Update app.py

app.py

@@ -191,7 +191,12 @@ def gradio_process_image(input_image, resolution, num_inference_steps, strength,
     print("Running inference...")
     result = lazy_pipe(**options).images[0]
     print("Image processing completed successfully")
-    return result
+    
+    # Convert input_image and result to numpy arrays
+    input_array = np.array(input_image)
+    result_array = np.array(result)
+    
+    return [input_array, result_array]
 
 title = """<h1 align="center">Image Upscaler with Tile Controlnet</h1>
 <p align="center">The main ideas come from</p>
@@ -208,7 +213,7 @@ with gr.Blocks() as demo:
             input_image = gr.Image(type="pil", label="Input Image")
             run_button = gr.Button("Enhance Image")
         with gr.Column():
-            output_image = gr.Image(type="pil", label="Enhanced Image")
+            output_slider = ImageSlider(label="Before / After", type="numpy")
     with gr.Accordion("Advanced Options", open=False):
         resolution = gr.Slider(minimum=256, maximum=2048, value=512, step=256, label="Resolution")
         num_inference_steps = gr.Slider(minimum=1, maximum=50, value=20, step=1, label="Number of Inference Steps")
@@ -216,6 +221,10 @@ with gr.Blocks() as demo:
         hdr = gr.Slider(minimum=0, maximum=1, value=0, step=0.1, label="HDR Effect")
         guidance_scale = gr.Slider(minimum=0, maximum=20, value=3, step=0.5, label="Guidance Scale")
 
+    run_button.click(fn=gradio_process_image, 
+                     inputs=[input_image, resolution, num_inference_steps, strength, hdr, guidance_scale],
+                     outputs=output_slider)
+
     # Add examples with all required inputs
     gr.Examples(
         examples=[
@@ -224,13 +233,9 @@ with gr.Blocks() as demo:
             ["image3.png", 512, 20, 0.4, 0, 3],
         ],
         inputs=[input_image, resolution, num_inference_steps, strength, hdr, guidance_scale],
-        outputs=[output_image],
+        outputs=output_slider,
         fn=gradio_process_image,
         cache_examples=True,
     )
 
-    run_button.click(fn=gradio_process_image, 
-                     inputs=[input_image, resolution, num_inference_steps, strength, hdr, guidance_scale],
-                     outputs=output_image)
-
 demo.launch(share=True)