update

app.py

@@ -44,10 +44,12 @@ pipe = StableDiffusionXLPipeline(
 )
 
 class FaceInfoGenerator():
+    @spaces.GPU
     def __init__(self, root_dir = "./.insightface/"):
         self.app = FaceAnalysis(name = 'antelopev2', root = root_dir, providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
         self.app.prepare(ctx_id = 0, det_size = (640, 640))
 
+    @spaces.GPU
     def get_faceinfo_one_img(self, face_image):
         face_info = self.app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))
 
@@ -57,6 +59,7 @@ class FaceInfoGenerator():
             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
         return face_info
 
+@spaces.GPU
 def face_bbox_to_square(bbox):
     ## l, t, r, b to square l, t, r, b
     l,t,r,b = bbox
@@ -74,6 +77,7 @@ def face_bbox_to_square(bbox):
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
+face_info_generator = FaceInfoGenerator()
 
 @spaces.GPU
 def infer(prompt, 
@@ -93,7 +97,6 @@ def infer(prompt,
     scale = 0.8
     pipe.set_face_fidelity_scale(scale)   
 
-    face_info_generator = FaceInfoGenerator()
     face_info = face_info_generator.get_faceinfo_one_img(image)
     face_bbox_square = face_bbox_to_square(face_info["bbox"])
     crop_image = image.crop(face_bbox_square)