Breaking cross-modal boundaries in multimodal AI: Introducing CoDi, composable diffusion for any-to-any generation
Imagine an AI model that can seamlessly generate high-quality content across text, images, video, and audio, all at once. Such a model would more accurately capture the multimodal nature of the world and human comprehension, seamlessly consolidate information from a wide range of sources, and enable strong immersion in human-AI interactions. This could transform the way humans interact with computers on various tasks, including assistive technology, custom learning tools, ambient computing, and content generation.
In a recent paper: Any-to-Any Generation via Composable Diffusion, Microsoft Azure Cognitive Service Research and UNC NLP present CoDi, a novel generative model capable of processing and simultaneously generating content across multiple modalities. CoDi allows for the synergistic generation of high-quality and coherent outputs