Instructions to use JaspervanLeuven/controlnetLarge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JaspervanLeuven/controlnetLarge with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("JaspervanLeuven/controlnetLarge", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download checkpoint-8000/optimizer.bin from JaspervanLeuven/controlnetLarge: direct link, hf CLI and curl.
- Browser
- Download file 2.89 GB
-
https://huggingface.co/JaspervanLeuven/controlnetLarge/resolve/main/checkpoint-8000/optimizer.bin
- Command line
-
hf download hf://JaspervanLeuven/controlnetLarge/checkpoint-8000/optimizer.bin
-
curl -L -o optimizer.bin https://huggingface.co/JaspervanLeuven/controlnetLarge/resolve/main/checkpoint-8000/optimizer.bin
2.89 GB
- Xet hash:
- 5f64a593953fece46bd0b5af15b47f1afcf4fc258f99c8107b0d56edf53ba350
- Size of remote file:
- 2.89 GB
- SHA256:
- b66d01c868302ef488dbc158d8ab5e7ece6f47534051162d6548bab7cfb71ef8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.