Any-to-Any Generation via Composable Diffusion
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
646ac311c37ca1e1230c434a
Zineng Tang
claimed_verified
2023-05-23T08:11:26.215Z
6326e20bf0e99f96e024a164
/avatars/9be111af274cad965f72ed48c71d6122.svg
Zineng Tang
user
ZinengTang
646ac311c37ca1e1230c434b
Ziyi Yang
claimed_verified
2023-05-23T06:39:40.181Z
646be72031968a60a021ddb3
/avatars/79f3766cf404fd180274dbc86ea4dac6.svg
Ziyi Yang
user
Ziyi-Yang
646ac311c37ca1e1230c434c
Chenguang Zhu
646ac311c37ca1e1230c434d
Michael Zeng
646ac311c37ca1e1230c434e
Mohit Bansal
Any-to-Any Generation via Composable Diffusion
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
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.
Composable Diffusion generative model output modalities language image video audio input modalities multimodal space diffusion process joint-modality generation unimodal state-of-the-art