Instructions to use DazMashaly/ctrlNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DazMashaly/ctrlNet with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("DazMashaly/ctrlNet", 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:
- aa6a5f5283c0591da49e4ddd6feb2422845607b5cbd5b180d6243c8c273bc0f3
- Size of remote file:
- 14.6 kB
- SHA256:
- 8afcb9eabbf579faa12f227f8be477d48836c5e6858bab3c7e027b3969b80f4a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.