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 Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 8d3e34f5f27e8da1f47f09bf973fb771c158996c0a362c496a65334a230746ad
- Size of remote file:
- 3.44 GB
- SHA256:
- 59aef336de6cf002670979ebdaf9f19b3e3c8f3b5294d9a4cf8a9e37f8e98496
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