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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Model description
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🤖Slider-toolkit : A set of Slider-LORA trained for Klein4B, which enhances the controllability of text-to-image generation. If you are looking for a fast and controllable text-to-image generation method, don't miss it.
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quality: the larger the value, the higher the image quality; the smaller the value, the worse the image quality.
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image
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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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image
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## I'm an independent model and workflow developer. If you like my work and want to support independent development, please consider buying me a cup of coffee to keep this motivation going! Thank you very much.
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Model description
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🤖Slider-toolkit : A set of Slider-LORA trained for Klein4B, which enhances the controllability of text-to-image generation. If you are looking for a fast and controllable text-to-image generation method, don't miss it.
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### quality: the larger the value, the higher the image quality; the smaller the value, the worse the image quality.
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image
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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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image
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# Continuously updating
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## I'm an independent model and workflow developer. If you like my work and want to support independent development, please consider buying me a cup of coffee to keep this motivation going! Thank you very much.
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