diff --git a/src/anomalib/engine/engine.py b/src/anomalib/engine/engine.py
index e7612e6e57..c0de7dbea1 100644
--- a/src/anomalib/engine/engine.py
+++ b/src/anomalib/engine/engine.py
@@ -8,7 +8,6 @@
 from pathlib import Path
 from typing import Any
 
-import torch
 from lightning.pytorch.callbacks import Callback
 from lightning.pytorch.loggers import Logger
 from lightning.pytorch.trainer import Trainer
@@ -302,60 +301,6 @@ def _setup_dataset_task(
                             )
                             data.task = self.task
 
-    @staticmethod
-    def _setup_transform(
-        model: AnomalyModule,
-        datamodule: AnomalibDataModule | None = None,
-        dataloaders: EVAL_DATALOADERS | TRAIN_DATALOADERS | None = None,
-        ckpt_path: Path | str | None = None,
-    ) -> None:
-        """Implements the logic for setting the transform at the start of each run.
-
-        Any transform passed explicitly to the datamodule takes precedence. Otherwise, if a checkpoint path is provided,
-        we can load the transform from the checkpoint. If no transform is provided, we use the default transform from
-        the model.
-
-        Args:
-            model (AnomalyModule): The model to assign the transform to.
-            datamodule (AnomalibDataModule | None): The datamodule to assign the transform from.
-                defaults to ``None``.
-            dataloaders (EVAL_DATALOADERS | TRAIN_DATALOADERS | None): Dataloaders to assign the transform to.
-                defaults to ``None``.
-            ckpt_path (str): The path to the checkpoint.
-                defaults to ``None``.
-
-        Returns:
-            Transform: The transform loaded from the checkpoint.
-        """
-        if isinstance(dataloaders, DataLoader):
-            dataloaders = [dataloaders]
-
-        # get transform
-        if datamodule and datamodule.transform:
-            # a transform passed explicitly to the datamodule takes precedence
-            transform = datamodule.transform
-        elif dataloaders and any(getattr(dl.dataset, "transform", None) for dl in dataloaders):
-            # if dataloaders are provided, we use the transform from the first dataloader that has a transform
-            transform = next(dl.dataset.transform for dl in dataloaders if getattr(dl.dataset, "transform", None))
-        elif ckpt_path is not None:
-            # if a checkpoint path is provided, we can load the transform from the checkpoint
-            checkpoint = torch.load(ckpt_path, map_location=model.device)
-            transform = checkpoint["transform"]
-        elif model.transform is None:
-            # if no transform is provided, we use the default transform from the model
-            image_size = datamodule.image_size if datamodule else None
-            transform = model.configure_transforms(image_size)
-        else:
-            transform = model.transform
-
-        # update transform in model
-        model.set_transform(transform)
-        # The dataloaders don't have access to the trainer and/or model, so we need to set the transforms manually
-        if dataloaders:
-            for dataloader in dataloaders:
-                if not getattr(dataloader.dataset, "transform", None):
-                    dataloader.dataset.transform = transform
-
     def _setup_anomalib_callbacks(self, model: AnomalyModule) -> None:
         """Set up callbacks for the trainer."""
         _callbacks: list[Callback] = []
@@ -471,7 +416,6 @@ def fit(
         )
         self._setup_trainer(model)
         self._setup_dataset_task(train_dataloaders, val_dataloaders, datamodule)
-        self._setup_transform(model, datamodule=datamodule, ckpt_path=ckpt_path)
         if model.learning_type in {LearningType.ZERO_SHOT, LearningType.FEW_SHOT}:
             # if the model is zero-shot or few-shot, we only need to run validate for normalization and thresholding
             self.trainer.validate(model, val_dataloaders, datamodule=datamodule, ckpt_path=ckpt_path)
@@ -525,7 +469,6 @@ def validate(
         if model:
             self._setup_trainer(model)
         self._setup_dataset_task(dataloaders)
-        self._setup_transform(model or self.model, datamodule=datamodule, ckpt_path=ckpt_path)
         return self.trainer.validate(model, dataloaders, ckpt_path, verbose, datamodule)
 
     def test(
@@ -619,7 +562,6 @@ def test(
             raise RuntimeError(msg)
 
         self._setup_dataset_task(dataloaders)
-        self._setup_transform(model or self.model, datamodule=datamodule, ckpt_path=ckpt_path)
         if self._should_run_validation(model or self.model, ckpt_path):
             logger.info("Running validation before testing to collect normalization metrics and/or thresholds.")
             self.trainer.validate(model, dataloaders, None, verbose=False, datamodule=datamodule)
@@ -724,7 +666,6 @@ def predict(
         dataloaders = dataloaders or None
 
         self._setup_dataset_task(dataloaders, datamodule)
-        self._setup_transform(model or self.model, datamodule=datamodule, dataloaders=dataloaders, ckpt_path=ckpt_path)
 
         if self._should_run_validation(model or self.model, ckpt_path):
             logger.info("Running validation before predicting to collect normalization metrics and/or thresholds.")
@@ -794,7 +735,6 @@ def train(
             test_dataloaders,
             datamodule,
         )
-        self._setup_transform(model, datamodule=datamodule, ckpt_path=ckpt_path)
         if model.learning_type in {LearningType.ZERO_SHOT, LearningType.FEW_SHOT}:
             # if the model is zero-shot or few-shot, we only need to run validate for normalization and thresholding
             self.trainer.validate(model, val_dataloaders, None, verbose=False, datamodule=datamodule)
@@ -841,8 +781,7 @@ def export(
             Path: Path to the exported model.
 
         Raises:
-            ValueError: If Dataset, Datamodule, and transform are not provided.
-            TypeError: If path to the transform file is not a string or Path.
+            ValueError: If Dataset, Datamodule are not provided.
 
         CLI Usage:
             1. To export as a torch ``.pt`` file you can run the following command.