OmniGen: Unified Image Generation
Open accessAbstract
The emergence of Large Language Models (LLMs) has unified language generation tasks and revolutionized human-machine interaction. However, in the realm of image generation, a unified model capable of handling various tasks within a single framework remains largely unexplored. In this work, we introduce OmniGen, a new diffusion model for unified image generation. OmniGen is characterized by the following features: 1) Unification: OmniGen not only demonstrates text-to-image generation capabilities but also inherently supports various downstream tasks, such as image editing, subject-driven generation, and visual-conditional generation. 2) Simplicity: The architecture of OmniGen is highly simplified, eliminating the need for additional plugins. Moreover, compared to existing diffusion models, it is more user-friendly and can complete complex tasks end-to-end through instructions without the need for extra intermediate steps, greatly simplifying the image generation workflow. 3) Knowledge Transfer: Benefit from learning in a unified format, OmniGen effectively transfers knowledge across different tasks, manages unseen tasks and domains, and exhibits novel capabilities. We also explore the model’s reasoning capabilities and potential applications of the chain-of-thought mechanism. This work represents the first attempt at a general-purpose image generation model, and we will release our resources at https://github.com/VectorSpaceLab/OmniGen to foster future advancements.
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Image customization approaches that reason over reference and noise latents tend to rely on implicitly learned spatial cues, limiting spatial positioning.
“However, these approaches tend to rely on implicitly learned spatial cues, limiting spatial positioning.”
From DreamingComics: A Story Visualization Pipeline via Subject and Layout Customized Generation using Video Models (2025) · cites this paper as [64]
Cites
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01042 · Cited by 15,880 · see citing works
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00371 · Cited by 11,953 · see citing works
Adding Conditional Control to Text-to-Image Diffusion Models
Zhang, L., Rao, A., & Agrawala, M. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00355 · Cited by 4,547 · see citing works
Scalable Diffusion Models with Transformers
Peebles, W., & Xie, S. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00387 · Cited by 2,284 · see citing works
The “Something Something” Video Database for Learning and Evaluating Visual Common Sense
Goyal, R., Kahou, S. E., Michalski, V., Materzynska, J., Westphal, S., Kim, H., Haenel, V., Fruend, I., Yianilos, P., Mueller-Freitag, M., et al. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.622 · Cited by 1,471 · cited 1× · see citing works
“We collect the data from multiple tasks and sources: image editing (MagicBrush [74], InstructPix2Pix [3] and SEED-edit [17], ), human motion (Something-Something [21]), virtual try-on (HR-VITON [30] and FashionTryon [77]), and style transfer (StyleBooth [23]).”
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2021) · arXiv · doi:10.48550/arxiv.2112.10752 · Cited by 737 · see citing works
Intern VL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks
Chen, Z., Wu, J., Wang, W., Su, W., Chen, G., Xing, S., Zhong, M., Zhang, Q., Zhu, X., Lu, L., et al. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02283 · Cited by 734 · see citing works
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Kwon, P., & Chen, C. (2025) · arXiv · doi:10.48550/arxiv.2512.01686
“With the advent of new Diffusion Transformer (DiT) models [2, 14, 38], many image customization works [19, 35, 52, 61, 63, 64, 76] directly reason over reference and noise latents in a unified attention space, improving subject fidelity without per-instance tuning.”
OmniGen2: Towards Instruction-Aligned Multimodal Generation
Wu, C., Zheng, P., Yan, R., Xiao, S., Luo, X., Wang, Y., Li, W., Jiang, X., Liu, Y., Zhou, J., et al. (2025) · arXiv · doi:10.48550/arxiv.2506.18871
Self-citation
- Authors
- Xiao, S., Wang, Y., Zhou, J., Yuan, H., Xing, X., Yan, R., Li, C., Wang, S., Huang, T., & Liu, Z.
- Affiliations
- Beijing Academy of Artificial Intelligence (CN)
- ORCID
- Venue
- 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2025-06-10
- Pages
- 13294-13304
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4413158163
- Cited by
- 75
- References
- 7
- Citations by year
- 2026: 47 · 2025: 28
- FWCI
- 56.68
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- diffusion models, text-to-image generation, image editing, chain-of-thought, large language models
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