diff --git a/onnxruntime/python/tools/transformers/models/stable_diffusion/test/check_image.py b/onnxruntime/python/tools/transformers/models/stable_diffusion/test/check_image.py
index 70657deeb462b..fcfe8b081fb0a 100644
--- a/onnxruntime/python/tools/transformers/models/stable_diffusion/test/check_image.py
+++ b/onnxruntime/python/tools/transformers/models/stable_diffusion/test/check_image.py
@@ -1,5 +1,5 @@
 import argparse
-from time import sleep
+import os
 
 import cv2
 import open_clip
@@ -15,36 +15,54 @@ def arg_parser():
     args = parser.parse_args()
     return args
 
-def imageEncoder(img):
+
+def image_encoder(img: Image.Image):  # -> torch.Tensor:
     device = "cuda" if torch.cuda.is_available() else "cpu"
-    model, _, preprocess = open_clip.create_model_and_transforms('ViT-B-16-plus-240', pretrained="laion400m_e32")
+    model, _, preprocess = open_clip.create_model_and_transforms("ViT-B-16-plus-240", pretrained="laion400m_e32")
     model.to(device)
 
-    img1 = Image.fromarray(img).convert('RGB')
+    img1 = Image.fromarray(img).convert("RGB")
     img1 = preprocess(img1).unsqueeze(0).to(device)
     img1 = model.encode_image(img1)
     return img1
 
-def generateScore(image1, image2):
-    test_img = cv2.imread(image1, cv2.IMREAD_UNCHANGED)
-    data_img = cv2.imread(image2, cv2.IMREAD_UNCHANGED)
-    img1 = imageEncoder(test_img)
-    img2 = imageEncoder(data_img)
+
+def load_image(image_path: str):  # -> Image.Image:
+    # cv2.imread() can silently fail when the path is too long
+    # https://stackoverflow.com/questions/68716321/how-to-use-absolute-path-in-cv2-imread
+    if os.path.isabs(image_path):
+        directory = os.path.dirname(image_path)
+        current_directory = os.getcwd()
+        os.chdir(directory)
+        img = cv2.imread(os.path.basename(image_path), cv2.IMREAD_UNCHANGED)
+        os.chdir(current_directory)
+    else:
+        img = cv2.imread(image_path, cv2.IMREAD_UNCHANGED)
+    return img
+
+
+def generate_score(image1: str, image2: str):  # -> float:
+    test_img = load_image(image1)
+    data_img = load_image(image2)
+    img1 = image_encoder(test_img)
+    img2 = image_encoder(data_img)
     cos_scores = util.pytorch_cos_sim(img1, img2)
-    score = round(float(cos_scores[0][0])*100, 2)
+    score = round(float(cos_scores[0][0]) * 100, 2)
     return score
 
+
 def main():
     args = arg_parser()
     image1 = args.image1
     image2 = args.image2
-
-    score = round(generateScore(image1, image2), 2)
-    print(f"score is{score}, Images are different", end=" ", flush=True)
-    sleep(1)
+    score = round(generate_score(image1, image2), 2)
+    print("similarity Score: ", {score})
     if score < 99:
-        print(f"Images are different")
+        print(f"{image1} and {image2} are different")
         raise SystemExit(1)
+    else:
+        print(f"{image1} and {image2} are same")
+
 
-if __name__ == "_main__":
+if __name__ == "__main__":
     main()