Update app.py

app.py

@@ -130,6 +130,10 @@ In this demo, we explore the potential biases in text-to-image models by generat
 2. **Gender Detection**: The [BLIP caption generator](https://huggingface.co/Salesforce/blip-image-captioning-large) is used to elicit gender markers by identifying words like "man," "boy," "woman," and "girl" in the captions.
 3. **Skin Tone Classification**: The [skin-tone-classifier library](https://github.com/ChenglongMa/SkinToneClassifier) is used to extract the skin tones of the generated subjects.
 
+## Models
+
+- Sintetico XL: a merged model with my favorite aesthetics
+- Sintetico XL: a SFW version that aims to remove unwanted nudity and sexual content.
 
 #### Visualization
 
@@ -149,7 +153,7 @@ This demo provides an insightful look into how current text-to-image models hand
         ], 
         value=default_model
     )
-    prompt = gr.Textbox(label="Enter the Prompt", value = "photo of a beautiful Brazilian woman, high quality, good lighting")
+    prompt = gr.Textbox(label="Enter the Prompt", value = "photo of a Brazilian scientist, high quality, good lighting")
     gallery = gr.Gallery(
         label="Generated images", 
         show_label=False,