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/0045.png from GaumlessGraham/Inner1730_10Real: direct link, hf CLI and curl.
- Browser
- Download file 25 kB
-
https://huggingface.co/GaumlessGraham/Inner1730_10Real/resolve/main/samples_generated/0045.png
- Command line
-
hf download hf://GaumlessGraham/Inner1730_10Real/samples_generated/0045.png
-
curl -L -o 0045.png https://huggingface.co/GaumlessGraham/Inner1730_10Real/resolve/main/samples_generated/0045.png
25 kB

- Xet hash:
- ba6a211001aad0f135c133d9b23e55e91adc5228720b64d460697cc8affd7b55
- Size of remote file:
- 25 kB
- SHA256:
- f29eb70fd6834f96f14c6c4bf86cf92912020e2d9b3b51b0f18d9a21248d32b0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.