Update app.py

app.py

@@ -128,7 +128,6 @@ zoe.to(device)
 pipe.to(device)
 
 last_lora = ""
-last_merged = False
 last_fused = False
 js = '''
 var button = document.getElementById('button');
@@ -205,8 +204,20 @@ def merge_incompatible_lora(full_path_lora, lora_scale):
                 del weights_sd
                 del lora_model
 @spaces.GPU
-def generate_image(prompt, negative, face_emb, face_image, image_strength, images, guidance_scale, face_strength, depth_control_scale, last_lora, repo_name, loaded_state_dict, lora_scale, sdxl_loras, selected_state_index):
-    global last_fused
+def generate_image(prompt, negative, face_emb, face_image, face_kps, image_strength, images, guidance_scale, face_strength, depth_control_scale, repo_name, loaded_state_dict, lora_scale, sdxl_loras, selected_state_index):
+    global last_fused, last_lora
+    print("Last LoRA: ", last_lora)
+    print("Current LoRA: ", repo_name)
+    print("Last fused: ", last_fused)
+    #prepare face zoe
+    st = time.time()
+    with torch.no_grad():
+        image_zoe = zoe(face_image)
+    width, height = face_kps.size
+    images = [face_kps, image_zoe.resize((height, width))]
+    et = time.time()
+    elapsed_time = et - st
+        print('Zoe Depth calculations took: ', elapsed_time, 'seconds')
     if last_lora != repo_name:
         if(last_fused):
             st = time.time()
@@ -259,10 +270,10 @@ def generate_image(prompt, negative, face_emb, face_image, image_strength, image
         guidance_scale = guidance_scale,
         controlnet_conditioning_scale=[face_strength, depth_control_scale],
     ).images[0]
+    last_lora = repo_name
     return image
     
 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)):
-    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()
@@ -276,6 +287,7 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
     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)
@@ -286,15 +298,6 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
     print("Prompt:", prompt) 
     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):
@@ -315,12 +318,8 @@ 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
-    print("Last LoRA: ", last_lora)
-    print("Current LoRA: ", repo_name)
-    print("Last fused: ", last_fused)
     
-    image = generate_image(prompt, negative, face_emb, face_image, image_strength, images, guidance_scale, face_strength, depth_control_scale, last_lora, repo_name, loaded_state_dict, lora_scale, sdxl_loras, selected_state_index)
-    last_lora = repo_name
+    image = generate_image(prompt, negative, face_emb, face_image, face_kps, image_strength, images, 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)
 
 def shuffle_gallery(sdxl_loras):