Instructions to use Visualignment/safe-SDXL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Visualignment/safe-SDXL with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Visualignment/safe-SDXL", 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
- Local Apps
- Draw Things
- DiffusionBee
| library_name: diffusers | |
| license: mit | |
| This is the official released checkpoint of **an aligned SDXL v1.0** of SafetyDPO: Scalable Safety Alignment for Text-to-Image Generation, designed to generate more safe images from our Safe-SDXL. | |
| Our project page is [🏠SafetyDPO HomePage](https://safetydpo.github.io/) and the GitHub repo is [⚙️SafetyDPO GitHub](https://github.com/Visualignment/SafetyDPO) where we released all the code and the data. | |
| In the future, we will release additional safe models. | |
| # Usage | |
| A simple use case of our model is: | |
| ``` | |
| from diffusers import DiffusionPipeline | |
| pipe = DiffusionPipeline.from_pretrained("Visualignment/safe-SDXL") | |
| prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" | |
| image = pipe(prompt).images[0] | |
| ``` |