Instructions to use nathanReitinger/MNIST-diffusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nathanReitinger/MNIST-diffusion with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("nathanReitinger/MNIST-diffusion", 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:
- fa49a037ab6be077dde5c53f6c854920d8ca4201a1ae48f6911edd1755926f34
- Size of remote file:
- 6.86 MB
- SHA256:
- bdc0650a9ecea7b8a38fb7d5cf1c1164c0e13c065e177fb3c4cb46cd982fbd95
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.