update

app.py

@@ -425,7 +425,7 @@ with gr.Blocks() as demo:
         We propose <b>TextDiffuser-2</b>, aiming at unleashing the power of language models for text rendering. Specifically, we <b>tame a language model into a layout planner</b> to transform user prompt into a layout using the caption-OCR pairs. The language model demonstrates flexibility and automation by inferring keywords from user prompts or incorporating user-specified keywords to determine their positions. Secondly, we <b>leverage the language model in the diffusion model as the layout encoder</b> to represent the position and content of text at the line level. This approach enables diffusion models to generate text images with broader diversity.
         </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. <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. ✨ <b>(6)</b> We also provide the experimental demo combining <b>TextDiffuser-2</b> and <b>LCM</b>. The inference is fast using less sampling steps, although the precision in text rendering might decrease.
+        👀 <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 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. ✨ <b>(6)</b> We also provide the experimental demo combining <b>TextDiffuser-2</b> and <b>LCM</b>. The inference is fast using less sampling steps, although the precision in text rendering might decrease.
         </h2>
 
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