Instructions to use johnowhitaker/Electronic_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use johnowhitaker/Electronic_test with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("johnowhitaker/Electronic_test", 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 unet/diffusion_pytorch_model.bin from johnowhitaker/Electronic_test: direct link, hf CLI and curl.
- Browser
- Download file 455 MB
-
https://huggingface.co/johnowhitaker/Electronic_test/resolve/main/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://johnowhitaker/Electronic_test/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/johnowhitaker/Electronic_test/resolve/main/unet/diffusion_pytorch_model.bin
455 MB
- Xet hash:
- c15ee3b5d01d5b7d17b4364c0ecef733fc78438add057e94e3c6bc24c8292d7e
- Size of remote file:
- 455 MB
- SHA256:
- e2df20c2f90ef86643d1586341715679c0382b309c23ccaa5bebcc4a74a8fd2a
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