diff --git a/keras_nlp/api/__init__.py b/keras_nlp/api/__init__.py
index 0225dd69a0..d0dc4576c6 100644
--- a/keras_nlp/api/__init__.py
+++ b/keras_nlp/api/__init__.py
@@ -22,7 +22,6 @@
 from keras_nlp.api import models
 from keras_nlp.api import samplers
 from keras_nlp.api import tokenizers
-from keras_nlp.api import utils
 from keras_nlp.src.utils.preset_utils import upload_preset
 from keras_nlp.src.version_utils import __version__
 from keras_nlp.src.version_utils import version
diff --git a/keras_nlp/api/layers/__init__.py b/keras_nlp/api/layers/__init__.py
index 73ad66b30b..8b92cc11b0 100644
--- a/keras_nlp/api/layers/__init__.py
+++ b/keras_nlp/api/layers/__init__.py
@@ -36,6 +36,8 @@
 )
 from keras_nlp.src.layers.modeling.transformer_decoder import TransformerDecoder
 from keras_nlp.src.layers.modeling.transformer_encoder import TransformerEncoder
+from keras_nlp.src.layers.preprocessing.audio_converter import AudioConverter
+from keras_nlp.src.layers.preprocessing.image_converter import ImageConverter
 from keras_nlp.src.layers.preprocessing.masked_lm_mask_generator import (
     MaskedLMMaskGenerator,
 )
@@ -44,4 +46,13 @@
 )
 from keras_nlp.src.layers.preprocessing.random_deletion import RandomDeletion
 from keras_nlp.src.layers.preprocessing.random_swap import RandomSwap
+from keras_nlp.src.layers.preprocessing.resizing_image_converter import (
+    ResizingImageConverter,
+)
 from keras_nlp.src.layers.preprocessing.start_end_packer import StartEndPacker
+from keras_nlp.src.models.pali_gemma.pali_gemma_image_converter import (
+    PaliGemmaImageConverter,
+)
+from keras_nlp.src.models.whisper.whisper_audio_converter import (
+    WhisperAudioConverter,
+)
diff --git a/keras_nlp/api/models/__init__.py b/keras_nlp/api/models/__init__.py
index e78271531c..64368e4c45 100644
--- a/keras_nlp/api/models/__init__.py
+++ b/keras_nlp/api/models/__init__.py
@@ -228,13 +228,7 @@
 from keras_nlp.src.models.text_classifier_preprocessor import (
     TextClassifierPreprocessor,
 )
-from keras_nlp.src.models.whisper.whisper_audio_feature_extractor import (
-    WhisperAudioFeatureExtractor,
-)
 from keras_nlp.src.models.whisper.whisper_backbone import WhisperBackbone
-from keras_nlp.src.models.whisper.whisper_preprocessor import (
-    WhisperPreprocessor,
-)
 from keras_nlp.src.models.whisper.whisper_tokenizer import WhisperTokenizer
 from keras_nlp.src.models.xlm_roberta.xlm_roberta_backbone import (
     XLMRobertaBackbone,
diff --git a/keras_nlp/src/layers/preprocessing/audio_converter.py b/keras_nlp/src/layers/preprocessing/audio_converter.py
new file mode 100644
index 0000000000..c552b86e04
--- /dev/null
+++ b/keras_nlp/src/layers/preprocessing/audio_converter.py
@@ -0,0 +1,121 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.layers.preprocessing.preprocessing_layer import (
+    PreprocessingLayer,
+)
+from keras_nlp.src.utils.preset_utils import AUDIO_CONVERTER_CONFIG_FILE
+from keras_nlp.src.utils.preset_utils import find_subclass
+from keras_nlp.src.utils.preset_utils import get_preset_loader
+from keras_nlp.src.utils.preset_utils import list_presets
+from keras_nlp.src.utils.preset_utils import list_subclasses
+from keras_nlp.src.utils.preset_utils import save_serialized_object
+from keras_nlp.src.utils.python_utils import classproperty
+
+
+@keras_nlp_export("keras_nlp.layers.AudioConverter")
+class AudioConverter(PreprocessingLayer):
+    """Convert raw audio for models that support audio input.
+
+    This class converts from raw audio tensors of any length, to preprocessed
+    audio for pretrained model inputs. It is meant to be a convenient way to
+    write custom preprocessing code that is not model specific. This layer
+    should be instantiated via the `from_preset()` constructor, which will
+    create the correct subclass of this layer for the model preset.
