CoDi-2
- Paper: CoDi-2: In-Context, Interleaved, and Interactive Any-to-Any Generation
- GitHub Link
- Publisher:
Arxiv
- Author Affiliation:
UC Berkeley
- Functional Division
- Understanding
- Generation
- Design Division
- Tool-using
- End-to-end
- Input Modalities $\rightarrow$ Output Modalities
(I: Image, V: Video, A: Audio, 3D: Point Cloud, T: Text, ID: Document understanding, IB: Output bounding box, IM: Output segmentation mask, IR: Output retrieved images)- I+V+A+T $\rightarrow$ I+V+A+T
- Model Architecture
(Input $\rightarrow$ Modality Encoder $\rightarrow$ Input Projector $\rightarrow$ LLM Backbone $\rightarrow$ Output Projector $\rightarrow$ Modality Generator $\rightarrow$ Output)- Modality Encoder
I/V/A: ImageBind
- Input Projector
MLP
- LLM Backbone
LLaMA-2-Chat-7B
- Output Projector
MLP
- Modality Generator
I: Stable Diffusion-2.1
V: Zeroscope-v2
A: AudioLDM-2
- Modality Encoder
- Datasets Scale
- Pre-training Stage
Not report
- Instruction-tuning Stage
Not report
- Pre-training Stage
This post is licensed under CC BY 4.0 by the author.