Instructions to use NO8D/ImagingControl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use NO8D/ImagingControl with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("NO8D/ImagingControl", dtype=torch.bfloat16, device_map="cuda") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 15e808cb8443792fb5d37afe36bf4ab66cfbff3e51e0be5f60a832b470893496
- Size of remote file:
- 41.4 MB
- SHA256:
- 6cafe277b0ddbe5191a2e68e980a8f6ae4fd19a217a49fc125aa7698619f056c
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