Instructions to use Kalvis66/chiptunes_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Kalvis66/chiptunes_test with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Kalvis66/chiptunes_test", 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
| license: mit | |
| tags: | |
| - pytorch | |
| - diffusers | |
| - unconditional-audio-generation | |
| - diffusion-models-class | |
| # Model Card for Unit 4 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class) | |
| This model is a diffusion model for unconditional audio generation of music in the genre Chiptune / Glitch | |
| ## Usage | |
| ```python | |
| from IPython.display import Audio | |
| from diffusers import DiffusionPipeline | |
| pipe = DiffusionPipeline.from_pretrained("johnowhitaker/chiptunes_test") | |
| output = pipe() | |
| display(output.images[0]) | |
| display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate())) | |
| ``` | |