added zero decorators

app.py

@@ -9,6 +9,7 @@ import os
 import sys
 import json # Added import
 import copy
+import spaces
 # Add project root to sys.path to allow direct import of var_post_samp
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "."))
 if project_root not in sys.path:
@@ -92,6 +93,7 @@ def save_configuration(image_editor_data, image_input, prompt, seed_val, task, r
     except Exception as e:
         return gr.Markdown(f"""<p style='color:red;'>Error saving configuration: {str(e)}</p>""")
 
+@spaces.GPU
 def embed_prompt(prompt, device):
     print(f"Generating prompt embeddings for: {prompt}")
     with torch.no_grad(): # Add torch.no_grad() here
@@ -264,6 +266,7 @@ def postprocess_image(image_tensor):
     pil_image = TF.to_pil_image(image_tensor)
     return pil_image
 
+@spaces.GPU
 def inpaint_image(image_editor_output, prompt_text, fixed_seed_value, use_random_seed, guidance_scale, num_steps): # MODIFIED: seed_input changed to fixed_seed_value, use_random_seed
     try:
         if image_editor_output is None:
@@ -364,6 +367,7 @@ def inpaint_image(image_editor_output, prompt_text, fixed_seed_value, use_random
         # Return a more user-friendly error message to Gradio
         raise gr.Error(f"An error occurred: {str(e)}. Check console for details.")
 
+@spaces.GPU
 def super_resolution_image(lr_image, prompt_text, fixed_seed_value, use_random_seed, guidance_scale, num_steps, sr_scale_factor, downscale_input):
     try:
         if lr_image is None:
@@ -728,55 +732,20 @@ 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
+                cache_examples=False
             )
         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
+                cache_examples=False
             )