Instructions to use deepsafe/deepsafe-services with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use deepsafe/deepsafe-services with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("deepsafe/deepsafe-services", 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 video/fake-stormer/model_code/demo/method.png from deepsafe/deepsafe-services: direct link, hf CLI and curl.
- Browser
- Download file 3.61 MB
-
https://huggingface.co/deepsafe/deepsafe-services/resolve/main/video/fake-stormer/model_code/demo/method.png
- Command line
-
hf download hf://deepsafe/deepsafe-services/video/fake-stormer/model_code/demo/method.png
-
curl -L -o method.png https://huggingface.co/deepsafe/deepsafe-services/resolve/main/video/fake-stormer/model_code/demo/method.png
3.61 MB

- Xet hash:
- c10eeb8c6061ead9db543930af9085e3f18a25b6e70b0e5efe49b55812c16c84
- Size of remote file:
- 3.61 MB
- SHA256:
- d7c6acdff1e9e7673b97b2ef5000e63b23c4e7be75cf9a8a3ba3989d90ab1131
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.