Instructions to use kronosta/blunstron with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kronosta/blunstron with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kronosta/blunstron", 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
Blunstron is a model I made for Harmonai's Dance Diffusion. The dataset is less than five minutes of the song Old and Wise by The Alan Parsons Project, yet it performs very well and does not overfit. Old and Wise is sung by Colin Blunstone, hence the name Blunstron.
Why
I put music out for free on YouTube containing lots of tiny samples (in imitation of a musician named Todd Edwards), and I've sampled Old and Wise a LOT because I like the auditory textures in it. I'm kind of running out of potential chops, so I decided to generate a practically infinite supply of them with AI.
How
I finetuned this on Google Colab for around two hours. I kind of dislike how the word "finetune" is used for Dance Diffusion, since unlike Dreambooth with Stable Diffusion, Dance Diffusion models (including this one) effectively become an entirely different model when fine-tuned for long enough.
Audio Characteristics
- Deep, ethereal, soft auditory texture
- Mostly chords, not much melody
- Colin Blunstone-like vocals
- Occasional drum hits
A few examples are provided in the files of this git repo called blunstron-test-x.wav.
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