+
+    The layer will take as input a raw audio tensor with shape `(batch_size,
+    num_samples)`, and output a preprocessed audio input for modeling. The exact
+    structure of the preprocessed input will vary per model. Preprocessing
+    will often include computing a spectogram of the raw audio signal.
+
+    Examples:
+    ```python
+    # Load an audio converter from a preset.
+    converter = keras_nlp.layers.AudioConverter.from_preset("whisper_base_en")
+    # Convert some raw audio input.
+    converter(np.ones(2, 1_000))
+    ```
+    """
+
+    backbone_cls = None
+
+    @classproperty
+    def presets(cls):
+        """List built-in presets for a `Task` subclass."""
+        presets = list_presets(cls)
+        for subclass in list_subclasses(cls):
+            presets.update(subclass.presets)
+        return presets
+
+    @classmethod
+    def from_preset(
+        cls,
+        preset,
+        **kwargs,
+    ):
+        """Instantiate a `keras_nlp.layers.AudioConverter` from a model preset.
+
+        A preset is a directory of configs, weights and other file assets used
+        to save and load a pre-trained model. The `preset` can be passed as
+        one of:
+
+        1. a built-in preset identifier like `'whisper_base_en'`
+        2. a Kaggle Models handle like
+           `'kaggle://user/whisper/keras/whisper_base_en'`
+        3. a Hugging Face handle like `'hf://user/whisper_base_en'`
+        4. a path to a local preset directory like `'./whisper_base_en'`
+
+        You can run `cls.presets.keys()` to list all built-in presets available
+        on the class.
+
+        This constructor can be called in one of two ways. Either from the base
+        class like `keras_nlp.models.AudioConverter.from_preset()`, or from a
+        model class like `keras_nlp.models.WhisperAudioConverter.from_preset()`.
+        If calling from the base class, the subclass of the returning object
+        will be inferred from the config in the preset directory.
+
+        Args:
+            preset: string. A built-in preset identifier, a Kaggle Models
+                handle, a Hugging Face handle, or a path to a local directory.
+            load_weights: bool. If `True`, the weights will be loaded into the
+                model architecture. If `False`, the weights will be randomly
+                initialized.
+
+        Examples:
+        ```python
+        # Load an audio converter from a preset.
+        converter = keras_nlp.layers.AudioConverter.from_preset(
+            "whisper_base_en"
+        )
+        # Convert some raw mono channel audio input.
+        converter(np.ones(2, 1_000))
+        ```
+        """
+        loader = get_preset_loader(preset)
+        backbone_cls = loader.check_backbone_class()
+        if cls.backbone_cls != backbone_cls:
+            cls = find_subclass(preset, cls, backbone_cls)
+        return loader.load_audio_converter(cls, **kwargs)
+
+    def save_to_preset(self, preset_dir):
+        """Save audio converter to a preset directory.
+
+        Args:
+            preset_dir: The path to the local model preset directory.
+        """
+        save_serialized_object(
+            self,
+            preset_dir,
+            config_file=AUDIO_CONVERTER_CONFIG_FILE,
+        )
diff --git a/keras_nlp/src/layers/preprocessing/audio_converter_test.py b/keras_nlp/src/layers/preprocessing/audio_converter_test.py
new file mode 100644
index 0000000000..7ac6ab22fd
--- /dev/null
+++ b/keras_nlp/src/layers/preprocessing/audio_converter_test.py
@@ -0,0 +1,69 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import pathlib
+
+import numpy as np
+import pytest
+
+from keras_nlp.src.layers.preprocessing.audio_converter import AudioConverter
+from keras_nlp.src.models.backbone import Backbone
+from keras_nlp.src.models.whisper.whisper_audio_converter import (
+    WhisperAudioConverter,
+)
+from keras_nlp.src.tests.test_case import TestCase
+
+
+class AudioConverterTest(TestCase):
+    def test_preset_accessors(self):
+        pali_gemma_presets = set(WhisperAudioConverter.presets.keys())
+        all_presets = set(AudioConverter.presets.keys())
+        self.assertContainsSubset(pali_gemma_presets, all_presets)
+
+    @pytest.mark.large
+    def test_from_preset(self):
+        self.assertIsInstance(
+            AudioConverter.from_preset("whisper_tiny_en"),
+            WhisperAudioConverter,
+        )
+
+    @pytest.mark.large
+    def test_from_preset_errors(self):
+        with self.assertRaises(ValueError):
+            AudioConverter.from_preset("bert_tiny_en_uncased")
+        with self.assertRaises(ValueError):
+            # No loading on a non-keras model.
