diff --git a/face_analysis.json b/face_analysis.json
index b7b755e..47a7674 100644
--- a/face_analysis.json
+++ b/face_analysis.json
@@ -1,690 +1,26 @@
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@@ -694,7 +30,7 @@
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@@ -706,58 +42,54 @@
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@@ -774,255 +106,113 @@
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-      1,
-      "CONDITIONING"
-    ],
-    [
-      107,
-      5,
-      0,
-      1,
-      2,
-      "CONDITIONING"
-    ],
-    [
-      115,
-      10,
-      0,
-      47,
-      0,
-      "IMAGE"
-    ],
-    [
-      117,
-      47,
-      0,
-      45,
-      0,
-      "IMAGE"
-    ],
-    [
-      134,
-      10,
-      0,
-      33,
-      3,
-      "IMAGE"
-    ],
-    [
-      141,
-      2,
-      0,
-      14,
-      0,
-      "MODEL"
-    ],
-    [
-      144,
-      14,
-      0,
-      33,
-      4,
-      "MODEL"
-    ],
-    [
-      146,
-      2,
-      1,
-      5,
-      0,
-      "CLIP"
-    ],
-    [
-      148,
-      2,
-      1,
-      4,
-      0,
-      "CLIP"
-    ],
-    [
-      150,
-      45,
-      0,
-      37,
-      2,
-      "IMAGE"
-    ],
-    [
-      155,
-      59,
-      0,
-      61,
-      0,
-      "ANALYSIS_MODELS"
-    ],
     [
       157,
       61,
@@ -1040,8 +230,16 @@
       "IMAGE"
     ],
     [
-      159,
-      6,
+      160,
+      62,
+      0,
+      61,
+      0,
+      "ANALYSIS_MODELS"
+    ],
+    [
+      162,
+      63,
       0,
       61,
       2,
diff --git a/faceanalysis.py b/faceanalysis.py
index 5699d83..854766a 100644
--- a/faceanalysis.py
+++ b/faceanalysis.py
@@ -1,27 +1,45 @@
 import dlib
+from insightface.app import FaceAnalysis
 import torch
 import torchvision.transforms.v2 as T
 import os
+import folder_paths
 import numpy as np
 from PIL import Image, ImageDraw, ImageFont, ImageColor
 
 DLIB_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "dlib")
+INSIGHTFACE_DIR = os.path.join(folder_paths.models_dir, "insightface")
 
 class FaceAnalysisModels:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": {}}
+        return {"required": {
+            "library": (["dlib", "insightface"], ),
+            "provider": (["CPU", "CUDA", "DirectML", "OpenVINO", "ROCM", "CoreML"], ),
+        }}
 
     RETURN_TYPES = ("ANALYSIS_MODELS", )
     FUNCTION = "load_models"
     CATEGORY = "FaceAnalysis"
 
-    def load_models(self):
-        return ({
-            "detector": dlib.get_frontal_face_detector(),
-            "shape_predict": dlib.shape_predictor(os.path.join(DLIB_DIR, "shape_predictor_68_face_landmarks.dat")),
-            "face_recog": dlib.face_recognition_model_v1(os.path.join(DLIB_DIR, "dlib_face_recognition_resnet_model_v1.dat")),
-            }, )
+    def load_models(self, library, provider):
+        out = {}
+
+        if library == "insightface":
+            out = {
+                "library": library,
+                "detector": FaceAnalysis(name="buffalo_l", root=INSIGHTFACE_DIR, providers=[provider + 'ExecutionProvider',])
+            }
+            out["detector"].prepare(ctx_id=0, det_size=(640, 640))
+        else:
+            out = {
+                "library": library,
+                "detector": dlib.get_frontal_face_detector(),
+                "shape_predict": dlib.shape_predictor(os.path.join(DLIB_DIR, "shape_predictor_68_face_landmarks.dat")),
+                "face_recog": dlib.face_recognition_model_v1(os.path.join(DLIB_DIR, "dlib_face_recognition_resnet_model_v1.dat")),
+            }
+
+        return (out, )
 
