Instructions to use InstantX/SD3-Controlnet-Canny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use InstantX/SD3-Controlnet-Canny with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("InstantX/SD3-Controlnet-Canny", 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:
- 7ac5dd642ba7641eb8c1f5a44066a7c79161af6834d1eabfd093767f793ab80d
- Size of remote file:
- 1.19 GB
- SHA256:
- 6e2ec71ac97cf9034aebb8a4f1a9143392f459bfd2777b1363aefe0d3eedde47
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.