diff --git a/src/wdtagger/__init__.py b/src/wdtagger/__init__.py
index e6f9482..e99f8b9 100644
--- a/src/wdtagger/__init__.py
+++ b/src/wdtagger/__init__.py
@@ -14,7 +14,6 @@ from huggingface_hub import hf_hub_download
 from huggingface_hub.utils import HfHubHTTPError
 from PIL import Image
 from PIL.ImageFile import ImageFile
-from rich.console import Console
 from timm.data import create_transform, resolve_data_config
 from torch import Tensor, nn
 from torch.nn import functional as F
@@ -137,15 +136,7 @@ class Result:
         character_tag: dict[str, float],
         general_tag: dict[str, float],
     ) -> None:
-        """Initialize the Result object to store tagging results.
-
-        Args:
-            preds (np.array): Predictions array from the model.
-            sep_tags (tuple): Tuple containing separated tags based on categories.
-            general_threshold (float): Threshold for general tags.
-            character_threshold (float): Threshold for character tags.
-        """
-
+        """Initialize the Result object with the tags and their ratings."""
         self.general_tag_data = general_tag
         self.character_tag_data = character_tag
         self.rating_data = rating_data
@@ -228,30 +219,9 @@ class Tagger:
         self,
         model_repo: str = "SmilingWolf/wd-swinv2-tagger-v3",
         hf_token: str = HF_TOKEN,
-        console: Console | None = None,
-        *,
-        slient: bool = False,
     ) -> None:
-        """Initialize the Tagger object with the model repository and tokens.
-
-        Args:
-            model_repo (str): Repository name on HuggingFace.
-            cache_dir (str, optional): Directory to cache the model. Defaults to None.
-            hf_token (str, optional): HuggingFace token for authentication. Defaults to HF_TOKEN.
-            loglevel (int, optional): Logging level. Defaults to logging.INFO.
-            num_threads (int, optional): Number of threads for ONNX runtime. Defaults to None.
-            providers (list, optional): List of providers for ONNX runtime. Defaults to None.
-            console (rich.console.Console, optional): Rich console object. Defaults to None.
-        """
-        self.slient = slient
-        if not slient:
-            if not console:
-                from rich import get_console
-
-                self.console = get_console()
-            else:
-                self.console = console
-            self.logger = logging.getLogger("wdtagger")
+        """Initialize the Tagger object with the model repository and tokens."""
+        self.logger = logging.getLogger("wdtagger")
         self.model_target_size = None
         self.hf_token = hf_token
         self.torch_device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -269,11 +239,10 @@ class Tagger:
             hf_token (str, optional): HuggingFace token for authentication. Defaults to None.
             num_threads (int, optional): Number of threads for ONNX runtime. Defaults to None.
         """
-        if not self.slient:
-            with self.console.status(f"Loading model '{model_repo}'..."):
-                self._do_load_model(model_repo)
-        else:
-            self._do_load_model(model_repo)
+        start_time = time.time()
+        self.logger.info("Loading model from %s", model_repo)
+        self._do_load_model(model_repo)
+        self.logger.info("Model loaded successfully in %.2fs", time.time() - start_time)
 
     def _do_load_model(self, model_repo: str) -> None:
         model: nn.Module = timm.create_model(f"hf-hub:{model_repo}").eval()
@@ -354,13 +323,12 @@ class Tagger:
 
         duration = time.time() - started_at
         image_length = len(images)
-        if not self.slient:
-            self.logger.info(
-                "Tagged %d image%s in %.2fs",
-                image_length,
-                "s" if image_length > 1 else "",
-                duration,
-            )
+        self.logger.info(
+            "Tagged %d image%s in %.2fs",
+            image_length,
+            "s" if image_length > 1 else "",
+            duration,
+        )
         if input_is_list:
             return results
         return results[0] if len(results) == 1 else results