Instructions to use xdcdtd/control_v11e_sd15_ip2p with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use xdcdtd/control_v11e_sd15_ip2p with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("xdcdtd/control_v11e_sd15_ip2p") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
Download diffusion_pytorch_model.fp16.bin from xdcdtd/control_v11e_sd15_ip2p: direct link, hf CLI and curl.
- Browser
- Download file 723 MB
-
https://huggingface.co/xdcdtd/control_v11e_sd15_ip2p/resolve/main/diffusion_pytorch_model.fp16.bin
- Command line
-
hf download hf://xdcdtd/control_v11e_sd15_ip2p/diffusion_pytorch_model.fp16.bin
-
curl -L -o diffusion_pytorch_model.fp16.bin https://huggingface.co/xdcdtd/control_v11e_sd15_ip2p/resolve/main/diffusion_pytorch_model.fp16.bin
723 MB
- Xet hash:
- 2b37ad5e31543831735513d40590f774d4c65a7a4ba8c3afe2b362e69fc5d2b4
- Size of remote file:
- 723 MB
- SHA256:
- 85a06695c394456f1f2031875209805fbf4fed3e44c1535ccc602f8bb12a412b
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