 class FaceEmbedDistance:
     @classmethod
@@ -31,8 +49,8 @@ class FaceEmbedDistance:
                 "analysis_models": ("ANALYSIS_MODELS", ),
                 "reference": ("IMAGE", ),
                 "image": ("IMAGE", ),
-                "filter_thresh_eucl": ("FLOAT", { "default": 1.0, "min": 0.001, "max": 1.0, "step": 0.001 }),
-                "filter_thresh_cos": ("FLOAT", { "default": 1.0, "min": 0.001, "max": 1.0, "step": 0.001 }),
+                "filter_thresh_eucl": ("FLOAT", { "default": 1.0, "min": 0.001, "max": 2.0, "step": 0.001 }),
+                "filter_thresh_cos": ("FLOAT", { "default": 1.0, "min": 0.001, "max": 2.0, "step": 0.001 }),
                 "generate_image_overlay": ("BOOLEAN", { "default": True })
             },
         }
@@ -49,9 +67,7 @@ class FaceEmbedDistance:
             background_color = ImageColor.getrgb("#000000AA")
             txt_height = font.getmask("Q").getbbox()[3] + font.getmetrics()[1]
 
-        self.detector = analysis_models.get("detector")
-        self.shape_predict = analysis_models.get("shape_predict")
-        self.face_recog = analysis_models.get("face_recog")
+        self.analysis_models = analysis_models
 
         ref = np.array(T.ToPILImage()(reference[0].permute(2, 0, 1)).convert('RGB'))
         ref = self.get_descriptor(ref)
@@ -71,27 +87,21 @@ class FaceEmbedDistance:
                 eucl_dist = 1.0
                 cos_dist = 1.0
             else:
-                if ref == img: # Same face
+                if np.array_equal(ref, img): # Same face
                     eucl_dist = 0.0
                     cos_dist = 0.0
                 else:
-                    eucl_dist = np.linalg.norm(np.array(ref) - np.array(img))
+                    eucl_dist = np.float64(np.linalg.norm(ref - img))
                     cos_dist = 1 - np.dot(ref, img) / (np.linalg.norm(ref) * np.linalg.norm(img))
             
             if eucl_dist <= filter_thresh_eucl and cos_dist <= filter_thresh_cos:
-                out_eucl.append(eucl_dist)
-                out_cos.append(cos_dist)
-                
                 print(f"\033[96mFace Analysis: Euclidean: {eucl_dist}, Cosine: {cos_dist}\033[0m")
 
-                eucl_dist = round(eucl_dist, 3)
-                cos_dist = round(cos_dist, 3)
-
                 if generate_image_overlay:
                     tmp = T.ToPILImage()(i.permute(2, 0, 1)).convert('RGBA')
                     txt = Image.new('RGBA', (image.shape[2], txt_height), color=background_color)
                     draw = ImageDraw.Draw(txt)
-                    draw.text((0, 0), f"EUC: {eucl_dist} | COS-1: {cos_dist}", font=font, fill=(255, 255, 255, 255))
+                    draw.text((0, 0), f"EUC: {round(eucl_dist, 3)} | COS: {round(cos_dist, 3)}", font=font, fill=(255, 255, 255, 255))
                     composite = Image.new('RGBA', tmp.size)
                     composite.paste(txt, (0, tmp.height - txt.height))
                     composite = Image.alpha_composite(tmp, composite)
@@ -99,6 +109,9 @@ class FaceEmbedDistance:
                 else:
                     out.append(i)
 
+                out_eucl.append(eucl_dist)
+                out_cos.append(cos_dist)
+
         if not out:
             raise Exception('No image matches the filter criteria.')
 
@@ -115,12 +128,19 @@ class FaceEmbedDistance:
         return(img, out_eucl, out_cos, csv,)
     
     def get_descriptor(self, image):
-        faces = self.detector(image)
-        if len(faces) > 0:
-            shape = self.shape_predict(image, faces[0])
-            return self.face_recog.compute_face_descriptor(image, shape)
+        embeds = None
 
-        return None
+        if self.analysis_models["library"] == "insightface":
+            faces = self.analysis_models["detector"].get(image)
+            if len(faces) > 0:
+                embeds = faces[0].normed_embedding
+        else:
+            faces = self.analysis_models["detector"](image)
+            if len(faces) > 0:
+                shape = self.analysis_models["shape_predict"](image, faces[0])
+                embeds = np.array(self.analysis_models["face_recog"].compute_face_descriptor(image, shape))
+
+        return embeds
 
 NODE_CLASS_MAPPINGS = {
     "FaceEmbedDistance": FaceEmbedDistance,
diff --git a/requirements.txt b/requirements.txt
index a86b17e..cb54590 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1 +1,3 @@
 dlib
+onnxruntime
+insightface
\ No newline at end of file