A defining feature of OmniGen is its remarkable simplicity and efficiency. The model’s architecture is highly streamlined, doing away with the need for separate text encoders or additional modules like ControlNet. Instead, OmniGen jointly models text and images within a single context, allowing for seamless knowledge transfer across tasks. This unified approach not only simplifies the workflow for users but also enables the model to tackle a variety of classical computer vision tasks-such as deblurring, deraining, inpainting, human pose estimation, and depth estimation-by reframing them as image generation problems. The system is capable of handling multi-modal instructions and can generate or edit images with high fidelity and minimal user intervention.


OmniGen is available as an open-source project, with a commercial cloud-based platform offering a range of subscription plans. The free plan allows users to generate images with watermarks and limited credits, suitable for non-commercial use and experimentation. Paid subscriptions start at $12.90 per month for the Starter Plan (30 credits), with Premium and Platinum plans offering more credits and additional features such as early access to new tools and extended generation history. Commercial usage rights and tailored business plans are also available for organizations with larger needs. The platform’s flexible credit system and transparent pricing make it accessible for hobbyists, professionals, and enterprises alike.


Key Features

Unified text-to-image, image-to-image, and subject-driven generation in one model
Supports image editing, visual conditional generation, and classical computer vision tasks
Streamlined architecture without extra text encoders or plugins
Handles multi-modal inputs and instructions with automatic object recognition
Efficient knowledge transfer across tasks and domains
Flexible open-source and commercial subscription options
Credit-based system with plans for individuals and businesses
No manual preprocessing required for most tasks

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