+            AudioConverter.from_preset("hf://spacy/en_core_web_sm")
+
+    @pytest.mark.large
+    def test_save_to_preset(self):
+        save_dir = self.get_temp_dir()
+        converter = AudioConverter.from_preset(
+            "whisper_tiny_en",
+            num_mels=40,
+        )
+        converter.save_to_preset(save_dir)
+        # Save a backbone so the preset is valid.
+        backbone = Backbone.from_preset("whisper_tiny_en", load_weights=False)
+        backbone.save_to_preset(save_dir)
+
+        # Check existence of files.
+        path = pathlib.Path(save_dir)
+        self.assertTrue(os.path.exists(path / "audio_converter.json"))
+
+        # Check loading.
+        restored = AudioConverter.from_preset(save_dir)
+        test_audio = np.random.rand(1_000)
+        self.assertAllClose(restored(test_audio), converter(test_audio))
diff --git a/keras_nlp/src/layers/preprocessing/image_converter.py b/keras_nlp/src/layers/preprocessing/image_converter.py
new file mode 100644
index 0000000000..28ace2aba3
--- /dev/null
+++ b/keras_nlp/src/layers/preprocessing/image_converter.py
@@ -0,0 +1,130 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.layers.preprocessing.preprocessing_layer import (
+    PreprocessingLayer,
+)
+from keras_nlp.src.utils.preset_utils import IMAGE_CONVERTER_CONFIG_FILE
+from keras_nlp.src.utils.preset_utils import find_subclass
+from keras_nlp.src.utils.preset_utils import get_preset_loader
+from keras_nlp.src.utils.preset_utils import list_presets
+from keras_nlp.src.utils.preset_utils import list_subclasses
+from keras_nlp.src.utils.preset_utils import save_serialized_object
+from keras_nlp.src.utils.python_utils import classproperty
+
+
+@keras_nlp_export("keras_nlp.layers.ImageConverter")
+class ImageConverter(PreprocessingLayer):
+    """Convert raw image for models that support image input.
+
+    This class converts from raw images of any size, to preprocessed
+    images for pretrained model inputs. It is meant to be a convenient way to
+    write custom preprocessing code that is not model specific. This layer
+    should be instantiated via the `from_preset()` constructor, which will
+    create the correct subclass of this layer for the model preset.
+
+    The layer will take as input a raw image tensor in the channels last or
+    channels first format, and output a preprocessed image input for modeling.
+    The exact structure of the output will vary per model, though in most cases
+    this layer will simply resize the image to the size needed by the model
+    input.
+
+    Examples:
+    ```python
+    # Resize images for `"pali_gemma_3b_224"`.
+    converter = keras_nlp.layers.ImageConverter.from_preset("pali_gemma_3b_224")
+    converter(np.ones(2, 512, 512, 3)) # Output shape: (2, 224, 224, 3)
+    # Resize images for `"pali_gemma_3b_448"`.
+    converter = keras_nlp.layers.ImageConverter.from_preset("pali_gemma_3b_448")
+    converter(np.ones(2, 512, 512, 3)) # Output shape: (2, 448, 448, 3)
+    ```
+    """
+
+    backbone_cls = None
+
+    @classproperty
+    def presets(cls):
+        """List built-in presets for a `Task` subclass."""
+        presets = list_presets(cls)
+        for subclass in list_subclasses(cls):
+            presets.update(subclass.presets)
+        return presets
+
+    @classmethod
+    def from_preset(
+        cls,
+        preset,
+        **kwargs,
+    ):
+        """Instantiate a `keras_nlp.layers.ImageConverter` from a model preset.
+
+        A preset is a directory of configs, weights and other file assets used
+        to save and load a pre-trained model. The `preset` can be passed as
+        one of:
+
+        1. a built-in preset identifier like `'pali_gemma_3b_224'`
+        2. a Kaggle Models handle like
+           `'kaggle://user/paligemma/keras/pali_gemma_3b_224'`
+        3. a Hugging Face handle like `'hf://user/pali_gemma_3b_224'`
+        4. a path to a local preset directory like `'./pali_gemma_3b_224'`
+
+        You can run `cls.presets.keys()` to list all built-in presets available
+        on the class.
