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Omnigen2
Overview :
OmniGen2 is an efficient multimodal generation model that combines visual language models and diffusion models, enabling functions such as visual understanding, image generation, and editing. Its open-source nature provides researchers and developers with a strong foundation to explore personalized and controllable AI generation.
Target Users :
This product is suitable for researchers, developers, and designers who need efficient tools to generate and edit images, supporting personalized customization and innovative design.
Use Cases
Generate corresponding images based on user-provided text descriptions.
Use instructions to modify existing images in design work to meet requirements.
Combine various input data to generate rich visual content for promotional or educational materials.
Features
Visual understanding: Strong ability to analyze image content.
Text-to-image generation: Generate high-quality images based on text prompts.
Instruction-guided image editing: Accurately perform complex image modifications.
Contextual generation: Process and combine different inputs to produce novel visual outputs.
Supports multiple input formats, flexible application in different scenarios.
Provides a user-friendly interface and online demo platform.
Open-source code and datasets for research and development.
How to Use
Clone the code repository: git clone git@github.com:VectorSpaceLab/OmniGen2.git
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Create and activate Python environment: conda create -n omnigen2 python=3.11, conda activate omnigen2
Install PyTorch and other dependencies: pip install torch==2.6.0 torchvision, pip install -r requirements.txt
Run the example: bash example_t2i.sh for text-to-image generation.
Access the online demo or run the local application for image generation and editing.
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