Text-to-Audio
Diffusers
Safetensors
PyTorch
minimax_music3
music-generation
text-to-music
sglang-omni
Instructions to use MiniMaxAI/MiniMax-Music3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MiniMaxAI/MiniMax-Music3 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MiniMaxAI/MiniMax-Music3", 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
Any plans to release a RVQ encoder or Flow-VAE encoder to enable audio-conditioned generation?
#11
by TheLatentSpacer - opened
Thanks for publishing these weights, the quality is great.
I'm wondering whether reference-audio conditioning (continuation, style priming, repaint, etc.) is possible with the released weights? As far as I can tell it isn't, because no audio-to-latent or audio-to-token path was published
Any plans to release any of these paths to enable audio-conditioned generation?
Thanks either way!
This comment has been hidden (marked as Resolved)
here's an open reproduction of a compatible RVQ encoder https://huggingface.co/collections/SimpleTuner/open-rvq-encoder-for-minimax-music-3
i'm interesed to see what you do with it and how you enable audio-conditioned generation.