Update app.py

app.py

@@ -165,8 +165,8 @@ def run_for_examples(text, img1, img2, img3, height, width, guidance_scale, infe
 description = """
 OmniGen is a unified image generation model that you can use to perform various tasks, including but not limited to text-to-image generation, subject-driven generation, Identity-Preserving Generation, and image-conditioned generation.
 
-For multi-modal to image generation, you should pass a string as `prompt`, and a list of image paths as `input_images`. The placeholder in the prompt should be in the format of `<img><|image_*|></img>`.
-For example, use a image of a woman to generate a new image:
+For multi-modal to image generation, you should pass a string as `prompt`, and a list of image paths as `input_images`. The placeholder in the prompt should be in the format of `<img><|image_*|></img>` (for the first image, the placeholder is <img><|image_1|></img>. for the second image, the the placeholder is <img><|image_2|></img>).
+For example, use an image of a woman to generate a new image:
 prompt = "A woman holds a bouquet of flowers and faces the camera. Thw woman is \<img\>\<|image_1|\>\</img\>."
 """
 
@@ -178,7 +178,7 @@ with gr.Blocks() as demo:
         with gr.Column():
             # 文本输入框
             prompt_input = gr.Textbox(
-                label="Enter your prompt, use <img><|image_i|></img> tokens for images", placeholder="Type your prompt here..."
+                label="Enter your prompt, use <img><|image_i|></img> to represent i-th input image", placeholder="Type your prompt here..."
             )
 
             with gr.Row(equal_height=True):