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
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### lighting: the higher the value, the stronger the front light; the lower the value, the stronger the back light.
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# Continuously updating
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### lighting: the higher the value, the stronger the front light; the lower the value, the stronger the back light.
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### colortone: the higher the value, the stronger the warm color tone; the lower the value, the stronger the cool color tone.
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### skin: the higher the value, the stronger the white skin; the lower the value, the stronger the dark skin
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### weight: the higher the value, the stronger the fat; the lower the value, the stronger the thin
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# Continuously updating
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