Use the `safety_concept` property of [`StableDiffusionPipelineSafe`] to check and edit the current safety concept:  
```python
>>> from diffusers import StableDiffusionPipelineSafe

>>> pipeline = StableDiffusionPipelineSafe.from_pretrained("AIML-TUDA/stable-diffusion-safe")
>>> pipeline.safety_concept
'an image showing hate, harassment, violence, suffering, humiliation, harm, suicide, sexual, nudity, bodily fluids, blood, obscene gestures, illegal activity, drug use, theft, vandalism, weapons, child abuse, brutality, cruelty'
```
For each image generation the active concept is also contained in [`StableDiffusionSafePipelineOutput`].  
There are 4 configurations (`SafetyConfig.WEAK`, `SafetyConfig.MEDIUM`, `SafetyConfig.STRONG`, and `SafetyConfig.MAX`) that can be applied:  
```python
>>> from diffusers import StableDiffusionPipelineSafe
>>> from diffusers.pipelines.stable_diffusion_safe import SafetyConfig

>>> pipeline = StableDiffusionPipelineSafe.from_pretrained("AIML-TUDA/stable-diffusion-safe")
>>> prompt = "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c. leyendecker"
>>> out = pipeline(prompt=prompt, **SafetyConfig.MAX)
```  
<Tip>  
Make sure to check out the Stable Diffusion [Tips](overview#tips) section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently!  
</Tip>
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_safe.md