Instructions to use rippertnt/sd15-controlnet-landmarks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use rippertnt/sd15-controlnet-landmarks with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("rippertnt/sd15-controlnet-landmarks", 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 diffusion_pytorch_model.bin from rippertnt/sd15-controlnet-landmarks: direct link, hf CLI and curl.
- Browser
- Download file 1.45 GB
-
https://huggingface.co/rippertnt/sd15-controlnet-landmarks/resolve/main/diffusion_pytorch_model.bin
- Command line
-
hf download hf://rippertnt/sd15-controlnet-landmarks/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/rippertnt/sd15-controlnet-landmarks/resolve/main/diffusion_pytorch_model.bin
1.45 GB
- Xet hash:
- 136f19f356b3bd85da8208b42ccba2a7d336ffcaf114347a5c48b39ab745b9d5
- Size of remote file:
- 1.45 GB
- SHA256:
- edc1fb649aa55885ec70fd0bc4ebc906e5d10774c2feed09df029ebfd3c20b36
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