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
- Draw Things
- DiffusionBee
- Xet hash:
- 408b750c11c454d50a0f9e074693a7cd0f55b0eed74a0f6431325d2ab8303d5c
- Size of remote file:
- 41.4 MB
- SHA256:
- 066c82bac8884f00422d4dfda243dcddeef5dae7a3f5bdc9b85fc971371cb35d
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