Instructions to use antonellaavad/unlight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use antonellaavad/unlight with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("WarriorMama777/AbyssOrangeMix2", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("antonellaavad/unlight") prompt = "unlight" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- c769a6fdb210b3cc31f492b6df1bbf2c45fc6a47f10951e15f61a8219f798358
- Size of remote file:
- 3.29 MB
- SHA256:
- ad9dfbc0382a153c9c299fb22726ba57190d9333820e28168f226239ec724563
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.