Instructions to use wjque/lyra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wjque/lyra with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("wjque/lyra", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6d4d44ef851769a06e572cb1e6d1a768da3fe544f316e069d0903b17f484e447
- Size of remote file:
- 3.15 GB
- SHA256:
- 5ca9c5e04e26fdbe37ea4819ea623d76c69747f9fcbacd5ca47f43e49cbfbd9f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.