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/losses/builder.py from deepsafe/deepsafe-services: direct link, hf CLI and curl.
- Browser
- Download file 334 Bytes
-
https://huggingface.co/deepsafe/deepsafe-services/resolve/3680c22ed9fca1657ae7b4c5c4f89042446aa503/video/fake-stormer/model_code/losses/builder.py
- Command line
-
hf download hf://deepsafe/deepsafe-services@3680c22ed9fca1657ae7b4c5c4f89042446aa503/video/fake-stormer/model_code/losses/builder.py
-
curl -L -o builder.py https://huggingface.co/deepsafe/deepsafe-services/resolve/3680c22ed9fca1657ae7b4c5c4f89042446aa503/video/fake-stormer/model_code/losses/builder.py
334 Bytes
| # -*- coding: utf-8 -*- | |
| from typing import Any, Dict, Optional | |
| from register.register import Registry, build_from_cfg | |
| LOSSES = Registry("Loss") | |
| def build_losses( | |
| cfg, | |
| loss_func: Registry, | |
| build_func=build_from_cfg, | |
| default_args: Optional[Dict] = None, | |
| ) -> Any: | |
| return build_func(cfg, loss_func, default_args) | |