Instructions to use ManojKhanal/controlnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ManojKhanal/controlnet with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ManojKhanal/controlnet", 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:
- 9638fac5185cc8648170303f28ca7b9727ffd1ff27847b202c02054167eee1b2
- Size of remote file:
- 1.46 GB
- SHA256:
- 7c1196e8b802a3002202dd52437a1cf36dc5bc459e9c5d55d397d3479b179659
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.