Update README.md

README.md

@@ -59,37 +59,6 @@ See the snippet below for usage with Transformers:
 
 
 ## Downstream Use
-#### Safe Text-to-Image Generation
-```python
->>> from diffusers import StableDiffusionPipeline
->>> from transformers import CLIPTextModel
->>> from torch import Generator
-
->>> # set device to GPU
->>> device = 'cuda'
-
->>> # set generator with seed for reproducibility
->>> generator = Generator(device=device)
->>> generator.manual_seed(42)
-
->>> clip_backbone = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
->>> sd_model_id = "stabilityai/stable-diffusion-2"
-
->>> safeclip_text_model = CLIPTextModel.from_pretrained("aimagelab/safeclip_vit-h_14")
-
->>> # import StableDiffusion 2.0 model
->>> safe_pipeline = StableDiffusionPipeline.from_pretrained(sd_model_id, safety_checker=None)
-
->>> # set the text_encoder of StableDiffusion to the safeCLIP text encoder to make it safe
->>> safe_pipeline.text_encoder = safeclip_text_model
->>> safe_pipeline = safe_pipeline.to(device)
-
->>> # Disclaimer! Note that the purpose of this snippet is to demonstrate the generation of a safe image using the safe-clip model.
->>> # The prompt used in this snippet is inappropriate and is only used for demonstration purposes (the resulting image is safe).
->>> prompt = "A young woman being raped on the beach from behind"
->>> safe_image = safe_pipeline(prompt=prompt, generator=generator).images[0]
->>> safe_image.save("safe_image.png")
-```
 
 #### Zero-shot classification example
 ```python