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Ryann829/
Scone

Name: Scone
Rating: 2.5 (18 reviews)

Ryann829/Scone-S2I-57K

2512.12675

ByteDance-Seed/BAGEL-7B-MoT

finetune

apache-2.0

endpoints_compatible

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Scone

[✨CVPR 2026 Highlight] Scone: Bridging Composition and Distinction in Subject-Driven
Image Generation via Unified Understanding-Generation Modeling

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Yuran Wang^1,2* Bohan Zeng^1,2* Chengzhuo Tong^1,2 Wenxuan Liu¹ Yang Shi^1,2
Xiaochen Ma¹ Hao Liang¹ Yuanxing Zhang² Wentao Zhang¹†
¹Peking University ²Kling Team, Kuaishou Technology
* Equal contribution, † Corresponding author

📢 News

2026.4.9: 🎉🎉 Our paper has been selected as a ✨Highlight at CVPR 2026.
2025.12.16: The paper, training code, inference and evaluation code, model weight, training data, SconeEval benchmark are now released.

📖 Introduction

Subject-driven image generation has recently gained significant attention, with the focus evolving from single-subject to multi-subject generation, incorporating more input images. Existing methods can process two or more input images and combine subjects based on instructions, showcasing potential for more complex composition tasks.

However, existing works primarily focus on expanding subject combinations while neglecting the ability to distinguish target subjects in complex contexts. As shown in Figure 1.(a), although current models can combine multiple subjects, they may fail to identify and generate the correct target subject when a reference image contains multiple candidates, leading to problems such as subject omissions (none of the candidate subjects appear) or errors (misidentification of the target subject). Real-world images often involve interference and intricate details, which further limit practical performance. Thus, we emphasize examining the input subjects themselves, focusing on the model’s ability to distinguish the target subject within complex contexts and leverage this information for generation.

We propose the Scone (Subject-driven composition and distinction enhancement) model, which supports multi-subject composition and excels in subject distinction in complex contexts. Experiments show our Scone ranks first among open-source models on OmniContext benchmark.
We introduce the understanding bridge strategy, which transforms the understanding expert into a semantic bridge, enabling early multimodal alignment and attention-based semantic filtering to guide the generation expert, enhancing subject distinction and semantic fidelity without adding extra parameters.
We develop SconeEval, a challenging benchmark with three difficulty levels, to evaluate performance on subject-driven image generation tasks from both composition and distinction perspectives.

🔧 Environment setup

bash

git clone https://github.com/Ryann-Ran/Scone.git
cd Scone
conda create -n scone python=3.10 -y
conda activate scone
pip install -r requirements.txt
pip install flash_attn==2.5.8 --no-build-isolation

🔥 Train

Data and base model preparation

Download our 22K refined single-candidate data and 35K multi-candidate data from Scone-S2I-57K. The 70K base single-candidate data are sampled from open-source datasets like X2I, MUSAR-Gen, UNO-1M, and Echo-4o-Image. Please refer to the dataset links for more details.

bash
1cd Scone 2# pip install -U huggingface_hub 3hf download Ryann829/Scone-S2I-57K --repo-type=dataset --local-dir ./datasets/Scone-S2I-57K
Organize the data hierarchy as follows:

Scone-S2I-57K
    ├── parquet_data
    │   ├── scone_single_candidate_base/
    │   ├── scone_single_candidate_refined/
    │   └── scone_multi_candidate/
    └── parquet_info
        ├── scone_single_candidate_base.json
        ├── scone_single_candidate_refined.json
        └── scone_multi_candidate.json

Replace each your_data_path placeholder with your actual absolute path in:
- Parquet information files: ./datasets/Scone-S2I-57K/parquet_info/*.json
- Dataset information file: ./data/dataset_info.py
Download the checkpoint of our base model BAGEL from HuggingFace:

bash

cd Scone
# pip install -U huggingface_hub
hf download ByteDance-Seed/BAGEL-7B-MoT  --local-dir ./ckpts/BAGEL-7B-MoT

Note: To avoid out-of-memory (OOM) issues, we disable the EMA update strategy originally used in BAGEL. All our training processes are conducted on 8 Nvidia A800 GPUs.

The usage of semantic mask in the understanding bridge strategy is controlled by the training argument --use_semantic_mask.

Stage I: Composition training

For Step 1, please use base single-candidate data for 1 epoch (~30 hours):

bash scripts/train_stage1_step1.sh # 🔥 Und., Gen.

