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
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.
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Model tree for SimpleTuner/MiniMax-Music-3-Encoder
Base model
MiniMaxAI/MiniMax-Music3