Update app.py

app.py

@@ -287,7 +287,7 @@ examples = [[examples_1, 'GLIDE', 10], [examples_2, 'GLIDE', 10], [examples_3, '
 import gradio as gr
 
 title = 'Compositional Visual Generation with Composable Diffusion Models'
-description = '<p>Demo for Composable Diffusion (~20s per example)</p><p>See more information from our <a href="https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/">Project Page</a>.</p><ul><li>One version is based on the released <a href="https://github.com/openai/glide-text2im">GLIDE</a> for composing natural language description.</li><li>Another is based on our pre-trained CLEVR Object Model for composing objects. <br>(<b>Note</b>: We recommend using <b><i>x</i></b> in range <b><i>[0.1, 0.9]</i></b> and <b><i>y</i></b> in range <b><i>[0.25, 0.7]</i></b>, since the training dataset labels are in given ranges.)</li></ul><p>When composing  multiple sentences, use `|` as the delimiter, see given examples below.</p>'
+description = '<p>Demo for Composable Diffusion (~20s per example if gpu is used, otherwise it will take quite a bit of time.)</p><p>See more information from our <a href="https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/">Project Page</a>.</p><ul><li>One version is based on the released <a href="https://github.com/openai/glide-text2im">GLIDE</a> for composing natural language description.</li><li>Another is based on our pre-trained CLEVR Object Model for composing objects. <br>(<b>Note</b>: We recommend using <b><i>x</i></b> in range <b><i>[0.1, 0.9]</i></b> and <b><i>y</i></b> in range <b><i>[0.25, 0.7]</i></b>, since the training dataset labels are in given ranges.)</li></ul><p>When composing  multiple sentences, use `|` as the delimiter, see given examples below.</p>'
 
 iface = gr.Interface(compose, inputs=["text", gr.inputs.Radio(['GLIDE','CLEVR Objects'], type="value", default='GLIDE', label='version'), gr.Slider(1, 10)], outputs='image', title=title, description=description, examples=examples)