Instructions to use QFun/checkpoint_Sign with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use QFun/checkpoint_Sign with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("QFun/checkpoint_Sign") pipe = StableDiffusionControlNetPipeline.from_pretrained( "stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from QFun/checkpoint_Sign: direct link, hf CLI and curl.
- Browser
- Download file 470 Bytes
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https://huggingface.co/QFun/checkpoint_Sign/resolve/main/README.md
- Command line
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hf download hf://QFun/checkpoint_Sign/README.md
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curl -L -o README.md https://huggingface.co/QFun/checkpoint_Sign/resolve/main/README.md
470 Bytes
| license: openrail++ | |
| base_model: stabilityai/stable-diffusion-xl-base-1.0 | |
| tags: | |
| - stable-diffusion-xl | |
| - stable-diffusion-xl-diffusers | |
| - text-to-image | |
| - diffusers | |
| - controlnet | |
| inference: true | |
| # controlnet-QFun/checkpoint_Sign | |
| These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with new type of conditioning. | |
| You can find some example images below. | |
| prompt: | |
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| prompt: | |
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