gradio MCP mode readiness

app.py

@@ -169,9 +169,55 @@ def preview_image_and_mask(image, width, height, overlap_percentage, resize_opti
     return preview
 
 @spaces.GPU(duration=24)
-def infer(image, width, height, overlap_percentage, num_inference_steps, resize_option, custom_resize_percentage, prompt_input, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom):
-    background, mask = prepare_image_and_mask(image, width, height, overlap_percentage, resize_option, custom_resize_percentage, alignment, overlap_left, overlap_right, overlap_top, overlap_bottom)
+def infer(
+    image, 
+    width, 
+    height, 
+    overlap_percentage, 
+    num_inference_steps, 
+    resize_option, 
+    custom_resize_percentage, 
+    prompt_input, 
+    alignment, 
+    overlap_left, 
+    overlap_right, 
+    overlap_top, 
+    overlap_bottom
+):
+    """
+    Generate an outpainted image using Stable Diffusion XL with ControlNet guidance.
     
+    This function performs intelligent image outpainting by expanding the input image 
+    according to the specified target dimensions and alignment, generating new content 
+    guided by a textual prompt. It uses a ControlNet-enabled diffusion pipeline to ensure 
+    coherent image extension.
+
+    Args:
+        image (PIL.Image): The input image to be outpainted.
+        width (int): The target width of the output image.
+        height (int): The target height of the output image.
+        overlap_percentage (int): Percentage of overlap between original and outpainted regions for seamless blending.
+        num_inference_steps (int): Number of inference steps for image generation. Higher values yield better results.
+        resize_option (str): Predefined or custom percentage to resize the input image ("Full", "50%", "33%", "25%", or "Custom").
+        custom_resize_percentage (int): Custom resize percentage if resize_option is "Custom".
+        prompt_input (str): A text prompt describing desired content for the generated region.
+        alignment (str): Alignment of the original image within the canvas ("Middle", "Left", "Right", "Top", "Bottom").
+        overlap_left (bool): Whether to allow blending on the left edge.
+        overlap_right (bool): Whether to allow blending on the right edge.
+        overlap_top (bool): Whether to allow blending on the top edge.
+        overlap_bottom (bool): Whether to allow blending on the bottom edge.
+
+    Yields:
+        Tuple[PIL.Image, PIL.Image]: 
+        - The intermediate ControlNet input image (showing the masked area).
+        - The final generated image with the inpainted region.
+    """
+    background, mask = prepare_image_and_mask(
+        image, width, height, overlap_percentage, 
+        resize_option, custom_resize_percentage, alignment,
+        overlap_left, overlap_right, overlap_top, overlap_bottom
+    )
+
     if not can_expand(background.width, background.height, width, height, alignment):
         alignment = "Middle"
 
@@ -202,6 +248,7 @@ def infer(image, width, height, overlap_percentage, num_inference_steps, resize_
 
     yield background, cnet_image
 
+
 def clear_result():
     """Clears the result ImageSlider."""
     return gr.update(value=None)
@@ -453,4 +500,4 @@ with gr.Blocks(css=css) as demo:
         queue=False
     )
 
-demo.queue(max_size=12).launch(share=False, show_error=True)
+demo.queue(max_size=12).launch(share=False, show_error=True, mcp_server=True)