Unified Multimodal Image Generation Model
A powerful and efficient unified model for text-to-image generation, image editing, and in-context generation. Built with distinct decoding pathways and decoupled architecture for superior performance.
Omnigen 2 represents a significant advancement in multimodal generation technology. Unlike its predecessor, Omnigen 2 features two distinct decoding pathways for text and image modalities, utilizing unshared parameters and a decoupled image tokenizer. This innovative architecture enables the model to build upon existing multimodal understanding models without requiring VAE input re-adaptation, thereby preserving original text generation capabilities.
The model demonstrates competitive performance across four primary capabilities that define modern AI image generation systems. Visual understanding capabilities inherit robust interpretation and analysis abilities from its Qwen-VL-2.5 foundation, allowing for comprehensive scene comprehension and content analysis. Text-to-image generation produces high-fidelity and aesthetically pleasing images from textual prompts, matching the quality standards of leading commercial solutions.
Instruction-guided image editing represents one of Omnigen 2's strongest capabilities, executing complex modifications with high precision and achieving state-of-the-art performance among open-source models. The system can understand nuanced editing instructions and apply them accurately while maintaining image coherence and quality. In-context generation provides versatile capability to process and flexibly combine diverse inputs, including humans, reference objects, and scenes, producing novel and coherent visual outputs that respect the relationships between different input elements.
As an open-source project, Omnigen 2 provides a powerful yet resource-efficient foundation for researchers and developers exploring controllable and personalized generative AI. The unified training approach, starting from a language model foundation, yields better performance compared to training solely on understanding or generation tasks, demonstrating the enhancement achieved by integrating these capabilities within a single architecture.
Omnigen 2 combines multiple advanced capabilities in a single unified model, eliminating the need for separate tools and workflows.
Create high-fidelity images from textual descriptions with superior quality and aesthetic appeal
Execute complex image modifications with high precision using natural language instructions
Process diverse inputs including humans, objects, and scenes to produce coherent visual outputs
Interpret and analyze image content with robust multimodal comprehension capabilities
The architecture of Omnigen 2 is built on unified training principles that integrate multiple capabilities within a single framework. This approach differs significantly from traditional methods that require separate models for different tasks. The model employs distinct decoding pathways for text and image modalities, each optimized for their specific requirements while maintaining seamless integration.
The decoupled image tokenizer represents a key innovation in Omnigen 2's design. This component enables the model to process visual information more effectively while maintaining compatibility with existing multimodal understanding models. The tokenizer handles image encoding and decoding operations independently, allowing for more efficient processing and better preservation of visual details throughout the generation pipeline.
Omnigen 2 achieves competitive results across multiple benchmarks while maintaining resource efficiency. The model requires approximately 17GB of VRAM for optimal performance, making it accessible to researchers and developers with standard GPU setups. The unified architecture eliminates the computational overhead associated with managing multiple separate models, resulting in improved inference speed and memory utilization.
The training process begins with a language model foundation and progressively incorporates image generation capabilities. This methodology ensures that text generation capabilities are preserved while adding visual processing abilities. The unified training approach demonstrates superior performance compared to models trained solely on individual tasks, highlighting the benefits of integrated multimodal learning.
NVIDIA RTX 3090 or equivalent
17GB+ VRAM recommended
Python 3.11+
PyTorch 2.6.0+
Linux, Windows, macOS
CUDA 12.4+ support
16GB+ system RAM
SSD storage recommended
Get Omnigen 2 running on your system with these simple installation steps.
# Clone the repository
git clone https://github.com/VectorSpaceLab/OmniGen2.git
cd OmniGen2
# Create Python environment
conda create -n omnigen2 python=3.11
conda activate omnigen2
# Install PyTorch with CUDA support
pip install torch==2.6.0 torchvision --extra-index-url https://download.pytorch.org/whl/cu124
# Install dependencies
pip install -r requirements.txt
# Optional: Install flash-attention for optimal performance
pip install flash-attn==2.7.4.post1 --no-build-isolationOnce installed, you can start using Omnigen 2 for various tasks. The model supports text-to-image generation, image editing, and in-context generation through simple command-line interfaces or Python scripts.
For text-to-image generation, simply provide a descriptive prompt and the model will generate high-quality images. The system supports various image resolutions and aspect ratios, automatically handling image resizing and optimization.
Image editing capabilities allow you to modify existing images using natural language instructions. The model can perform complex editing operations including object removal, style transfer, and detail enhancement while maintaining image quality and coherence.
In-context generation enables the combination of multiple input sources to create new images. This feature is particularly useful for subject-driven generation and style transfer applications.
bash example_t2i.shbash example_edit.shbash example_in_context_generation.shAccess code, documentation, research papers, and community resources for Omnigen 2.
Source code, documentation, and issue tracking
Pre-trained model weights and inference tools
Technical report and methodology details
Demos, examples, and additional resources
Omnigen 2 represents ongoing research in unified multimodal generation. The development team continues to improve the model's capabilities and release updates based on community feedback and research findings.