Transfusion: A unified multimodal model for text and image generation
Traditional multimodal generative models typically require specialized processing methods for different modalities like text and images. While text may use a language model, images often use a diffusion model or other generative models. This approach necessitates the use of multiple independent models, which can be inefficient for processing and generating various types of data simultaneously within a single framework.
Introducing the Transfusion model
Researchers from Meta and the University of Southern California have developed the Transfusion model to address this challenge by processing both text and images within a unified model. Unlike traditional methods, Transfusion can handle both discrete data (e.g., text) and continuous data (e.g., images) concurrently. By combining the next token prediction task of a language model for text processing and the technology of a diffusion model for image processing, Transfusion trains a unified model capable of managing multiple modalities.
Performance and scalability
Experiments have demonstrated the effectiveness of the Transfusion model in both single-modal and cross-modal tasks, encompassing text-to-text, image-to-text, and text-to-image generation. When compared to the Chameleon method, Transfusion exhibits superior scalability and efficiency across various scales and computational loads. Particularly in image generation tasks, Transfusion showcases a computational efficiency that is 34 times better than Chameleon. Moreover, Transfusion outperforms Chameleon in text-related tasks, despite both models employing similar text modeling approaches.
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