title: Any-to-Any Generation via Composable Diffusion
summary: 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
paper_authors._id: 646ac311c37ca1e1230c434a
paper_authors.name: Zineng Tang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-23T08:11:26.215Z
paper_authors._id: 6326e20bf0e99f96e024a164
paper_authors.avatarUrl: /avatars/9be111af274cad965f72ed48c71d6122.svg
paper_authors.fullname: Zineng Tang
paper_authors.type: user
paper_authors.user: ZinengTang
paper_authors._id: 646ac311c37ca1e1230c434b
paper_authors.name: Ziyi Yang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-05-23T06:39:40.181Z
paper_authors._id: 646be72031968a60a021ddb3
paper_authors.avatarUrl: /avatars/79f3766cf404fd180274dbc86ea4dac6.svg
paper_authors.fullname: Ziyi Yang
paper_authors.type: user
paper_authors.user: Ziyi-Yang
paper_authors._id: 646ac311c37ca1e1230c434c
paper_authors.name: Chenguang Zhu
paper_authors._id: 646ac311c37ca1e1230c434d
paper_authors.name: Michael Zeng
paper_authors._id: 646ac311c37ca1e1230c434e
paper_authors.name: Mohit Bansal
paper_title: Any-to-Any Generation via Composable Diffusion
paper_summary: 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
paper_ai_summary: CoDi is a generative model that can produce and condition on any combination of modalities by aligning them through a shared multimodal space in the diffusion process.
paper_ai_keywords: Composable Diffusion generative model output modalities language image video audio input modalities multimodal space diffusion process joint-modality generation unimodal state-of-the-art