Instructions to use faverogian/Smithsonian128ControlNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use faverogian/Smithsonian128ControlNet with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("faverogian/Smithsonian128ControlNet", 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
- Xet hash:
- 53a31cfc03101189be1700f64d1f1c7aa8feb15694c667010083edbefdee5079
- Size of remote file:
- 157 MB
- SHA256:
- 596dadedd97d60ba47bea3a448a80f000e892472d5f52864dd7324510bf34874
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