Update app.py

app.py

@@ -181,9 +181,9 @@ def update_selection(selected_state: gr.SelectData, sdxl_loras, face_strength, i
 
     for lora_list in lora_defaults:
         if lora_list["model"] == sdxl_loras[selected_state.index]["repo"]:
-            face_strength = lora_list.get("face_strength", 0.85)
-            image_strength = lora_list.get("image_strength", 0.15)
-            weight = lora_list.get("weight", 0.9)
+            face_strength = lora_list.get("face_strength", 0.95)
+            image_strength = lora_list.get("image_strength", 0.2)
+            weight = lora_list.get("weight", 0.95)
             depth_control_scale = lora_list.get("depth_control_scale", 0.8)
             negative = lora_list.get("negative", "")
     
@@ -229,6 +229,7 @@ def merge_incompatible_lora(full_path_lora, lora_scale):
                 )
                 del weights_sd
                 del lora_model
+
 def resize_image_aspect_ratio(img, max_dim=512):
     width, height = img.size
     aspect_ratio = width / height
@@ -248,22 +249,28 @@ def resize_image_aspect_ratio(img, max_dim=512):
 
 
 def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, sdxl_loras, custom_lora, progress=gr.Progress(track_tqdm=True)):
-    print("Custom LoRA: ", custom_lora)
+    print("Custom LoRA:", custom_lora)
     custom_lora_path = custom_lora[0] if custom_lora else None
     selected_state_index = selected_state.index if selected_state else -1
 
-    original_width, original_height = face_image.size
+    st = time.time()
+    face_image = resize_image_aspect_ratio(face_image)
 
     face_detected = True
     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]
+        face_info = sorted(face_info, key=lambda x: (x['bbox'][2]-x['bbox'][0]) * x['bbox'][3]-x['bbox'][1])[-1]
         face_emb = face_info['embedding']
         face_kps = draw_kps(face_image, face_info['kps'])
     except:
         face_detected = False
         face_emb = None
-        face_kps = face_image
+        face_kps = face_image  # Optional face handling, verify intentional
+
+    et = time.time()
+    print('Cropping and calculating face embeds took:', et - st, 'seconds')
+
+    st = time.time()
 
     if custom_lora_path and custom_lora[1]:
         prompt = f"{prompt} {custom_lora[1]}"
@@ -274,11 +281,14 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
                 if prompt_full:
                     prompt = prompt_full.replace("<subject>", prompt)
 
+    print("Prompt:", prompt)
     if prompt == "":
         prompt = "a person"
+    print(f"Executing prompt: {prompt}")
 
     if negative == "":
         negative = None
+    print("Custom Loaded LoRA:", custom_lora_path)
 
     if not selected_state and not custom_lora_path:
         raise gr.Error("You must select a style")
@@ -289,17 +299,24 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
         repo_name = sdxl_loras[selected_state_index]["repo"]
         full_path_lora = state_dicts[repo_name]["saved_name"]
 
-    resized_image = resize_with_aspect_ratio(face_image)
+    print("Full path LoRA", full_path_lora)
 
+    et = time.time()
+    print('Small content processing took:', et - st, 'seconds')
+
+    st = time.time()
     image = generate_image(
-        prompt, negative, face_emb, resized_image, face_kps, image_strength, guidance_scale, face_strength, depth_control_scale,
-        repo_name, full_path_lora, lora_scale, sdxl_loras, selected_state_index, face_detected
+        prompt, negative, face_emb, face_image, face_kps, image_strength,
+        guidance_scale, face_strength, depth_control_scale, repo_name,
+        full_path_lora, lora_scale, sdxl_loras, selected_state_index, face_detected, st
     )
 
-    return (resized_image, image), gr.update(visible=True)
+    return (face_image, image), gr.update(visible=True)
+
+run_lora.zerogpu = True
 
 @spaces.GPU(duration=100)
-def 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, face_detected):
+def 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, face_detected, st):
     global last_fused, last_lora
 
     print("Loaded state dict:", loaded_state_dict)