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OmniGen: Unified Image Generation
OmniGen is a unified image generation model that can generate a wide range of images from multi-modal prompts. It is designed to be simple, flexible, and easy to use. We provide inference code so that everyone can explore more functionalities of OmniGen.
Existing image generation models often require loading several additional network modules (such as ControlNet, IP-Adapter, Reference-Net, etc.) and performing extra preprocessing steps (e.g., face detection, pose estimation, cropping, etc.) to generate a satisfactory image. However, we believe that the future image generation paradigm should be more simple and flexible, that is, generating various images directly through arbitrarily multi-modal instructions without the need for additional plugins and operations, similar to how GPT works in language generation.
Due to the limited resources, OmniGen still has room for improvement. We will continue to optimize it, and hope it inspires more universal image-generation models. You can also easily fine-tune OmniGen without worrying about designing networks for specific tasks; you just need to prepare the corresponding data, and then run the script . Imagination is no longer limited; everyone can construct any image-generation task, and perhaps we can achieve very interesting, wonderful, and creative things.
If you have any questions, ideas, or interesting tasks you want OmniGen to accomplish, feel free to discuss with us: [email protected], [email protected], [email protected]. We welcome any feedback to help us improve the model.
License
This repo is licensed under the MIT License .
Citation
If you find this repository useful, please consider giving a star ⭐ and citation
@article{xiao2024omnigen,
title={Omnigen: Unified image generation},
author={Xiao, Shitao and Wang, Yueze and Zhou, Junjie and Yuan, Huaying and Xing, Xingrun and Yan, Ruiran and Wang, Shuting and Huang, Tiejun and Liu, Zheng},
journal={arXiv preprint arXiv:2409.11340},
year={2024}
}
