Update app.py

further de-tune to get it within 30 seconds.

app.py

@@ -57,7 +57,7 @@ def generate_image(
     Args:
        prompt (str): Text description of the image to generate. Include: subject, style (e.g. photorealistic/anime/oil painting/watercolor/3D render), composition (e.g. close-up/wide shot/dynamic angle), lighting (e.g. soft/dramatic/backlit), colors (e.g. vibrant/muted/monochrome), mood (e.g. serene/dramatic/mysterious), setting. Specify text inquotes with position if needed. Keep under 200 words for best results.
        negative_prompt (str): Text description of traits to exclude e.g. text, watermark, copyright, blurry, low resolution
-       num_inference_steps (int): Use 18 or fewer steps to keep generation time under 30 seconds. Only specify if instructed by the User.
+       num_inference_steps (int): Use 16 or fewer steps to keep generation time under 30 seconds. Only specify if instructed by the User.
     """
     # Hardcode the negative prompt as requested
 #    negative_prompt = "text, watermark, copyright, blurry, low resolution"
@@ -112,7 +112,7 @@ css = """
 with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
         gr.Markdown('<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/qwen_image_logo.png" alt="Qwen-Image Logo" width="400" style="display: block; margin: 0 auto;">')
-        gr.Markdown("[Learn more](https://github.com/QwenLM/Qwen-Image) about the Qwen-Image series. Try on [Qwen Chat](https://chat.qwen.ai/), or [download model](https://huggingface.co/Qwen/Qwen-Image) to run locally with ComfyUI or diffusers. <br /> **This version does not include the 'prompt polisher', and defaults to 18 inference steps to target ~30s generation times**.")
+        gr.Markdown("[Learn more](https://github.com/QwenLM/Qwen-Image) about the Qwen-Image series. Try on [Qwen Chat](https://chat.qwen.ai/), or [download model](https://huggingface.co/Qwen/Qwen-Image) to run locally with ComfyUI or diffusers. <br /> **This version does not include the 'prompt polisher', and defaults to 16 inference steps to target ~30s generation times**.")
         with gr.Row():
             prompt = gr.Text(
                 label="Prompt",
@@ -163,7 +163,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=1,
                     maximum=50,
                     step=1,
-                    value=18,
+                    value=16,
                 )
 
         gr.Examples(
@@ -177,7 +177,7 @@ with gr.Blocks(css=css) as demo:
                 aspect_ratio="16:9",
                 negative_prompt="text, watermark, copyright, blurry, low resolution",
                 guidance_scale=4.0,
-                num_inference_steps=18
+                num_inference_steps=16
             ), 
             cache_examples=False
         )