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 this https URL

Composable Diffusion 是一种新颖的生成模型，能够从任意的输入单模态生成任意的输出多模态，应用了一种基于扩散的模态对齐方式，实现了输入和输出的多模态自由组合生成，并在单模态综合中表现出色。