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:
- a7ea1f553f75b7a7cb15bb8e8c3dd8207df80f29d6af708b9f7476cbffe870ad
- Size of remote file:
- 246 MB
- SHA256:
- 97d199da7fd6007d6ff1b5c2f293aeef169d3172ecea08f3b9eadd2392f387aa
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