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 Settings
- Draw Things
- DiffusionBee
|
Download README.md from Visualignment/safe-SDXL: direct link, hf CLI and curl.
- Browser
- Download file 870 Bytes
-
https://huggingface.co/Visualignment/safe-SDXL/resolve/refs%2Fpr%2F1/README.md
- Command line
-
hf download hf://Visualignment/safe-SDXL@refs/pr/1/README.md
-
curl -L -o README.md https://huggingface.co/Visualignment/safe-SDXL/resolve/refs%2Fpr%2F1/README.md
870 Bytes
metadata
library_name: diffusers
license: mit
pipeline_tag: text-to-image
This is the official released checkpoint of an aligned SDXL v1.0 from the paper AlignGuard: Scalable Safety Alignment for Text-to-Image Generation, designed to generate more safe images from our Safe-SDXL.
Our project page is 🏠SafetyDPO HomePage and the GitHub repo is ⚙️SafetyDPO GitHub 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]