Instructions to use ssmaameri/qrcode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ssmaameri/qrcode with Diffusers:
pip install -U diffusers transformers accelerate
from diffusers import ControlNetModel, StableDiffusionControlNetPipeline controlnet = ControlNetModel.from_pretrained("ssmaameri/qrcode") pipe = StableDiffusionControlNetPipeline.from_pretrained( "fill-in-base-model", controlnet=controlnet ) - Notebooks
- Google Colab
- Kaggle
Download diffusion_pytorch_model.fp16.bin from ssmaameri/qrcode: direct link, hf CLI and curl.
- Browser
- Download file 729 MB
-
https://huggingface.co/ssmaameri/qrcode/resolve/main/diffusion_pytorch_model.fp16.bin
- Command line
-
hf download hf://ssmaameri/qrcode/diffusion_pytorch_model.fp16.bin
-
curl -L -o diffusion_pytorch_model.fp16.bin https://huggingface.co/ssmaameri/qrcode/resolve/main/diffusion_pytorch_model.fp16.bin
729 MB
- Xet hash:
- 52a4e839497d70f5bf180a8c57b93f9a0596fa26cd5ac4abc1e13dc7c7ff614f
- Size of remote file:
- 729 MB
- SHA256:
- 8b3daef6325b0c8e453a30b98cebc146bece31658ac15c178ce122071f7301c3
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.