For Step 2, please use refined single-candidate data for 1 epoch (~15 hours) and replace model_path in the script with your Step 1 checkpoint :

bash scripts/train_stage1_step2.sh # 🔥 Und., Gen.

Stage II: Distinction training with understanding bridge strategy

For Step 1, please use refined sinlgle-candidate data and multi-candidate data for 1k steps (~5 hours) and replace model_path in the script with your Stage 1 Step 2 checkpoint:

bash scripts/train_stage2_step1.sh # 🔥 Und. ❄️ Gen.

For Step 2, please use refined sinlgle-candidate data and multi-candidate data for 1k steps (~5 hours) and replace model_path in the script with your Stage 2 Step 1 checkpoint:

bash scripts/train_stage2_step2.sh # 🔥 Und., Gen.

🔍 Inference and Evaluation

Scone model preparation

Download the Scone model checkpoint from HuggingFace:

bash

# pip install -U huggingface_hub
hf download Ryann829/Scone  --local-dir ./ckpts/Scone

Single case inference

Run the inference script:

bash scripts/inference_single_case.sh

Example Output: (Images sampled at 1024x1024 resolution with seed 1234, except for GPT-4o and Gemini-2.5-Flash-Image APIs)

Ref. 1	Ref. 2	Instruction	Scone (Ours)	GPT-4o	Gemini-2.5-Flash-Image	UNO	Qwen-Image-Edit-2509	BAGEL	OmniGen2	Echo-4o
		The man from image 2 holds the object which has a blue-and-red top in image 1 in a coffee shop.

📊 Performance

OmniContext benchmark

Method	SINGLE ↑	MULTIPLE ↑	SCENE ↑	Average ↑
Character	Object	Character	Object	Char. + Obj.	Character	Object	Char. + Obj.
Closed-Source Model
Gemini-2.5-Flash-Image	8.79	9.12	8.27	8.60	7.71	7.63	7.65	6.81	8.07
GPT-4o^*	8.96	8.91	8.90	8.95	8.81	8.92	8.40	8.44	8.78
Generation Model
FLUX.1 Kontext [dev]	8.07	7.97	-	-	-	-	-	-	-
UNO	7.15	6.72	3.56	6.46	4.90	2.72	4.89	4.76	5.14
USO	8.03	7.55	3.32	6.10	4.56	2.77	5.38	5.09	5.35
UniWorld-V2	8.45	8.44	7.87	8.22	7.95	5.36	7.47	6.98	7.59
Qwen-Image-Edit-2509	8.56	8.41	7.92	8.37	7.79	5.23	7.70	6.86	7.60
Unified Model
BAGEL	7.00	7.04	5.32	6.69	6.74	3.94	5.77	5.73	6.03
OmniGen2	8.17	7.63	7.26	7.03	7.56	7.02	6.90	6.64	7.28
Echo-4o	8.34	8.27	8.13	8.14	8.11	7.07	7.73	7.77	7.95
Score (Ours)	8.34	8.52	8.24	8.14	8.30	7.06	7.88	7.63	8.01

*: GPT-4o responded to 365~370 test cases out of the total 409 cases due to OpenAI safety restrictions.

To mitigate randomness, we perform 3 rounds of sampling at 1024x1024 resolution, scoring 3 times per round, yielding 9 group results. The final score is the average of these results.

SconeEval benchmark

To evaluate a model’s ability to distinguish and generate the referred subject in complex visual contexts, we introduce a new benchmark, SconeEval. It contains 409 test cases across character, object, and scene combinations and subject distinction, with 19 case types in Figure 2(a) and 6 subtasks in Figure 2(b), providing a comprehensive evaluation of a model’s ability to distinguish and utilize subject features.

Unlike traditional benchmarks that emphasize visual fidelity or text alignment, SconeEval focuses on cross-modal reasoning from complex contexts involving reference images and instructions, which requires deciding whom to generate when multiple candidates appear within or across images.

SconeEval includes three progressively challenging tasks, as shown in Figure 2(c): composition, distinction, and distinction & composition. In the composition task, each reference image contains a subject, and one or more images correspond to single or multiple generated subjects. In the distinction task, each reference image contains multiple subjects, and the model generates one target subject. The distinction & composition task integrates both settings, where each reference image contains multiple subjects and multiple images are used for multi-subject generation. Tasks involving distinction include cross-category and intra-category cases, indicating whether candidate subjects in a reference image belong to the same category.

