Instructions to use rrw23/pets3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rrw23/pets3 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/pets3") 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-3000/optimizer.bin from rrw23/pets3: direct link, hf CLI and curl.
- Browser
- Download file 6.59 MB
-
https://huggingface.co/rrw23/pets3/resolve/main/checkpoint-3000/optimizer.bin
- Command line
-
hf download hf://rrw23/pets3/checkpoint-3000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/rrw23/pets3/resolve/main/checkpoint-3000/optimizer.bin
6.59 MB
- Xet hash:
- b3333c7ecbd13a28b499827d44ea4917d43b17a57e4d705135eb98261a28492f
- Size of remote file:
- 6.59 MB
- SHA256:
- b1c33cad786b995aa84e88af24f972bd5b9d8e35fbbdd0041512a2a6865b01e3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.