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 image/yermandy/weights/model.ckpt from deepsafe/deepsafe-services: direct link, hf CLI and curl.
- Browser
- Download file 1.21 GB
-
https://huggingface.co/deepsafe/deepsafe-services/resolve/main/image/yermandy/weights/model.ckpt
- Command line
-
hf download hf://deepsafe/deepsafe-services/image/yermandy/weights/model.ckpt
-
curl -L -o model.ckpt https://huggingface.co/deepsafe/deepsafe-services/resolve/main/image/yermandy/weights/model.ckpt
1.21 GB
- Xet hash:
- a696fe16bf7b425ce5e7303ee899d2cf440abbdcffe91733fc95c15cfe18c4b3
- Size of remote file:
- 1.21 GB
- SHA256:
- 57a0d00f2f5b4046afd2c344ff9877a35e8889e075916cf816796c54816c9955
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.