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/scripts/vit_sbi.sh from deepsafe/deepsafe-services: direct link, hf CLI and curl.
- Browser
- Download file 299 Bytes
-
https://huggingface.co/deepsafe/deepsafe-services/resolve/3680c22ed9fca1657ae7b4c5c4f89042446aa503/video/fake-stormer/model_code/scripts/vit_sbi.sh
- Command line
-
hf download hf://deepsafe/deepsafe-services@3680c22ed9fca1657ae7b4c5c4f89042446aa503/video/fake-stormer/model_code/scripts/vit_sbi.sh
-
curl -L -o vit_sbi.sh https://huggingface.co/deepsafe/deepsafe-services/resolve/3680c22ed9fca1657ae7b4c5c4f89042446aa503/video/fake-stormer/model_code/scripts/vit_sbi.sh
299 Bytes
| CUDA_VISIBLE_DEVICES=0,1,2,3 python scripts/train.py --cfg configs/spatial/vit_sbi_small.yaml | |
| # CUDA_VISIBLE_DEVICES=0,1,2,3 python scripts/train.py --cfg configs/spatial/vit_sbi_base.yaml | |
| # CUDA_VISIBLE_DEVICES=0,1,2,3 python scripts/train.py --cfg configs/spatial/vit_sbi_large.yaml | |