update

app.py

@@ -403,6 +403,9 @@ with gr.Blocks() as demo:
         <h2 style="text-align: left; font-weight: 450; font-size: 1rem; margin-top: 0.5rem; margin-bottom: 0.5rem">
         👀 <b>Tips for using this demo</b>: <b>(1)</b> Please carefully read the disclaimer in the below. <b>(2)</b> The specification of keywords is optional. If provided, the language model will do its best to plan layouts using the given keywords. <b>(3)</b> If a template is given, the layout planner (M1) is not used. <b>(4)</b> Three operations, including redo, undo, and skip are provided. When using skip, only the left-top point of a keyword will be recorded, resulting in more diversity but sometimes decreasing the accuracy. <b>(5)</b> The layout planner can produce different layouts. You can increase the temperature to enhance the diversity.
         </h2>
+        <h2 style="text-align: left; font-weight: 450; font-size: 1rem; margin-top: 0.5rem; margin-bottom: 0.5rem">
+        ✨ <b>[L] [C] [M]</b>: We also provide the model combining <b>TextDiffuser-2</b> and <b>LCM</b>. The inference speed is fast using less sampling steps. 
+        </h2>
 
         <style>
             .scaled-image {
@@ -438,7 +441,7 @@ with gr.Blocks() as demo:
                 skip_button.click(skip_fun, [i,t])
 
                 radio = gr.Radio(["TextDiffuser-2", "TextDiffuser-2-LCM"], label="Choice of models", value="TextDiffuser-2")
-                slider_step = gr.Slider(minimum=1, maximum=50, value=20, step=1, label="Sampling step", info="The sampling step for TextDiffuser-2.")
+                slider_step = gr.Slider(minimum=1, maximum=50, value=20, step=1, label="Sampling step", info="The sampling step for TextDiffuser-2. You may decease the step to 4 when using LCM.")
                 slider_guidance = gr.Slider(minimum=1, maximum=9, value=7.5, step=0.5, label="Scale of classifier-free guidance", info="The scale of cfg and is set to 7.5 in default. When using LCM, cfg is set to 1.")
                 slider_batch = gr.Slider(minimum=1, maximum=4, value=4, step=1, label="Batch size", info="The number of images to be sampled.")
                 slider_temperature = gr.Slider(minimum=0.1, maximum=2, value=0.7, step=0.1, label="Temperature", info="Control the diversity of layout planner. Higher value indicates more diversity.")