Diffusers
Safetensors
PyTorch
AudioDiffusionPipeline
unconditional-audio-generation
diffusion-models-class
Instructions to use johnsett/audio-diffusion-electronic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use johnsett/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("johnsett/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
- Xet hash:
- 826f353d434ee2ca94f65411dbc56d05281729ab8ebbfe765229e97ebdad8d0f
- Size of remote file:
- 455 MB
- SHA256:
- 06ec1ed7885f856b70494f9accc246a9fe65fbb06e103b872da3d6e68bf464ae
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