Instructions to use lvarshini/kiara with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use lvarshini/kiara with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lvarshini/kiara", dtype=torch.bfloat16, device_map="cuda") prompt = "kiara" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 5c65e13d07ad54e3a83946d81efde880c045e6d04d333d9aa8339dd84dca8c9d
- Size of remote file:
- 492 MB
- SHA256:
- 3f991e32001e2ac6508f90edb326efbbe74edfbe5402c0814de5d8985b72011f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.