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/0035.png from GaumlessGraham/Inner1730_10Real: direct link, hf CLI and curl.
- Browser
- Download file 24.9 kB
-
https://huggingface.co/GaumlessGraham/Inner1730_10Real/resolve/main/samples_generated/0035.png
- Command line
-
hf download hf://GaumlessGraham/Inner1730_10Real/samples_generated/0035.png
-
curl -L -o 0035.png https://huggingface.co/GaumlessGraham/Inner1730_10Real/resolve/main/samples_generated/0035.png
24.9 kB

- Xet hash:
- c86c293c20b1aea7c70e1d005f418091d54cd3425e97bef07cbf778577a48b1d
- Size of remote file:
- 24.9 kB
- SHA256:
- 875ffc822172665ffaa16d3acd9f280e9763a58f9f3f6bb0ba386fddff18dc75
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.