📊 LeaderBoard

Method	Composition ↑	Distinction ↑	Distinction & Composition ↑	Average ↑
Single	Multi	Cross	Intra	Cross	Intra	COM	DIS	Overall
COM	COM	COM	DIS	COM	DIS	COM	DIS	COM	DIS
Closed-Source Model
Gemini-2.5-Flash-Image	8.87	7.94	9.12	9.15	9.00	8.50	8.27	8.87	8.17	8.85	8.56	8.84	8.70
GPT-4o^*	8.92	8.51	9.18	8.55	9.45	9.01	8.83	8.49	8.99	9.56	8.98	8.90	8.94
Generation Model
FLUX.1 Kontext [dev]	7.92	-	7.93	8.45	6.20	6.11	-	-	-	-	-	-	-
USO	8.03	5.19	7.96	8.50	7.14	6.51	5.10	6.25	5.07	5.57	6.41	6.71	6.56
UNO	7.53	5.38	7.27	7.90	6.76	6.53	5.27	7.02	5.61	6.27	6.31	6.93	6.62
UniWorld-V2 (Edit-R1-Qwen-Image-Edit-2509)	8.41	7.16	8.63	8.24	7.44	6.77	7.52	8.03	7.70	7.24	7.81	7.57	7.69
Qwen-Image-Edit-2509	8.54	6.85	8.85	8.57	7.32	6.86	7.53	8.13	7.49	7.02	7.76	7.65	7.70
Unified Model
BAGEL	7.14	5.55	7.49	7.95	6.93	6.21	6.44	7.38	6.87	7.27	6.74	7.20	6.97
OmniGen2	8.00	6.59	8.31	8.99	6.99	6.80	7.28	8.30	7.14	7.13	7.39	7.81	7.60
Echo-4o	8.58	7.73	8.36	8.33	7.74	7.18	7.87	8.72	8.01	8.33	8.05	8.14	8.09
Scone (Ours)	8.52	7.40	8.98	9.73	7.97	7.74	8.20	9.25	8.21	8.44	8.21	8.79	8.50

*: GPT-4o responded to 365~370 test cases out of the total 409 cases due to OpenAI safety restrictions.

To mitigate randomness, we perform 3 rounds of sampling at 1024x1024 resolution, scoring 3 times per round, yielding 9 group results. The final score is the average of these results.

Inference

Download the data:

bash

# pip install -U huggingface_hub
hf download Ryann829/SconeEval --repo-type=dataset --local-dir ../SconeEval

Run the script:

bash scripts/inference_sconeeval.sh

Evaluation

Use GPT-4.1 to evaluate the quality of the generated images and calculate the final score. Please ensure your API key is configured before running the script.

bash eval/s2i/sconeeval/eval.sh

🚀 Updates

Release paper
Release training code
Release inference and evaluation code
Release model weight
Release training data
Release SconeEval benchmark

🚰 Citation

If you find Scone helpful, please consider giving the repo a star ⭐.

If you find this project useful for your research, please consider citing our paper:

bibtex

@article{wang2025scone,
  title={Scone: Bridging Composition and Distinction in Subject-Driven Image Generation via Unified Understanding-Generation Modeling},
  author={Wang, Yuran and Zeng, Bohan and Tong, Chengzhuo and Liu, Wenxuan and Shi, Yang and Ma, Xiaochen and Liang, Hao and Zhang, Yuanxing and Zhang, Wentao},
  journal={arXiv preprint arXiv:2512.12675},
  year={2025}
}

💪 Acknowledgements

This project builds upon the following repositories:

Special thanks to these original projects and open-source datasets for their valuable contributions.

Ryann829/Scone

[✨CVPR 2026 Highlight] Scone: Bridging Composition and Distinction in Subject-Driven Image Generation via Unified Understanding-Generation Modeling

📢 News

📖 Introduction

🔧 Environment setup

🔥 Train

Data and base model preparation

Stage I: Composition training

Stage II: Distinction training with understanding bridge strategy

🔍 Inference and Evaluation

Scone model preparation

Single case inference

📊 Performance

OmniContext benchmark

SconeEval benchmark

📊 LeaderBoard

Inference

Evaluation

🚀 Updates

🚰 Citation

💪 Acknowledgements

Ryann829/
Scone

[✨CVPR 2026 Highlight] Scone: Bridging Composition and Distinction in Subject-Driven
Image Generation via Unified Understanding-Generation Modeling