The Beijing Academy of Artificial Intelligence (BAAI) has recently announced the launch of OmniGen, a groundbreaking all-purpose visual generation model, marking a significant advancement in the field of image generation. Known for its unity, simplicity, and cross-task knowledge transfer capabilities, the OmniGen model can handle a variety of image generation tasks within a single framework, including text-to-image conversion, image editing, subject-driven generation, and visual conditional generation. Additionally, OmniGen is capable of addressing classic computer vision tasks such as image denoising and edge detection by transforming these tasks into image generation processes.
The core advantage of OmniGen lies in its simplified architecture and user-friendly operation. Users can accomplish complex image generation tasks with simple instructions, without the need for additional plugins or complicated processing steps. This unified learning format allows OmniGen to effectively transfer knowledge across different tasks, handle unseen tasks and domains, and demonstrate novel functionalities.
Beyond the aforementioned capabilities, OmniGen also includes basic image processing functions such as denoising and edge extraction. The model's weights and code have been open-sourced, enabling users to explore more of OmniGen's capabilities and fine-tune it as needed. The BAAI has constructed a large-scale and diverse unified image generation dataset, X2I, which contains approximately 100 million images and will be open-sourced in the future to promote the development of the general image generation field.
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Paper: https://arxiv.org/pdf/2409.11340