Update app.py

app.py

@@ -214,13 +214,13 @@ def generate_image(prompt, negative, face_emb, face_image, image_strength, image
             pipe.unload_lora_weights()
             et = time.time()
             elapsed_time = et - st
-            print('Unfuse and unload took: ', elapsed_time, 'seconds')
+            print('Unfuse and unload LoRA took: ', elapsed_time, 'seconds')
         st = time.time()
         pipe.load_lora_weights(loaded_state_dict)
         pipe.fuse_lora(lora_scale)
         et = time.time()
         elapsed_time = et - st
-        print('Fuse and load took: ', elapsed_time, 'seconds')
+        print('Fuse and load LoRA took: ', elapsed_time, 'seconds')
         last_fused = True
         is_pivotal = sdxl_loras[selected_state_index]["is_pivotal"]
         if(is_pivotal):
@@ -265,6 +265,7 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
     global last_lora, last_merged, last_fused, pipe
     selected_state_index = selected_state.index
     face_image = center_crop_image_as_square(face_image)
+    st = time.time()
     try:
         face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))
         face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1] # only use the maximum face
@@ -272,7 +273,9 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
         face_kps = draw_kps(face_image, face_info['kps'])
     except: 
         raise gr.Error("No face found in your image. Only face images work here. Try again")
-        
+    et = time.time()
+    elapsed_time = et - st
+    print('Calculating face embeds took: ', elapsed_time, 'seconds')
     for lora_list in lora_defaults:
         if lora_list["model"] == sdxl_loras[selected_state_index]["repo"]:
             prompt_full = lora_list.get("prompt", None)
@@ -284,13 +287,15 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
     if(prompt == ""):
         prompt = "a person"
     #prepare face zoe
+    st = time.time()
     with torch.no_grad():
         image_zoe = zoe(face_image)
-    
+    et = time.time()
+    elapsed_time = et - st
+    print('Zoe Depth calculations took: ', elapsed_time, 'seconds')
     width, height = face_kps.size
     images = [face_kps, image_zoe.resize((height, width))]
     
-    
     #if(selected_state.index < 0):
     #    if(selected_state.index == -9999):
     #        selected_state.index = 0