Instructions to use rrw23/pets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rrw23/pets with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("rrw23/pets") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download checkpoint-12500/pytorch_model.bin from rrw23/pets: direct link, hf CLI and curl.
- Browser
- Download file 3.29 MB
-
https://huggingface.co/rrw23/pets/resolve/main/checkpoint-12500/pytorch_model.bin
- Command line
-
hf download hf://rrw23/pets/checkpoint-12500/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rrw23/pets/resolve/main/checkpoint-12500/pytorch_model.bin
3.29 MB
- Xet hash:
- 27cec7a3c2c605c653f946c397433fd76be193b01b98422639806685387f5461
- Size of remote file:
- 3.29 MB
- SHA256:
- 684014b9d12c7f0ef4f53265f90204132dd02613e83f10dcf53b1fb5e61ede25
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.