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", 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
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("WarriorMama777/AbyssOrangeMix2", dtype=torch.bfloat16, device_map="cuda")
pipe.load_lora_weights("antonellaavad/unlight")
prompt = "unlight"
image = pipe(prompt).images[0]LoRA DreamBooth - unlight
These are LoRA adaption weights for WarriorMama777/AbyssOrangeMix2. The weights were trained on the instance prompt "unlight" using DreamBooth. You can find some example images in the following.
- Downloads last month
- 3
Model tree for antonellaavad/unlight
Base model
WarriorMama777/AbyssOrangeMix2


