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:
- da9c6ac4b60bfa00350d17c0deaa7dff32757b0c271540e457a41d87dba2ddac
- Size of remote file:
- 2.13 GB
- SHA256:
- b0589cb4eb6d990d487ca93d1abbf8d24d26b0b8eabad2b4d6818dc2ea752e1f
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