Instructions to use Zillis/lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Zillis/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("Zillis/lora", 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:
- 9e42b9572e3733e2b13fc14c33f40610850e1deb5aa2ad50ee00694a1f13325d
- Size of remote file:
- 307 MB
- SHA256:
- 31b839ffa92cbb7839d63615bbe39d7a7fcc7f46a174bbdc619110b2c6ae09b3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.