2025-01-09 21:57:57 +05:30
2025-01-09 15:25:17 +05:30
2025-01-09 14:45:43 +05:30
2025-01-09 14:45:43 +05:30
2025-01-09 14:45:43 +05:30
2025-01-09 15:05:44 +05:30
2025-01-09 21:57:57 +05:30
2025-01-09 14:45:43 +05:30
2025-01-09 15:09:32 +05:30
2025-01-09 21:57:57 +05:30
2025-01-09 21:57:57 +05:30
2025-01-09 21:57:57 +05:30
2025-01-09 14:45:43 +05:30
2025-01-09 14:45:43 +05:30

ComfyUI-TangoFlux

ComfyUI Custom Nodes for "TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching". These nodes, adapted from the official implementations, generates high-quality 44.1kHz audio up to 30 seconds using just a text promptproduction.

Installation

  1. Navigate to your ComfyUI's custom_nodes directory:
cd ComfyUI/custom_nodes
  1. Clone this repository:
git clone https://github.com/LucipherDev/ComfyUI-TangoFlux
  1. Install requirements:
cd ComfyUI-TangoFlux
python install.py

Or Install via ComfyUI Manager

Check out some demos from the official demo page

Example Workflow

example_workflow

Usage

All the necessary models should be automatically downloaded when the TangoFluxLoader node is used for the first time.

Models can also be downloaded using the install.py script

Manual Download:

  • Download TangoFlux from here and put everything in models/tangoflux (make sure to include the config files)
  • Download text encoders from here and put everything in models/text_encoders/google-flan-t5-large (make sure to include the config files)

The nodes can be found in "TangoFlux" category as TangoFluxLoader, TangoFluxSampler, TangoFluxVAEDecodeAndPlay.

Citation

@misc{hung2024tangofluxsuperfastfaithful,
      title={TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization}, 
      author={Chia-Yu Hung and Navonil Majumder and Zhifeng Kong and Ambuj Mehrish and Rafael Valle and Bryan Catanzaro and Soujanya Poria},
      year={2024},
      eprint={2412.21037},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2412.21037}, 
}
S
Description
No description provided
Readme
69 KiB
Languages
Python 95.2%
JavaScript 4.8%