Instructions to use DankCloth/Output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DankCloth/Output with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("DankCloth/Output", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of owen wilson" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- cb19a54ed983039948f25f3165f755e72cbe2f8095b44c04a54cfa7bdab74ac6
- Size of remote file:
- 9.6 MB
- SHA256:
- 10a8bdca795f7423b7bd127350da116a650a7651e29702cc8c637adb5c154ec4
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