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
Is the model trainable?
Lora, Lokrs, etc
Important, important thing for making the model relevant on actual use.
Thankss
Until they release the Encoder, it's not.
yes: https://docs.simpletuner.io/quickstart/MINIMAX_MUSIC/
LyCORIS and PEFT LoRA trainable via SimpleTuner, with lyrics + caption validation libraries too. though that's not easily configured via the webui just yet (the validation library), the example prompts and captions from their website are included as a built in validation prompt library if you're doing eg. a broad finetune and want to validate a number of separate concepts.
it works with distillation too, so, the next project i'm on with MM Music is to throw AnyFlow at it and bring it down to a decent 4-step generation pipeline in the flow transformer.
what we're limited by is the lack of RVQ encoder's semantic audio tokens from raw audio. i think we could reverse engineer one though if so inclined, i'm not sure whether i'd be going against the license to do so.
Nice.. Its something.
Would it make any sense to opensource a model to then not share a fundamental piece? A model that is not trainable/conditionable is pretty much a toy.