Instructions to use GaumlessGraham/Inner1730_10Real with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use GaumlessGraham/Inner1730_10Real with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("GaumlessGraham/Inner1730_10Real", 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_generated/0010.png from GaumlessGraham/Inner1730_10Real: direct link, hf CLI and curl.
- Browser
- Download file 23.2 kB
-
https://huggingface.co/GaumlessGraham/Inner1730_10Real/resolve/main/samples_generated/0010.png
- Command line
-
hf download hf://GaumlessGraham/Inner1730_10Real/samples_generated/0010.png
-
curl -L -o 0010.png https://huggingface.co/GaumlessGraham/Inner1730_10Real/resolve/main/samples_generated/0010.png
23.2 kB

- Xet hash:
- 95427607934bf44e8f95aeeec0e6b61b07717ec551309b28a01f896cc765de00
- Size of remote file:
- 23.2 kB
- SHA256:
- f795faedc266e3106872226674ecc6d0a1c78850c6295f9fd6a4083f8be6f4c6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.