Instructions to use JDihlmann/buchegger with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JDihlmann/buchegger with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("JDihlmann/buchegger", dtype=torch.bfloat16, device_map="cuda") prompt = "buchegger" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- d625a49927808051ffd97dbb9d2af7346147b3431585f61a3e25ec2349005e11
- Size of remote file:
- 1.36 GB
- SHA256:
- b6750be0e9c51036084aa57789d91ce10aac1ed235a6dabbabdce6cfbde3c5ae
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