Instructions to use alppo/amuse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use alppo/amuse with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("alppo/amuse", 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
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This is a conditional unet model designed for music generation using mel spectrogram images. The model was trained on the alppo/music dataset, which includes 5 different genres. It accepts 512x512 images and 1x64 condition embeddings, which can be obtained from my own variational autoencoder implementation.
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