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yeonwoo378/
flowbind

Name: flowbind
Rating: 2.5 (14 reviews)

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FlowBind: Efficient Any-to-Any Generation with Bidirectional Flows

Overview

FlowBind is an efficient flow-based multimodal generation model with bidirectional flows.

Try it out in our demo! : demo.ipynb

Model Description

The following files are required for inference and training:

Filename	Description
`model.pth`	Main FlowBind model weights. Includes three drift networks, one for each modality (Text, Image, Audio).
`config.json`	Configuration file containing model hyperparameters and architecture details.
`gemmae_3.pt`	Weights for the Text Decoder (Gemma).
`clap.pt`	Weights for the Audio Encoder (CLAP).

Data Preparation

We utilize the following datasets for training and evaluation.

Text-Image:
- LAION-COCO (Filtered by aesthetic scores)
- Flickr30k
Text-Audio:
- AudioCaps
Audio-Image:
- VGGSound

Note: Due to copyright and licensing restrictions, we cannot provide the raw training datasets directly. Please download the data from the official links provided above.

Citation

If you find our work helpful, please cite:

bibtex

@inproceedings{
cha2026flowbind,
title={FlowBind: Efficient Any-to-Any Generation with Bidirectional Flows},
author={Yeonwoo Cha and Semin Kim and Jinhyeon Kwon and Seunghoon Hong},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=7DeARTwvwL}
}

Contact

For any inquiries, please contact Yeonwoo Cha at ckdusdn03@kaist.ac.kr.

yeonwoo378/flowbind