Instructions to use augh2000/aughaugh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use augh2000/aughaugh with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("lodestones/Chroma", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("augh2000/aughaugh") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 81c5f1cd5bfd39456f7ec2324ce440e6112cf5ee4e8ebee26630e4e44c400b16
- Size of remote file:
- 19 MB
- SHA256:
- 3f02f1d1287bbebb584412854dfea02085805e2f9a130cfb2ac9c9a59f45290f
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