Text-to-Image
Diffusers
Safetensors
stable-diffusion
stable-diffusion-diffusers
controlnet
diffusers-training
Instructions to use wjworld/controlnet_quilt1m_256 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wjworld/controlnet_quilt1m_256 with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("wjworld/controlnet_quilt1m_256") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download optimizer.bin from wjworld/controlnet_quilt1m_256: direct link, hf CLI and curl.
- Browser
- Download file 2.89 GB
-
https://huggingface.co/wjworld/controlnet_quilt1m_256/resolve/main/optimizer.bin
- Command line
-
hf download hf://wjworld/controlnet_quilt1m_256/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/wjworld/controlnet_quilt1m_256/resolve/main/optimizer.bin
2.89 GB
- Xet hash:
- 0be71e639f418c97c4e921312a0ff19117d8a2522347c1c1cd5483a3bb70b911
- Size of remote file:
- 2.89 GB
- SHA256:
- e299c3ba329c514f5c34fcab5d923a6f7eb36e4f8a91d61f6d34a8ec6b4dc95b
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