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/requirements.txt from deepsafe/deepsafe-services: direct link, hf CLI and curl.
- Browser
- Download file 275 Bytes
-
https://huggingface.co/deepsafe/deepsafe-services/resolve/3680c22ed9fca1657ae7b4c5c4f89042446aa503/video/fake-stormer/requirements.txt
- Command line
-
hf download hf://deepsafe/deepsafe-services@3680c22ed9fca1657ae7b4c5c4f89042446aa503/video/fake-stormer/requirements.txt
-
curl -L -o requirements.txt https://huggingface.co/deepsafe/deepsafe-services/resolve/3680c22ed9fca1657ae7b4c5c4f89042446aa503/video/fake-stormer/requirements.txt
275 Bytes
| fastapi | |
| uvicorn | |
| pydantic | |
| python-multipart | |
| torch>=1.8.0 | |
| torchvision>=0.9.0 | |
| opencv-python-headless | |
| numpy<2.0.0 | |
| Pillow | |
| PyYAML | |
| natsort | |
| tqdm | |
| scikit-image | |
| albumentations==1.1.0 | |
| imgaug==0.4.0 | |
| tensorboardX==2.5.1 | |
| plotly | |
| simplejson | |
| ptflops | |
| mmengine | |
| einops | |
| timm | |
| python-box | |
| mmcv==1.6.1 | |