cache examples

app.py

@@ -81,12 +81,6 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         if device == 'cuda':
             pipe_inference.to('cpu')
             torch.cuda.empty_cache()
-            # pipe_inversion = pipe_inversion.to(device)
-            # if image_editor is not None:
-            #     image_editor = image_editor.to('cpu')
-
-        print(f"#### 1 #### pipe_inversion.device: {pipe_inversion.device}")
-        print(f"#### 2 #### pipe_inference.device: {pipe_inference.device}")
 
         inversion_state = ImageEditorDemo.invert(pipe_inversion.to(device), input_image, description_prompt, config,
                                                  [rnri_iterations, rnri_alpha, rnri_lr], device)
@@ -94,9 +88,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             pipe_inversion.to('cpu')
             torch.cuda.empty_cache()
             pipe_inference.to(device)
-            # pipe_inversion = pipe_inversion.to('cpu')
-        print(f"#### 3 #### pipe_inversion.device: {pipe_inversion.device}")
-        print(f"#### 4 #### pipe_inference.device: {pipe_inference.device}")
+
         gr.Info('Input has set!')
         return inversion_state, "Input has set!"
 
@@ -105,14 +97,9 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
         if inversion_state is None:
             raise gr.Error("Set inputs before editing. Progress indication below")
 
-        print(f"#### 5 #### pipe_inversion.device: {pipe_inversion.device}")
-        print(f"#### 6 #### pipe_inference.device: {pipe_inference.device}")
-
         image = ImageEditorDemo.edit(pipe_inference, target_prompt, inversion_state['latent'], inversion_state['noise'],
                                      inversion_state['cfg'], inversion_state['cfg'].edit_guidance_scale)
 
-        print(f"#### 7 #### pipe_inversion.device: {pipe_inversion.device}")
-        print(f"#### 8 #### pipe_inference.device: {pipe_inference.device}")
         return image
 
 
@@ -202,6 +189,7 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
                     examples='examples',
                     inputs=[input_image, description_prompt, target_prompt, edit_guidance_scale, num_inference_steps,
                             inversion_max_step, rnri_iterations, rnri_alpha, rnri_lr],
+                    cache_examples=True,
                 )
 
     gr.Markdown(f"""Disclaimer: Performance may be inferior to the reported in the paper due to hardware limitation.