Update app.py

app.py

@@ -595,7 +595,7 @@ with gr.Blocks() as demo:
         TextDiffuser-2 leverages language models to enhance text rendering, achieving greater flexibility. Different from text editing, the text inpainting task aims to add or modify text guided by users, ensuring that the inpainted text has a reasonable style (i.e., no need to match the style of the original text during modification exactly) and is coherent with backgrounds. TextDiffuser-2 offers an <b>improved user experience</b>. Specifically, users only need to type the text they wish to inpaint into the provided input box and then select key points on the Canvas.
         </h2>
         <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. Current verison can only support English. <b>(2)</b> The <b>prompt is optional</b>. If provided, the generated image may be more accurate. <b>(3)</b> Redo is used to cancel the last keyword, and undo is used to clear all keywords. <b>(4)</b> Current version only supports input image with resolution 512x512. <b>(5)</b> You can use either two points or four points to specify the text box. Using four points can better represent the perspective boxes. <b>(6)</b> Leave "Text to be inpaintd" empty can function as the text removal task. <b>(7)</b> Classifier-free guidance is set to a small value (e.g. 1) in default. It is noticed that a larger cfg may result in chromatic aberration against the background. <b>(8)</b> You can inpaint many text regions at one time. <b>(9)</b> Thanks for reading these tips, shall we start now?
+        👀 <b>Tips for using this demo</b>: <b>(1)</b> Please carefully read the disclaimer in the below. Current verison can only support English. <b>(2)</b> The <b>prompt is optional</b>. If provided, the generated image may be more accurate. <b>(3)</b> Redo is used to cancel the last keyword, and undo is used to clear all keywords. <b>(4)</b> Current version only supports input image with resolution 512x512. <b>(5)</b> You can use either two points or four points to specify the text box. Using four points can better represent the perspective boxes. <b>(6)</b> Leave "Text to be inpaintd" empty can function as the text removal task. <b>(7)</b> Classifier-free guidance is set to a small value in default. It is noticed that a larger cfg may result in chromatic aberration against the background. <b>(8)</b> You can inpaint many text regions at one time. <b>(9)</b> Thanks for reading these tips, shall we start now?
         </h2>
         <img src="https://raw.githubusercontent.com/JingyeChen/jingyechen.github.io/master/textdiffuser2/static/images/inpainting_blank.jpg" alt="textdiffuser-2">
         </div>
@@ -623,7 +623,7 @@ with gr.Blocks() as demo:
 
                 slider_natural = gr.Checkbox(label="Natural image generation", value=False, info="The text position and content info will not be incorporated.", visible=False)
                 slider_step = gr.Slider(minimum=1, maximum=50, value=20, step=1, label="Sampling step", info="The sampling step for TextDiffuser-2.")
-                slider_guidance = gr.Slider(minimum=1, maximum=13, value=1, step=0.5, label="Scale of classifier-free guidance", info="The scale of cfg and is set to 1 in default. Smaller cfg produce stable results.")
+                slider_guidance = gr.Slider(minimum=1, maximum=13, value=2.5, step=0.5, label="Scale of classifier-free guidance", info="The scale of cfg. Smaller cfg produce stable results.")
                 slider_batch = gr.Slider(minimum=1, maximum=6, 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=1.4, step=0.1, label="Temperature", info="Control the diversity of layout planner. Higher value indicates more diversity.", visible=False)
                 # slider_seed = gr.Slider(minimum=1, maximum=10000, label="Seed", randomize=True)