rm debugging code

app.py

@@ -435,10 +435,6 @@ def super_resolution_image(lr_image, prompt_text, fixed_seed_value, use_random_s
             # add some noise to the input image
             noise_std = current_sr_config.get("degradation", {}).get("kwargs", {}).get("noise_std", 0.0)
             y_tensor += torch.randn_like(y_tensor) * noise_std
-            # save for debugging purposes
-            # first convert to PIL
-            pil_y = postprocess_image(y_tensor)# Remove batch dimension and convert to PIL
-            pil_y.save("debug_input_image.png")  # Save the input image for debugging
                 
         
         print("Running SR inference...")
@@ -455,8 +451,6 @@ def super_resolution_image(lr_image, prompt_text, fixed_seed_value, use_random_s
         upscaled_input = POSTERIOR_MODEL.forward_operator.nn(upscaled_input)  # Use nearest neighbor upscaling
         upscaled_input = postprocess_image(upscaled_input)
         # save for debugging purposes
-        upscaled_input.save("debug_upscaled_input.png")  # Save the upscaled input image for debugging
-        # upscaled_input = upscaled_input.resize((hr_resolution, hr_resolution), resample=Image.NEAREST)
         return (upscaled_input, output_pil), current_sr_config["seed"]
 
     except gr.Error as e:
@@ -734,55 +728,57 @@ Use the slider to compare the low resolution input image with the super-resolved
 
         gr.Markdown("---") # Separator
         gr.Markdown("### Click an example to load:")
+        def load_inp_example(input_data):
+            for ex in example_list_inp:
+                if ex[0]["background"] == input_data:
+                    prompt_value, task, num_steps = ex[1], ex[2], ex[3]
+            # Load inpainting example into ImageEditor
+            image_editor.clear()
+            if input_data and input_data.get("background"):  
+                image_editor.upload_image(input_data["background"])
+            if input_data and input_data.get("layers"):
+                for layer in input_data["layers"]:
+                    image_editor.upload_mask(layer)
+            # Update other UI controls
+            prompt_text.value = prompt_value
+            task_selector.value = task
+            num_steps_slider.value = num_steps
+            seed_slider.value = random.randint(0, 2**32 - 1)
+            guidance_scale_slider.value = default_guidance_scale
+            update_visibility(task)
+
+        def load_sr_example(input_data):
+            for ex in example_list_sr:
+                if ex[0] == input_data:
+                    prompt_value, task, num_steps = ex[1], ex[2], ex[3]
+            # Load super-resolution example into Image component
+            image_input.clear()
+            image_input.upload_image(input_data)
+            # Update other UI controls
+            prompt_text.value = prompt_value
+            task_selector.value = task
+            num_steps_slider.value = num_steps
+            seed_slider.value = random.randint(0, 2**32 - 1)
+            guidance_scale_slider.value = default_guidance_scale
+            update_visibility(task)
+        # Examples for both tasks, loading via load_example (mutates components directly)
+        with gr.Row():
+            gr.Examples(
+                examples=example_list_sr,
+                inputs=[image_input, prompt_text, task_selector, num_steps_slider],
+                fn=load_sr_example,
+                label="Super Resolution Examples",
+                cache_examples=True
+            )
+        with gr.Row():
+            gr.Examples(
+                examples=example_list_inp,
+                inputs=[image_editor, prompt_text, task_selector, num_steps_slider],
+                fn=load_inp_example,
+                label="Inpainting Examples",
+                cache_examples=True
+            )
 
-        # --- GALLERY FOR SUPER RESOLUTION EXAMPLES ---
-        sr_gallery_items = [[ex[0], f"Prompt: {ex[1]} Steps: {ex[3]}"] for ex in example_list_sr]
-        sr_gallery = gr.Gallery(
-            value=sr_gallery_items,
-            label="Super Resolution Examples",
-            columns=4,
-            height="auto",
-            visible=True
-        )
-
-        # --- GALLERY FOR INPAINTING EXAMPLES ---
-        inp_gallery_items = [[ex[0]["background"], f"Prompt: {ex[1]} Steps: {ex[3]}"] for ex in example_list_inp]
-        inp_gallery = gr.Gallery(
-            value=inp_gallery_items,
-            label="Inpainting Examples",
-            columns=4,
-            height="auto",
-            visible=True
-        )
-
-        def on_sr_gallery_select(evt: gr.SelectData):
-            idx = evt.index
-            ex = example_list_sr[idx]
-            image_input.value = ex[0]
-            prompt_text.value = ex[1]
-            task_selector.value = ex[2]
-            num_steps_slider.value = ex[3]
-            update_visibility(ex[2])
-            return [image_input, prompt_text, task_selector, num_steps_slider]
-
-        def on_inp_gallery_select(evt: gr.SelectData):
-            idx = evt.index
-            ex = example_list_inp[idx]
-            image_editor.value = ex[0]
-            prompt_text.value = ex[1]
-            task_selector.value = ex[2]
-            num_steps_slider.value = ex[3]
-            update_visibility(ex[2])
-            return [image_editor, prompt_text, task_selector, num_steps_slider]
-
-        sr_gallery.select(
-            fn=on_sr_gallery_select,
-            outputs=[image_input, prompt_text, task_selector, num_steps_slider]
-        )
-        inp_gallery.select(
-            fn=on_inp_gallery_select,
-            outputs=[image_editor, prompt_text, task_selector, num_steps_slider]
-        )
 
     # --- End of Gradio UI definition ---