+
+        This constructor can be called in one of two ways. Either from the base
+        class like `keras_nlp.models.ImageConverter.from_preset()`, or from a
+        model class like
+        `keras_nlp.models.PaliGemmaImageConverter.from_preset()`. If calling
+        from the base class, the subclass of the returning object will be
+        inferred from the config in the preset directory.
+
+        Args:
+            preset: string. A built-in preset identifier, a Kaggle Models
+                handle, a Hugging Face handle, or a path to a local directory.
+            load_weights: bool. If `True`, the weights will be loaded into the
+                model architecture. If `False`, the weights will be randomly
+                initialized.
+
+        Examples:
+        ```python
+        # Resize images for `"pali_gemma_3b_224"`.
+        converter = keras_nlp.layers.ImageConverter.from_preset(
+            "pali_gemma_3b_224"
+        )
+        converter(np.ones(2, 512, 512, 3)) # Output shape: (2, 224, 224, 3)
+        # Override arguments on the base class.
+        converter = keras_nlp.layers.ImageConverter.from_preset(
+            "pali_gemma_3b_448",
+            crop_to_aspect_ratio=False,
+        )
+        converter(np.ones(2, 512, 512, 3)) # (2, 448, 448, 3)
+        ```
+        """
+        loader = get_preset_loader(preset)
+        backbone_cls = loader.check_backbone_class()
+        if cls.backbone_cls != backbone_cls:
+            cls = find_subclass(preset, cls, backbone_cls)
+        return loader.load_image_converter(cls, **kwargs)
+
+    def save_to_preset(self, preset_dir):
+        """Save image converter to a preset directory.
+
+        Args:
+            preset_dir: The path to the local model preset directory.
+        """
+        save_serialized_object(
+            self,
+            preset_dir,
+            config_file=IMAGE_CONVERTER_CONFIG_FILE,
+        )
diff --git a/keras_nlp/src/layers/preprocessing/image_converter_test.py b/keras_nlp/src/layers/preprocessing/image_converter_test.py
new file mode 100644
index 0000000000..c3ecefead0
--- /dev/null
+++ b/keras_nlp/src/layers/preprocessing/image_converter_test.py
@@ -0,0 +1,84 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+import pathlib
+
+import numpy as np
+import pytest
+
+from keras_nlp.src.layers.preprocessing.image_converter import ImageConverter
+from keras_nlp.src.models.pali_gemma.pali_gemma_backbone import (
+    PaliGemmaBackbone,
+)
+from keras_nlp.src.models.pali_gemma.pali_gemma_image_converter import (
+    PaliGemmaImageConverter,
+)
+from keras_nlp.src.tests.test_case import TestCase
+
+
+class ImageConverterTest(TestCase):
+    def test_preset_accessors(self):
+        pali_gemma_presets = set(PaliGemmaImageConverter.presets.keys())
+        all_presets = set(ImageConverter.presets.keys())
+        self.assertContainsSubset(pali_gemma_presets, all_presets)
+
+    @pytest.mark.large
+    def test_from_preset(self):
+        self.assertIsInstance(
+            ImageConverter.from_preset("pali_gemma_3b_mix_224"),
+            PaliGemmaImageConverter,
+        )
+
+    @pytest.mark.large
+    def test_from_preset_errors(self):
+        with self.assertRaises(ValueError):
+            ImageConverter.from_preset("bert_tiny_en_uncased")
+        with self.assertRaises(ValueError):
+            # No loading on a non-keras model.
+            ImageConverter.from_preset("hf://spacy/en_core_web_sm")
+
+    @pytest.mark.large
+    def test_save_to_preset(self):
+        save_dir = self.get_temp_dir()
+        converter = ImageConverter.from_preset(
+            "pali_gemma_3b_mix_224",
+            interpolation="nearest",
+        )
+        converter.save_to_preset(save_dir)
+        # Save a tiny backbone so the preset is valid.
+        backbone = PaliGemmaBackbone(
+            vocabulary_size=100,
+            image_size=224,
+            num_layers=1,
+            num_query_heads=1,
+            num_key_value_heads=1,
+            hidden_dim=8,
+            intermediate_dim=16,
+            head_dim=8,
+            vit_patch_size=14,
+            vit_num_heads=1,
+            vit_hidden_dim=8,
+            vit_num_layers=1,
+        )
+        backbone.save_to_preset(save_dir)
+
+        # Check existence of files.
