Add 2GB gpu_mem_limit

ConsistentID/lib/pipeline_ConsistentID.py

@@ -72,11 +72,15 @@ class ConsistentIDPipeline(StableDiffusionPipeline):
         self.id_image_processor = CLIPImageProcessor()
         self.crop_size = 512
 
+        '''
         # face_app: FaceAnalysis object
         self.face_app = FaceAnalysis(name="buffalo_l", root='models/insightface', 
                                      providers=['CPUExecutionProvider'])
         # The original det_size=(640, 640) is too large and face_app often fails to detect faces.
         self.face_app.prepare(ctx_id=0, det_size=(256, 256))
+        '''
+        # Will be initialized within adaface.
+        self.face_app = None
 
         if not os.path.exists(consistentID_weight_path):
             ### Download pretrained models

adaface/face_id_to_ada_prompt.py

@@ -658,9 +658,10 @@ class Arc2Face_ID2AdaPrompt(FaceID2AdaPrompt):
         else:
             device_id = device.index
             self.face_app = FaceAnalysis(name='antelopev2', root='models/insightface', 
-                                        providers=['CUDAExecutionProvider'],
-                                        provider_options=[{"device_id": device_id, 
-                                                           "cudnn_conv_algo_search": "HEURISTIC"}])
+                                         providers=['CUDAExecutionProvider'],
+                                         provider_options=[{"device_id": device_id, 
+                                                            "cudnn_conv_algo_search": "HEURISTIC",
+                                                            "gpu_mem_limit": 2 * 1024**3}])
             self.face_app.prepare(ctx_id=device_id, det_size=(256, 256))
 
         self.device = device
@@ -804,9 +805,10 @@ class ConsistentID_ID2AdaPrompt(FaceID2AdaPrompt):
         else:
             device_id = device.index
             self.face_app = FaceAnalysis(name='buffalo_l', root='models/insightface', 
-                                        providers=['CUDAExecutionProvider'],
-                                        provider_options=[{"device_id": device_id, 
-                                                           "cudnn_conv_algo_search": "HEURISTIC"}])
+                                         providers=['CUDAExecutionProvider'],
+                                         provider_options=[{"device_id": device_id, 
+                                                            "cudnn_conv_algo_search": "HEURISTIC",
+                                                            "gpu_mem_limit": 2 * 1024**3}])
             self.face_app.prepare(ctx_id=device_id, det_size=(256, 256))
 
         self.device = device