Instructions to use SimpleTuner/MiniMax-Music-3-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SimpleTuner/MiniMax-Music-3-Encoder with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SimpleTuner/MiniMax-Music-3-Encoder", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 742 Bytes
fce0d00 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ---
library_name: diffusers
pipeline_tag: text-to-audio
base_model: MiniMaxAI/MiniMax-Music3
tags:
- minimax-music-3
- audio-vae
- diffusers
- simpletuner
---
# MiniMax Music 3 Audio VAE
This repository contains the MiniMax Music 3 DAV audio autoencoder converted to a Diffusers-style component for SimpleTuner.
The converted component is stored in `audio_vae/` and can be loaded with `MiniMaxMusic3DAV.from_pretrained(repo_id, subfolder="audio_vae")` from SimpleTuner.
This is the continuous waveform autoencoder used for VAECache and waveform decode. It is not the RVQ tokenizer, Qwen3 language model, RVQ depth decoder, or flow transformer from the full MiniMax Music 3 pipeline.
Source weights: `MiniMaxAI/MiniMax-Music3` `dav.pth`.
|