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!
dav.pth already has the VAE encoder
the RVQ encoder is only required for covering songs.
also an open discussion about it is already there. did you check first?