Update app.py

app.py

@@ -249,11 +249,15 @@ def generate_image(prompt, negative, face_emb, face_image, face_kps, image_stren
                 pipe.load_textual_inversion(state_dict_embedding["text_encoders_1"], token=["<s0>", "<s1>"], text_encoder=pipe.text_encoder_2, tokenizer=pipe.tokenizer_2)
 
     print("Processing prompt...")
+    st = time.time()
     conditioning, pooled = compel(prompt)
     if(negative):
         negative_conditioning, negative_pooled = compel(negative)
     else:
         negative_conditioning, negative_pooled = None, None
+    et = time.time()
+    elapsed_time = et - st
+    print('Prompt processing took: ', elapsed_time, 'seconds')
     print("Processing image...")
     image = pipe(
         prompt_embeds=conditioning,
@@ -275,8 +279,8 @@ def generate_image(prompt, negative, face_emb, face_image, face_kps, image_stren
     
 def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, sdxl_loras, progress=gr.Progress(track_tqdm=True)):
     selected_state_index = selected_state.index
-    face_image = center_crop_image_as_square(face_image)
     st = time.time()
+    face_image = center_crop_image_as_square(face_image)
     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
@@ -286,8 +290,9 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
         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')
+    print('Cropping and calculating face embeds took: ', elapsed_time, 'seconds')
 
+    st = time.time()
     for lora_list in lora_defaults:
         if lora_list["model"] == sdxl_loras[selected_state_index]["repo"]:
             prompt_full = lora_list.get("prompt", None)
@@ -299,12 +304,6 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
     if(prompt == ""):
         prompt = "a person"
     
-    #if(selected_state.index < 0):
-    #    if(selected_state.index == -9999):
-    #        selected_state.index = 0
-    #    else:
-    #         selected_state.index *= -1
-        #sdxl_loras = sdxl_loras_new
     print("Selected State: ", selected_state_index)
     print(sdxl_loras[selected_state_index]["repo"])
     if negative == "":
@@ -318,6 +317,9 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
     full_path_lora = state_dicts[repo_name]["saved_name"]
     loaded_state_dict = copy.deepcopy(state_dicts[repo_name]["state_dict"])
     cross_attention_kwargs = None
+    et = time.time()
+    elapsed_time = et - st
+    print('Small content processing took: ', elapsed_time, 'seconds')
     
     image = generate_image(prompt, negative, face_emb, face_image, face_kps, image_strength, guidance_scale, face_strength, depth_control_scale, repo_name, loaded_state_dict, lora_scale, sdxl_loras, selected_state_index)
     return image, gr.update(visible=True)