+        path = pathlib.Path(save_dir)
+        self.assertTrue(os.path.exists(path / "image_converter.json"))
+
+        # Check loading.
+        restored = ImageConverter.from_preset(save_dir)
+        test_image = np.random.rand(100, 100, 3) * 255
+        self.assertAllClose(restored(test_image), converter(test_image))
diff --git a/keras_nlp/src/layers/preprocessing/masked_lm_mask_generator.py b/keras_nlp/src/layers/preprocessing/masked_lm_mask_generator.py
index 2c14394437..fe78e0c172 100644
--- a/keras_nlp/src/layers/preprocessing/masked_lm_mask_generator.py
+++ b/keras_nlp/src/layers/preprocessing/masked_lm_mask_generator.py
@@ -18,7 +18,7 @@
     PreprocessingLayer,
 )
 from keras_nlp.src.utils.tensor_utils import convert_to_ragged_batch
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 try:
     import tensorflow as tf
@@ -166,7 +166,7 @@ def __init__(
             random_token_rate=self.random_token_rate,
         )
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(self, inputs):
         inputs, unbatched, rectangular = convert_to_ragged_batch(inputs)
 
diff --git a/keras_nlp/src/layers/preprocessing/multi_segment_packer.py b/keras_nlp/src/layers/preprocessing/multi_segment_packer.py
index a972bf551c..53625783cc 100644
--- a/keras_nlp/src/layers/preprocessing/multi_segment_packer.py
+++ b/keras_nlp/src/layers/preprocessing/multi_segment_packer.py
@@ -17,7 +17,7 @@
     PreprocessingLayer,
 )
 from keras_nlp.src.utils.tensor_utils import convert_to_ragged_batch
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 try:
     import tensorflow as tf
@@ -282,7 +282,7 @@ def _combine_inputs(
         segment_ids = tf.concat(segment_ids_to_combine, 1)
         return token_ids, segment_ids
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(
         self,
         inputs,
diff --git a/keras_nlp/src/layers/preprocessing/multi_segment_packer_test.py b/keras_nlp/src/layers/preprocessing/multi_segment_packer_test.py
index 463a43b412..2c34dcd0bd 100644
--- a/keras_nlp/src/layers/preprocessing/multi_segment_packer_test.py
+++ b/keras_nlp/src/layers/preprocessing/multi_segment_packer_test.py
@@ -31,7 +31,7 @@ def test_trim_single_input_ints(self):
         self.assertAllEqual(segment_ids, [0, 0, 0, 0, 0, 0, 0, 0])
 
     def test_trim_single_input_strings(self):
-        input_data = np.array(["a", "b", "c", "d"])
+        input_data = ["a", "b", "c", "d"]
         packer = MultiSegmentPacker(
             sequence_length=5, start_value="[CLS]", end_value="[SEP]"
         )
diff --git a/keras_nlp/src/layers/preprocessing/random_deletion.py b/keras_nlp/src/layers/preprocessing/random_deletion.py
index bd1b6764fa..6df8b6ef28 100644
--- a/keras_nlp/src/layers/preprocessing/random_deletion.py
+++ b/keras_nlp/src/layers/preprocessing/random_deletion.py
@@ -21,7 +21,7 @@
 from keras_nlp.src.utils.tensor_utils import convert_to_ragged_batch
 from keras_nlp.src.utils.tensor_utils import is_int_dtype
 from keras_nlp.src.utils.tensor_utils import is_string_dtype
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 try:
     import tensorflow as tf
@@ -171,7 +171,7 @@ def __init__(
                 default_value=False,
             )
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(self, inputs):
         inputs, unbatched, rectangular = convert_to_ragged_batch(inputs)
 
diff --git a/keras_nlp/src/layers/preprocessing/random_swap.py b/keras_nlp/src/layers/preprocessing/random_swap.py
index 71b07193dc..32f7a08d26 100644
--- a/keras_nlp/src/layers/preprocessing/random_swap.py
+++ b/keras_nlp/src/layers/preprocessing/random_swap.py
@@ -21,7 +21,7 @@
 from keras_nlp.src.utils.tensor_utils import convert_to_ragged_batch
 from keras_nlp.src.utils.tensor_utils import is_int_dtype
 from keras_nlp.src.utils.tensor_utils import is_string_dtype
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 try:
     import tensorflow as tf
@@ -167,7 +167,7 @@ def __init__(
                 default_value=False,
             )
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(self, inputs):
         inputs, unbatched, rectangular = convert_to_ragged_batch(inputs)
 
diff --git a/keras_nlp/src/layers/preprocessing/resizing_image_converter.py b/keras_nlp/src/layers/preprocessing/resizing_image_converter.py
new file mode 100644
index 0000000000..8012acf5c2
--- /dev/null
+++ b/keras_nlp/src/layers/preprocessing/resizing_image_converter.py
@@ -0,0 +1,97 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import keras
+
+from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.layers.preprocessing.image_converter import ImageConverter
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
+
+
+@keras_nlp_export("keras_nlp.layers.ResizingImageConverter")
+class ResizingImageConverter(ImageConverter):
+    """An `ImageConverter` that simply resizes the input image.
