Update app.py

app.py

@@ -404,7 +404,7 @@ def process_images_for_task_type(images_state: List[Image.Image], task_type: str
 
 with gr.Blocks(title="OneDiffusion Demo") as demo:
     gr.Markdown("""
-    # OneDiffusion Demo
+    # OneDiffusion Demo with LLaVA captioner
 
     **Welcome to the OneDiffusion Demo!**
 
@@ -414,7 +414,7 @@ with gr.Blocks(title="OneDiffusion Demo") as demo:
 
     2. **Upload Images**: Drag and drop images directly onto the upload area, or click to select files from your device.
 
-    3. **Generate Captions**: **If you upload any images**, Click the "Generate Captions with Molmo" button to generate descriptive captions for your uploaded images (depend on the task). You can enter a custom message in the "Custom Message for Molmo" textbox e.g., "caption in 30 words" instead of 50 words.
+    3. **Generate Captions**: **If you upload any images**, Click the "Generate Captions" button to generate descriptive captions for your uploaded images (depend on the task). You can enter a custom message in the "Custom Message for captioner" textbox e.g., "caption in 30 words" instead of 50 words.
 
     4. **Configure Generation Settings**: Expand the "Advanced Configuration" section to adjust parameters like the number of inference steps, guidance scale, image size, and more.
 
@@ -488,7 +488,7 @@ with gr.Blocks(title="OneDiffusion Demo") as demo:
             
             captioning_message = gr.Textbox(
                 lines=2,
-                value="Describe the contents of the photo in 50 words.",
+                value="Describe the contents of the photo in 60 words.",
                 label="Custom message for captioner"
             )