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
-
https://huggingface.co/QFun/checkpoint_Sign/resolve/main/README.md
- Command line
-
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
metadata
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:
prompt: