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

翻译：我们提出了可组合扩散模型（Composable Diffusion, CoDi），一种能够从任意输入模态组合生成任意输出模态组合（如语言、图像、视频或音频）的新型生成式模型。与现有生成式人工智能系统不同，CoDi能够并行生成多种模态，且其输入不限于文本或图像等某类模态子集。尽管缺乏针对多种模态组合的训练数据集，我们提出在输入和输出空间中对齐模态。这使得CoDi能够自由地以任意输入组合为条件，生成任意分组模态，即使这些模态未出现在训练数据中。CoDi采用了一种新颖的可组合生成策略，通过在扩散过程中桥接模态对齐来构建共享多模态空间，从而实现对交织模态（如时间对齐的视频与音频）的同步生成。CoDi具有高度可定制性和灵活性，在联合模态生成质量上表现出色，其在单模态合成任务上超越或等同于单模态最先进方法。项目页面（含演示与代码）为：https://codi-gen.github.io