Instructions to use Abhi5ingh/model_out with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Abhi5ingh/model_out with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("Abhi5ingh/model_out") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download diffusion_pytorch_model.bin from Abhi5ingh/model_out: direct link, hf CLI and curl.
- Browser
- Download file 1.45 GB
-
https://huggingface.co/Abhi5ingh/model_out/resolve/main/diffusion_pytorch_model.bin
- Command line
-
hf download hf://Abhi5ingh/model_out/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/Abhi5ingh/model_out/resolve/main/diffusion_pytorch_model.bin
1.45 GB
- Xet hash:
- c57d1c906278fef3f9a4df0229671a8d0f2abf525a57545fe309c89a95ab1cd3
- Size of remote file:
- 1.45 GB
- SHA256:
- f2246349413e8769e9b7daec172e1911f8b1ef4fdab985d9795c9a0ba0c7c519
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.