rollback

app.py

@@ -67,17 +67,18 @@ def random_positioning(input, output_size=(1024, 1024)):
     
     scale_factor = random.uniform(0.5, 1.0)
     
-    new_size = (int(input_image.shape[1] * scale_factor), int(input_image.shape[0] * scale_factor))
+    new_size = (int(input.shape[1] * scale_factor), int(input.shape[0] * scale_factor))
     
-    resized_image = cv2.resize(input_image, new_size, interpolation=cv2.INTER_AREA)
+    resized_image = cv2.resize(input, new_size, interpolation=cv2.INTER_AREA)
     
-    background = np.ones((output_size[1], output_size[0], 3), dtype=np.uint8) * 255
+    background = np.zeros((output_size[1], output_size[0], 3), dtype=np.uint8)
     
-    x_offset = (output_size[0] - new_size[0]) // 2
-    y_offset = (output_size[1] - new_size[1]) // 2
+    x_offset = random.randint(0, output_size[0] - new_size[0])
+    y_offset = random.randint(0, output_size[1] - new_size[1])
     
     background[y_offset:y_offset+new_size[1], x_offset:x_offset+new_size[0]] = resized_image
-    
+    background = np.clip(background, 0, 255) 
+    background = background.astype(np.uint8)
 
     return background
 
@@ -111,10 +112,7 @@ def execute(image, prompt):
   img = cv2.imread(image)
   img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
 
-  imgs = [ random_positioning(img), random_positioning(img)]
-
-  # Created by Fountai 
-  # https://x.com/EuFountai
+  imgs = [ random_positioning(img)]
 
   pipe.load_lora_weights("XLabs-AI/flux-RealismLora", weight_name='lora.safetensors')
   response = []
@@ -149,6 +147,8 @@ def execute(image, prompt):
 
   return response
 
+# Created by Fountai
+# https://x.com/EuFountai
 description = "This is an unofficial implementation of the ip face adapter for FLUX DEV and does not explicitly follow the ip face model, I created a wrapper with inpaint and mediapipe, I like using Fake IP Adapter"
 title = "Flux IP Face Adapter"
 iface = gr.Interface(
@@ -164,3 +164,5 @@ iface = gr.Interface(
 
 iface.launch(share=True, debug=True)
 
+iface.launch(share=True, debug=True)
+