Instructions to use Jinstudio/control_v11e_sd15_ip2p with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Jinstudio/control_v11e_sd15_ip2p with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("Jinstudio/control_v11e_sd15_ip2p") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
Download diffusion_pytorch_model.bin from Jinstudio/control_v11e_sd15_ip2p: direct link, hf CLI and curl.
- Browser
- Download file 1.45 GB
-
https://huggingface.co/Jinstudio/control_v11e_sd15_ip2p/resolve/main/diffusion_pytorch_model.bin
- Command line
-
hf download hf://Jinstudio/control_v11e_sd15_ip2p/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/Jinstudio/control_v11e_sd15_ip2p/resolve/main/diffusion_pytorch_model.bin
1.45 GB
- Xet hash:
- d57b8225e9ce88781e38049c1ea2874271b8515f536079c7d49529e050aa8465
- Size of remote file:
- 1.45 GB
- SHA256:
- 318f239682150aaa8b9d0cadf529b4e7e17db42d08a01aa2b52720e31b311c27
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