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 audio/sonics/weights/pytorch_model.bin from deepsafe/deepsafe-services: direct link, hf CLI and curl.
- Browser
- Download file 97.8 MB
-
https://huggingface.co/deepsafe/deepsafe-services/resolve/main/audio/sonics/weights/pytorch_model.bin
- Command line
-
hf download hf://deepsafe/deepsafe-services/audio/sonics/weights/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/deepsafe/deepsafe-services/resolve/main/audio/sonics/weights/pytorch_model.bin
97.8 MB
- Xet hash:
- 6f326c9cb2666717a1b0104a3b42d1c95dfc8eacd23ba4af512c284e7be1bf6f
- Size of remote file:
- 97.8 MB
- SHA256:
- bd50a6f2f120d3911470933c0d2d7135f89b451e2446b42b3d96ecf7c8255371
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.