiT-based model with separate encoders plus stochastic conditioning dropout and Style Reward Learning to reinforce style extraction. On the new USO-Bench and DreamBench, USO attains state-of-the-art subject consistency, style fidelity, and text alignment.
weaknesses: 1.CSD is trained on contrastive learning that uses artist name as the style label. There are discrepancy in the artworks for the single artist and most images in CSD's trained dataset are oil paintings, resulting un-reliable style reward score.

2.Proposed framework is similar to OmniStyle (replace VAE with SigLIP) and proposed reward learning is widely used in previous work, lack of novelty;

3.The paper shows that baselines fall shorts in text following in Figure 4, but USO do not achieve the best CLIP-T  score in Tabel 1.

4.The destylization model and dataset curation pipeline is similar to OmniStyle2[1].

5.Actually, SigLip is good at extract semantic feature with coupled style and content. The disentanglement in your paper is from dataset not the proposed encoder.

6.Lack of comparisons of SOTA style transfer methods like AttentionDistillation[2], AlignedGen[3].

[1]OmniStyle2: Scalable and High Quality Artistic Style Transfer Data Generation via Destylization.
[2]Attention Distillation: A Unified Approach to Visual Characteristics Transfer, CVPR 2025.
[3]AlignedGen: Aligning Style Across Generated Images, NIPS 2025.

---

confidence: 5
contribution: 1
initial_rating: 2
presentation: 2
questions: 1.  In terms of style-driven generation itself, which one is better, the style expert model or USO? The style expert is trained with how much data?

2. With what proof could not VAE  learn semantic style transfer well? This is generally not true. 

3. Will the SRL cause USO to specialize on certain styles thus lose generalization capability?

4. What is the point of layout-shifted generation? This could be simply done by image customization/editing, then feed the result to layout-preserved style transfer. And in fact, the layout is not shifted obviously in the current version of USO.
soundness: 2
strengths: 1. USO validated that  style-driven generation  and subject-driven generation could be jointly trained. The model obtain good results on subject-driven generation benchmarks. 



2. The authors conducted rich experiments to demonstrate the effectiveness of the method. 

3. The paper is organized well and overall easy to follow.
summary: USO addresses the isolation of style-driven generation  and subject-driven generation, which are treated as disjoint in previous research. The authors argue that these two objectives can be unified by leveraging disentanglement and recomposition of content and style and propose USO (Unified Style-subject Optimized customization model), a framework that enables mutual reinforcement between the two tasks rather than isolation.
The paper proposes 

1. Cross-Task Triplet Curation Framework, which reverses the target style image to a style reference and a content reference. 

2. Content-Style Disentangled Training, which is a quite standard in-context DiT, using SIGLIP to extract style feature, VAE to extract content feature. 

3. Style Reward Learning,  which reinforces the model to prioritize desired style/content features by computing a reward (via VLM filters or the CSD model) between the generated stylized image and the style reference. 

Experiments on USO-Bench and DreamBench show USO achieves state-of-the-art results in subject-driven generation, style-driven generation and style-subject-driven generation.
weaknesses: Major Weakness: 
1. Cross-Task Triplet Curation Framework lacks novelty and misses many implementation details.  

 First, the data synthesis framework lacks novelty. Presented in Figure 2, USO data curation framework reverses the style target image to generate style reference and content reference, which is not new because a similar idea of style removal module had been proposed and demonstrated effectively in 2023 [1].  

 Second, many implementation details have been hided. For example, what is the performance of the style-expert model and de-stylization expert mdoel? With what data are they trained? In terms of style transfer ability itself, which one is better, the style expert model or USO? None of such information is revealed in the paper.

Third, it is a very strange setting to purposely shift the layout of content reference. Besides, even following this setting, the 'layout-shifted' results in Figure 1 and Figure 6 still demonstrate a strong copy-paste problem where the layouts are not obviously shifted.  Overall, this setting seems weird to me and even the purpose of the shifting layout is not achieved well by USO. 

2. The USO training framework is  deficient in novelty. The overall structure is a standard in-context DiT training paradigm, with VAE encoder extracting content feature, SigLIP extracting style features. The authors claimed that style demands more semantic cues thus they employed the SigLIP encoder instead of VAE encoder, which seems lacking  persuasiveness to me. With what proof could not VAE  learn semantic style transfer? In fact, VAE definitely can and is well capable of learning semantic cues in style transfer, including 'geometric deformaton (3D cartoons)'. Thus the motivation of the USO design choice is  questionable. Besides, the hierarchical projector design lacks novelty, because it has been used by many  adapter-based image customization method, including styleshot[2], Ssr-encoder[3] in style transfer,  PuLID[4] in ID customization.


3. The proposed Style Reward Learning (SRL) is slightly adjusting existing reward-learning paradigm to style transfer scenario. SRL differs from ReFL  only in that ReFL focuses on "text fidelity or visual appeal" in text-to-image generation, while SRL is tailored for "reference-to-image setting". This is not a paradigm innovation but a scenario-specific adjustment. For example, both ReFL and SRL rely on external models (ReFL uses human preference models, SRL uses VLM/CSD models) to compute rewards, then backpropagate the reward signal to optimize the generator, which makes SRL a derivative work rather than an original design. Besides, the paper uses only existing and off-the-shelf similarity metrics (VLM-based filters or the CSD model) to compute this reward, with no novel design for the reward signal itself. This means the most critical part of SRL—the "reward definition"—lacks originality, as it depends entirely on pre-existing tools rather than introducing a new way to quantify "desired style features."  Finally, the reward learning may hurt the generalization ability of the in-context DiT model, making this model converges to a small number of biased style features.

Minor Weakness:

1. Some styles in the paper are not transfered well. For example, the stroke of the Van Gogh style is not well reconstructed, worse than many SDXL-based style transfer methods. 

2. We could observe a significant color leakage problem in the style-subject-driven scenario, which consistently appears in all related figures in this paper. The color of the style reference image is always transfered to the background of the generated image, sometimes affecting the foreground subject as well. 

3. The example triplet in Figure 3 shows that the reversely generated content reference still has a strong Chinese Ink style. The reverse content reference images in Figure 1 could not align with the target images very well. For example, there are more vegetables in the basket than the target image, the branch of bamboo is not aligned with target image.  Such miss-alignments may lead to inconsistent layout preservation. 

Reference

[1] StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Models,  ICCV 2023

[2] StyleShot: A SnapShot on Any Style, arxiv 

[3] SSR-Encoder: Encoding Selective Subject Representation for Subject-Driven Generatio, CVPR 2024

[4] PuLID: Pure and Lightning ID Customization via Contrastive Alignment, NeurIPS 2025