Instructions to use NormalUhr/UnlearnCanvas-ckpts with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use NormalUhr/UnlearnCanvas-ckpts with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("NormalUhr/UnlearnCanvas-ckpts", 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/compvis/compvis.ckpt from NormalUhr/UnlearnCanvas-ckpts: direct link, hf CLI and curl.
- Browser
- Download file 7.7 GB
-
https://huggingface.co/NormalUhr/UnlearnCanvas-ckpts/resolve/main/diffusion/compvis/compvis.ckpt
- Command line
-
hf download hf://NormalUhr/UnlearnCanvas-ckpts/diffusion/compvis/compvis.ckpt
-
curl -L -o compvis.ckpt https://huggingface.co/NormalUhr/UnlearnCanvas-ckpts/resolve/main/diffusion/compvis/compvis.ckpt
7.7 GB
- Xet hash:
- 4fd77c48650911e8cffaed5c020e6b10b9c7058200d4dc02903b01f6848d435d
- Size of remote file:
- 7.7 GB
- SHA256:
- fc8a2e23460d19acc0a8f4f4aa3963c37461cd9f78cac8c109eeb2f131c18e2e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.