Instructions to use Hackenbacker/Bdan with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Hackenbacker/Bdan with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Hackenbacker/Bdan", 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
- Local Apps
- Draw Things
- DiffusionBee
Add Diffusers weights
#1
by Hackenbacker - opened
Add Diffusers weights converted from checkpoint bdan.ckpt in revision 45b36939501cd97743ebfb16211fb0ac61d2e542
Hackenbacker changed pull request status to closed