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-10500/optimizer.bin from rrw23/pets: direct link, hf CLI and curl.
- Browser
- Download file 6.59 MB
-
https://huggingface.co/rrw23/pets/resolve/main/checkpoint-10500/optimizer.bin
- Command line
-
hf download hf://rrw23/pets/checkpoint-10500/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/rrw23/pets/resolve/main/checkpoint-10500/optimizer.bin
6.59 MB
- Xet hash:
- e1d924886a81eb184df06a4d7f5947832b97ca11d01dbe3bfcab380f516ff7ed
- Size of remote file:
- 6.59 MB
- SHA256:
- be44368f0790a43e68ba917e6f59a8e9375f8b80a1d376997779540c009e4961
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.