Instructions to use JujoHotaru/lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JujoHotaru/lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fill-in-base-model", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("JujoHotaru/lora") 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

- Xet hash:
- 9318ca3ccde0c1404f62527f2130bf22fb3400f61fd262e8990eb49b2afac2c3
- Size of remote file:
- 2.66 MB
- SHA256:
- 4570a8165ad7f979a2b7015aaff8eddd44ad83d4cc2896c007cd2f7be3837604
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.