Diffusers
Safetensors
PyTorch
AudioDiffusionPipeline
unconditional-audio-generation
diffusion-models-class
Instructions to use dogfish-thinker/audio-diffusion-electronic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dogfish-thinker/audio-diffusion-electronic with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dogfish-thinker/audio-diffusion-electronic", 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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Download README.md from dogfish-thinker/audio-diffusion-electronic: direct link, hf CLI and curl.
- Browser
- Download file 619 Bytes
-
https://huggingface.co/dogfish-thinker/audio-diffusion-electronic/resolve/main/README.md
- Command line
-
hf download hf://dogfish-thinker/audio-diffusion-electronic/README.md
-
curl -L -o README.md https://huggingface.co/dogfish-thinker/audio-diffusion-electronic/resolve/main/README.md
619 Bytes
metadata
license: mit
tags:
- pytorch
- diffusers
- unconditional-audio-generation
- diffusion-models-class
Model Card for Unit 4 of the Diffusion Models Class 🧨
This model is a diffusion model for unconditional audio generation of music in the genre Electronic
Usage
from IPython.display import Audio
from diffusers import DiffusionPipeline
pipe = DiffusionPipeline.from_pretrained("dogfish-thinker/audio-diffusion-electronic")
output = pipe()
display(output.images[0])
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))