oops

app.py

@@ -22,7 +22,7 @@ with gr.Blocks() as demo:
      <p style="margin-bottom: 10px; font-size: 94%"> Example images generated by three text-to-image models (Dall-E 2, Stable Diffusion v1.4 and v.2). </p>
           ''')    
     gr.HTML('''
-     <p style="margin-bottom: 14px; font-size: 100%">  As AI-enabled Text-to-Image systems are becoming increasingly used, characterizing the social biases they exhibit is a necessary first step to lowering their risk of discriminatory outcomes. <br> We propose a new method for exploring and quantifying social biases in these kinds of systems by directly comparing collections of generated images designed to showcase a system’s variation across social attributes — gender and ethnicity — and target attributes for bias evaluation — professions and gender-coded adjectives. <br> We compare three models: Stable Diffusion v.1.4, Stable Diffusion v.2., and Dall-E 2 and find <b> clear evidence of ethnicity and gender biases <b>. <br> You can explore these findings in the collapsed sections below, which present our findings: </p>
+     <p style="margin-bottom: 14px; font-size: 100%">  As AI-enabled Text-to-Image systems are becoming increasingly used, characterizing the social biases they exhibit is a necessary first step to lowering their risk of discriminatory outcomes. <br> We propose a new method for exploring and quantifying social biases in these kinds of systems by directly comparing collections of generated images designed to showcase a system’s variation across social attributes — gender and ethnicity — and target attributes for bias evaluation — professions and gender-coded adjectives. <br> We compare three models: Stable Diffusion v.1.4, Stable Diffusion v.2., and Dall-E 2 and find <b> clear evidence of ethnicity and gender biases </b>. <br> You can explore these findings in the collapsed sections below, which present our findings: </p>
           ''')    
 
     with gr.Accordion("Identity group results (ethnicity and gender)", open=False):