Instructions to use DavyMorgan/sd3-controlnet-out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DavyMorgan/sd3-controlnet-out with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("DavyMorgan/sd3-controlnet-out", 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:
- f10154f5b8876004729348806c729e23001676b560e9e66cc8dfcf5fb4f08879
- Size of remote file:
- 4.48 GB
- SHA256:
- a52312587e8635dbebfa95b91ce7ff34e1969752683d8862e6eea36e590a4e67
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.