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

- Xet hash:
- 7077a26de3c8635a37c8ffa6e1d0527730ea05ae4c1ed5257514049076d49579
- Size of remote file:
- 2.18 MB
- SHA256:
- 1d97d174853c5468aa242394971f4f228d1a2665042fbc43a945a3d1ca1debf9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.