

UNO
Overview :
UNO is a multi-image conditional generation model based on diffusion transformers. By introducing progressive cross-modal alignment and universal rotational positional embedding, it achieves highly consistent image generation. Its main advantages lie in enhanced controllability over the generation of single or multiple subjects, making it suitable for various creative image generation tasks.
Target Users :
This product is suitable for researchers, developers, and artists, enabling them to explore new possibilities in image generation and providing strong support for their creations.
Use Cases
Art Creation: Artists use UNO to generate inspirational images.
Advertising Design: Advertising companies use UNO to create highly consistent multi-image advertising materials.
Academic Research: Researchers use UNO for experiments and exploration in image generation.
Features
High Consistency Generation: Maintains consistency when generating multi-subject images.
Enhanced Controllability: Provides better control over the subject and style of generated images.
Cross-Modal Alignment: Achieves good alignment of cross-modal data by leveraging the training of text-to-image models.
Open-Source Tool: Completely open-source, convenient for researchers and developers.
Easy Integration: Can be easily integrated with existing generative models and datasets.
How to Use
Clone the GitHub repository.
Install the required dependencies.
Download the model checkpoint.
Run the inference script to generate images.
Adjust parameters as needed for diversified generation.
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