experimental gpu option

scripts/faceswaplab_settings/faceswaplab_settings.py

@@ -37,11 +37,22 @@ def on_ui_settings() -> None:
         ),
     )
 
+    shared.opts.add_option(
+        "faceswaplab_det_size",
+        shared.OptionInfo(
+            640,
+            "det_size : Size of the detection area for face analysis. Higher values may improve quality but reduce speed. Low value may improve detection of very large face.",
+            gr.Slider,
+            {"minimum": 320, "maximum": 640, "step": 320},
+            section=section,
+        ),
+    )
+
     shared.opts.add_option(
         "faceswaplab_detection_threshold",
         shared.OptionInfo(
             0.5,
-            "Face Detection threshold",
+            "det_thresh : Face Detection threshold",
             gr.Slider,
             {"minimum": 0.1, "maximum": 0.99, "step": 0.001},
             section=section,

scripts/faceswaplab_swapping/swapper.py

@@ -37,8 +37,19 @@ from scripts.faceswaplab_postprocessing.postprocessing_options import (
 from scripts.faceswaplab_utils.models_utils import get_current_model
 from scripts.faceswaplab_utils.typing import CV2ImgU8, PILImage, Face
 from scripts.faceswaplab_inpainting.i2i_pp import img2img_diffusion
+from modules import shared
 
-providers = ["CPUExecutionProvider"]
+
+use_gpu = getattr(shared.cmd_opts, "faceswaplab_gpu", False)
+
+if use_gpu and sys.platform != "darwin":
+    providers = [
+        "TensorrtExecutionProvider",
+        "CUDAExecutionProvider",
+        "CPUExecutionProvider",
+    ]
+else:
+    providers = ["CPUExecutionProvider"]
 
 
 def cosine_similarity_face(face1: Face, face2: Face) -> float:
@@ -258,7 +269,9 @@ def capture_stdout() -> Generator[StringIO, None, None]:
 
 
 @lru_cache(maxsize=1)
-def getAnalysisModel() -> insightface.app.FaceAnalysis:
+def getAnalysisModel(
+    det_size: Tuple[int, int] = (640, 640), det_thresh: float = 0.5
+) -> insightface.app.FaceAnalysis:
     """
     Retrieves the analysis model for face analysis.
 
@@ -269,7 +282,9 @@ def getAnalysisModel() -> insightface.app.FaceAnalysis:
         if not os.path.exists(faceswaplab_globals.ANALYZER_DIR):
             os.makedirs(faceswaplab_globals.ANALYZER_DIR)
 
-        logger.info("Load analysis model, will take some time. (> 30s)")
+        logger.info(
+            f"Load analysis model det_size={det_size}, det_thresh={det_thresh}, will take some time. (> 30s)"
+        )
         # Initialize the analysis model with the specified name and providers
 
         with tqdm(
@@ -281,6 +296,9 @@ def getAnalysisModel() -> insightface.app.FaceAnalysis:
                     providers=providers,
                     root=faceswaplab_globals.ANALYZER_DIR,
                 )
+
+                # Prepare the analysis model for face detection with the specified detection size
+                model.prepare(ctx_id=0, det_thresh=det_thresh, det_size=det_size)
             pbar.update(1)
         logger.info("%s", pformat(captured.getvalue()))
 
@@ -350,7 +368,6 @@ def getFaceSwapModel(model_path: str) -> upscaled_inswapper.UpscaledINSwapper:
 
 def get_faces(
     img_data: CV2ImgU8,
-    det_size: Tuple[int, int] = (640, 640),
     det_thresh: Optional[float] = None,
 ) -> List[Face]:
     """
@@ -368,21 +385,12 @@ def get_faces(
     if det_thresh is None:
         det_thresh = opts.data.get("faceswaplab_detection_threshold", 0.5)
 
-    # Create a deep copy of the analysis model (otherwise det_size is attached to the analysis model and can't be changed)
-    face_analyser = copy.deepcopy(getAnalysisModel())
-
-    # Prepare the analysis model for face detection with the specified detection size
-    face_analyser.prepare(ctx_id=0, det_thresh=det_thresh, det_size=det_size)
+    det_size = opts.data.get("faceswaplab_det_size", 640)
+    face_analyser = getAnalysisModel((det_size, det_size), det_thresh)
 
     # Get the detected faces from the image using the analysis model
     face = face_analyser.get(img_data)
 
-    # If no faces are detected and the detection size is larger than 320x320,
-    # recursively call the function with a smaller detection size
-    if len(face) == 0 and det_size[0] > 320 and det_size[1] > 320:
-        det_size_half = (det_size[0] // 2, det_size[1] // 2)
-        return get_faces(img_data, det_size=det_size_half, det_thresh=det_thresh)
-
     try:
         # Sort the detected faces based on their x-coordinate of the bounding box
         return sorted(face, key=lambda x: x.bbox[0])

scripts/faceswaplab_swapping/upscaled_inswapper.py

@@ -262,7 +262,6 @@ class UpscaledINSwapper(INSwapper):
                 )
                 img_white[img_white > 20] = 255
                 fthresh = 10
-                print("fthresh", fthresh)
                 fake_diff[fake_diff < fthresh] = 0
                 fake_diff[fake_diff >= fthresh] = 255
                 img_mask = img_white