OmniGen is a next-generation Unified Image Generation System designed to significantly reduce the complexity of image generation models and enable the unified processing of numerous visual tasks within a single architecture. Unlike conventional image generation infrastructures that clumsily assemble a pipeline by tacking together various pre-processing modules and fine-tuning adapters, OmniGen is built upon a single Autoregressive Transformer, treating text and images as equivalent context tokens. Just as GPT, as a large language model, smoothly handles any complex contextual or conditional text query within a single prompt window, this model naturally performs everything from simple text input to image-to-image editing using reference images, all within the same interface. To overcome the limitations of existing technologies, OmniGen introduces architectural differentiators that fundamentally eliminate plugin dependencies. Previously, implementing identity-preserving generation, which maintains the appearance of a specific subject, or ControlNet techniques, which fix the composition, required pre-preparing auxiliary networks in addition to the main model and manually performing complex pre-processing steps such as facial recognition or edge extraction. However, OmniGen's internal attention mechanism automatically captures the logical connections between multiple reference images contained within the prompt and outputs the results. This eliminates the severe data distortion and quality degradation that occurred during the pre-processing phase, allowing users to induce high-quality visual synthesis with just a few lines of text manipulation, without requiring sophisticated programming knowledge. When biomedical and medical researchers incorporate this tool into their research workflows, they will experience benefits that are far more intuitive and advanced than existing image transformation technologies. For example, by providing multiple stained tissue pathology slide images as references, they can easily generate simulated images of new drug responses by virtually adjusting the cellular differentiation state within the tissue while preserving the characteristics of the staining pattern. In this process, the model accurately recognizes the morphological characteristics unique to microscopic images, ensuring that the resulting visualizations maintain scientific validity. As a result, it creates clinical value by rapidly generating large-scale virtual control datasets that meet new experimental conditions, thereby significantly shortening the initial learning and validation stages of next-generation medical AI analysis models.

OmniGen is a next-generation Unified Image Generation System designed to significantly reduce the complexity of image generation models and handle numerous visual tasks with a single architecture. Unlike conventional image generation infrastructure, which awkwardly cobbles together various pre-processing modules and fine-tuning adapters to form a single pipeline, OmniGen is built on a single Autoregressive Transformer that treats text and images as equivalent context tokens.