Instructions to use Joscelino/padrecelino with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Joscelino/padrecelino with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Joscelino/padrecelino", dtype=torch.bfloat16, device_map="cuda") prompt = "Padrecelino" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download text_encoder/pytorch_model.bin from Joscelino/padrecelino: direct link, hf CLI and curl.
- Browser
- Download file 1.36 GB
-
https://huggingface.co/Joscelino/padrecelino/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://Joscelino/padrecelino/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Joscelino/padrecelino/resolve/main/text_encoder/pytorch_model.bin
1.36 GB
- Xet hash:
- e71ae6ae18954f23d214772be337fdae8b9dab1a4f6fb992ceeb831fa5719ebb
- Size of remote file:
- 1.36 GB
- SHA256:
- 6954f4de02308fefe26a196f607e0623c446d85aaa3284a98527b579425eadc3
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