diff --git a/scripts/image_scorer.py b/scripts/image_scorer.py
index eef2a1e..feb9d04 100644
--- a/scripts/image_scorer.py
+++ b/scripts/image_scorer.py
@@ -1,4 +1,5 @@
 from modules import sd_samplers, shared, scripts, script_callbacks
+from modules.script_callbacks import ImageSaveParams
 import modules.images as images
 from modules.processing import Processed, process_images, StableDiffusionProcessing
 from modules.shared import opts, OptionInfo
@@ -26,6 +27,7 @@ if not Path(state_name).exists():
     with open(state_name, "wb") as f:
         f.write(r.content)
 
+
 class AestheticPredictor(nn.Module):
     def __init__(self, input_size):
         super().__init__()
@@ -44,6 +46,7 @@ class AestheticPredictor(nn.Module):
     def forward(self, x):
         return self.layers(x)
 
+
 try:
     force_cpu = opts.ais_force_cpu
 except:
@@ -53,7 +56,7 @@ if force_cpu:
     print("Aesthtic Image Scorer: Forcing prediction model to run on CPU")
 device = "cuda" if not force_cpu and torch.cuda.is_available() else "cpu"
 # load the model you trained previously or the model available in this repo
-pt_state = torch.load(state_name, map_location=torch.device(device=device)) 
+pt_state = torch.load(state_name, map_location=torch.device(device=device))
 
 # CLIP embedding dim is 768 for CLIP ViT L 14
 predictor = AestheticPredictor(768)
@@ -63,6 +66,7 @@ predictor.eval()
 
 clip_model, clip_preprocess = clip.load("ViT-L/14", device=device)
 
+
 def get_image_features(image, device=device, model=clip_model, preprocess=clip_preprocess):
     image = preprocess(image).unsqueeze(0).to(device)
     with torch.no_grad():
@@ -72,6 +76,7 @@ def get_image_features(image, device=device, model=clip_model, preprocess=clip_p
     image_features = image_features.cpu().detach().numpy()
     return image_features
 
+
 def get_score(image):
     image_features = get_image_features(image)
     score = predictor(torch.from_numpy(image_features).to(device).float())
@@ -85,34 +90,33 @@ def on_ui_settings():
         "ais_windows_tag": OptionInfo(False, "Save score as tag (Windows Only)"),
         "ais_force_cpu": OptionInfo(False, "Force CPU (Requires Custom Script Reload)"),
     }))
-    
+
     opts.add_option("ais_add_exif", options["ais_add_exif"])
     opts.add_option("ais_windows_tag", options["ais_windows_tag"])
     opts.add_option("ais_force_cpu", options["ais_force_cpu"])
 
 
-def on_before_image_saved(image, p, **kwargs):
+def on_before_image_saved(params: ImageSaveParams):
     if opts.ais_add_exif:
-        score = round(get_score(image), 1)
-        if "existing_info" not in kwargs or kwargs["existing_info"] is None:
-            kwargs["existing_info"] = {}
-        kwargs["existing_info"].update({
+        score = round(get_score(params.image), 1)
+        params.pnginfo.update({
             "aesthetic_score": score,
         })
-    return image, p, kwargs
 
-def on_image_saved(image, p, fullfn, txt_fullfn, **kwargs):
-    if "existing_info" in kwargs and kwargs["existing_info"] is not None and "aesthetic_score" in kwargs["existing_info"]:
-        score = kwargs["existing_info"]["aesthetic_score"]
+
+def on_image_saved(params: ImageSaveParams):
+    if "aesthetic_score" in params.pnginfo:
+        score = params.pnginfo["aesthetic_score"]
     else:
-        score = round(get_score(image), 1)
+        score = round(get_score(params.image), 1)
     if score is not None and opts.ais_windows_tag:
         if tag_files is not None:
             tags = [f"aesthetic_score_{score}"]
-            tag_files(filename=fullfn, tags=tags)
+            tag_files(filename=params.filename, tags=tags)
         else:
             print("Aesthetic Image Scorer: Unable to load Windows tagging script")
 
+
 class AestheticImageScorer(scripts.Script):
     def title(self):
         return "Aesthetic Image Scorer"
@@ -126,6 +130,7 @@ class AestheticImageScorer(scripts.Script):
     def process(self, p):
         pass
 
+
 script_callbacks.on_ui_settings(on_ui_settings)
 script_callbacks.on_before_image_saved(on_before_image_saved)
 script_callbacks.on_image_saved(on_image_saved)