Instructions to use jonquimbly/shap-e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jonquimbly/shap-e with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("jonquimbly/shap-e", 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:
- 2f5b4e503af863f7be6092ec0401f4c1845ecbdd0eaa8384606a222030906929
- Size of remote file:
- 495 MB
- SHA256:
- 85f5bcf101dde33d8ab9f7e5e1678339fa4258ea07bc65e6ca66e01f9de99622
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.