Instructions to use jrd971000/out_dir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jrd971000/out_dir 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("jrd971000/out_dir") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- e2f33097b1d00215abb3b2f579fad1bcd1ff281462554e96e7c9df6f3c10890a
- Size of remote file:
- 6.59 MB
- SHA256:
- d084323dcfbb3bce6f324e2a0744113c76960f45486b778801c42856489aec7b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.