Documentation fixes.

app.py

@@ -305,13 +305,13 @@ with blocks:
     )
     gr.Markdown(
         "<h4 style='font-size: 110%;margin-top:.5em'>Usage</h4><div>Upload an image of your face, pick your desired output styles, and apply StyleGAN-based editing.</div>"
-        "<div>Choose the edit image tab to create static images in all chosen styles. Choose the video tab in order to interpolate between all chosen styles (But take it easy on the servers! We've limited video length, so if you add too many styles, they'll pass in the blink of an eye! 🤗)</div>"
+        "<div>Choose the edit image tab to create static images in all chosen styles. Choose the video tab in order to interpolate between all chosen styles</div><div>(To make it easier on the servers, we've limited video length. If you add too many styles (we recommend no more than 3!), they'll pass in the blink of an eye! 🤗)</div>"
     )
     gr.Markdown(
-        "For more information about the paper and code for training your own models (with examples OR text), please visit our <a href='https://stylegan-nada.github.io/' target='_blank'>Project Page</a> or the <a href='https://github.com/rinongal/StyleGAN-nada' target='_blank'>official repository</a>."
+        "For more information about the paper and code for training your own models (with text or images), please visit our <a href='https://stylegan-nada.github.io/' target='_blank'>project page</a> or the <a href='https://github.com/rinongal/StyleGAN-nada' target='_blank'>official repository</a>."
     )
 
-    gr.Markdown("<h4 style='font-size: 110%;margin-top:.5em'>A note on social impact</h4><div>This model relies on StyleGAN and CLIP, both of which are prone to biases inherited from their training data and their architecture. These may include (but are not limited to) poor representation of minorities or the perpetution of societal biases, such as gender norms. Moreover, generative models can, and have been used to create deep fake imagery which may assist in the spread of propaganda. However, <a href='https://github.com/NVlabs/stylegan3-detector' target='_blank'>tools are available</a> for identifying StyleGAN generated imagery, and any 'realistic' results produced by this model should be easily identifiable through such tools.</div>")
+    gr.Markdown("<h4 style='font-size: 110%;margin-top:.5em'>A note on social impact</h4><div>This model relies on StyleGAN and CLIP, both of which are prone to biases inherited from their training data and their architecture. These may include (but are not limited to) poor representation of minorities or the perpetution of societal biases, such as gender norms. In particular, StyleGAN editing may induce undesired changes in skin tones. Moreover, generative models can, and have been used to create deep fake imagery which may assist in the spread of propaganda. However, <a href='https://github.com/NVlabs/stylegan3-detector' target='_blank'>tools are available</a> for identifying StyleGAN generated imagery, and any 'realistic' results produced by this model should be easily identifiable through such tools.</div>")
     
     with gr.Row():
         input_img = gr.inputs.Image(type="filepath", label="Input image")