Any-to-Any Generation via Composable Diffusion
This addresses the limitation of existing generative AI systems that are restricted to specific input-output modality subsets, enabling more flexible and customizable multimodal generation for applications in AI and creative fields.
The paper tackles the problem of generating any combination of output modalities (e.g., language, image, video, audio) from any input modalities, proposing Composable Diffusion (CoDi) that aligns modalities to enable generation even for unseen modality combinations, achieving strong joint-modality quality and matching or outperforming unimodal state-of-the-art.
We present Composable Diffusion (CoDi), a novel generative model capable of generating any combination of output modalities, such as language, image, video, or audio, from any combination of input modalities. Unlike existing generative AI systems, CoDi can generate multiple modalities in parallel and its input is not limited to a subset of modalities like text or image. Despite the absence of training datasets for many combinations of modalities, we propose to align modalities in both the input and output space. This allows CoDi to freely condition on any input combination and generate any group of modalities, even if they are not present in the training data. CoDi employs a novel composable generation strategy which involves building a shared multimodal space by bridging alignment in the diffusion process, enabling the synchronized generation of intertwined modalities, such as temporally aligned video and audio. Highly customizable and flexible, CoDi achieves strong joint-modality generation quality, and outperforms or is on par with the unimodal state-of-the-art for single-modality synthesis. The project page with demonstrations and code is at https://codi-gen.github.io