Diffusers
Safetensors
PyTorch
AudioDiffusionPipeline
unconditional-audio-generation
diffusion-models-class
Instructions to use mingyujeon/audio-diffusion-electronic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use mingyujeon/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("mingyujeon/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
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("mingyujeon/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]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("mingyujeon/audio-diffusion-electronic")
output = pipe()
display(output.images[0])
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))
- Downloads last month
- 1
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support