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:
- cd66d3316aa651f014cb0aae1db9b5aeae78f3aab5ae741e4be04a8a7dc421a8
- Size of remote file:
- 247 MB
- SHA256:
- fc42badf529dd83f2f7c3d20fe6bda1e22036162f37c4c668b9e130884e20561
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