Instructions to use nathanReitinger/MNIST-diffusion-oneImage with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nathanReitinger/MNIST-diffusion-oneImage 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-oneImage", 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
Download samples/0602.png from nathanReitinger/MNIST-diffusion-oneImage: direct link, hf CLI and curl.
- Browser
- Download file 10.4 kB
-
https://huggingface.co/nathanReitinger/MNIST-diffusion-oneImage/resolve/main/samples/0602.png
- Command line
-
hf download hf://nathanReitinger/MNIST-diffusion-oneImage/samples/0602.png
-
curl -L -o 0602.png https://huggingface.co/nathanReitinger/MNIST-diffusion-oneImage/resolve/main/samples/0602.png
10.4 kB

- Xet hash:
- 61f5aa05d6b5ce43497ef84360f833755e902de670d9f2a50e2be715591f95dd
- Size of remote file:
- 10.4 kB
- SHA256:
- cd36b616eb477813e97072e8ec37eac2c745d26da06f79b33f4de076d6f3686d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.