+
+    The `ResizingImageConverter` is a subclass of `ImageConverter` for models
+    that simply need to resize image tensors before using them for modeling.
+    The layer will take as input a raw image tensor (batched or unbatched) in the
+    channels last or channels first format, and output a resize tensor.
+
+    Args:
+        height: Integer, the height of the output shape.
+        width: Integer, the width of the output shape.
+        crop_to_aspect_ratio: If `True`, resize the images without aspect
+            ratio distortion. When the original aspect ratio differs
+            from the target aspect ratio, the output image will be
+            cropped so as to return the
+            largest possible window in the image (of size `(height, width)`)
+            that matches the target aspect ratio. By default
+            (`crop_to_aspect_ratio=False`), aspect ratio may not be preserved.
+        interpolation: String, the interpolation method.
+            Supports `"bilinear"`, `"nearest"`, `"bicubic"`,
+            `"lanczos3"`, `"lanczos5"`. Defaults to `"bilinear"`.
+        data_format: String, either `"channels_last"` or `"channels_first"`.
+            The ordering of the dimensions in the inputs. `"channels_last"`
+            corresponds to inputs with shape `(batch, height, width, channels)`
+            while `"channels_first"` corresponds to inputs with shape
+            `(batch, channels, height, width)`. It defaults to the
+            `image_data_format` value found in your Keras config file at
+            `~/.keras/keras.json`. If you never set it, then it will be
+            `"channels_last"`.
+
+    Examples:
+    ```python
+    # Resize images for `"pali_gemma_3b_224"`.
+    converter = keras_nlp.layers.ImageConverter.from_preset("pali_gemma_3b_224")
+    converter(np.ones(2, 512, 512, 3)) # Output shape: (2, 224, 224, 3)
+    # Resize images for `"pali_gemma_3b_224"`.
+    converter = keras_nlp.layers.ImageConverter.from_preset("pali_gemma_3b_448")
+    converter(np.ones(2, 512, 512, 3)) # Output shape: (2, 448, 448, 3)
+    ```
+    """
+
+    def __init__(
+        self,
+        height,
+        width,
+        crop_to_aspect_ratio=True,
+        interpolation="bilinear",
+        data_format=None,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        # By default, we just do a simple resize. Any model can subclass this
+        # layer for preprocessing of a raw image to a model image input.
+        self.resizing = keras.layers.Resizing(
+            height,
+            width,
+            crop_to_aspect_ratio=crop_to_aspect_ratio,
+            interpolation=interpolation,
+            data_format=data_format,
+        )
+
+    @preprocessing_function
+    def call(self, inputs):
+        return self.resizing(inputs)
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "height": self.resizing.height,
+                "width": self.resizing.width,
+                "interpolation": self.resizing.interpolation,
+                "crop_to_aspect_ratio": self.resizing.crop_to_aspect_ratio,
+            }
+        )
+        return config
diff --git a/keras_nlp/src/layers/preprocessing/resizing_image_converter_test.py b/keras_nlp/src/layers/preprocessing/resizing_image_converter_test.py
new file mode 100644
index 0000000000..f96a3a3488
--- /dev/null
+++ b/keras_nlp/src/layers/preprocessing/resizing_image_converter_test.py
@@ -0,0 +1,46 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+from keras import ops
+
+from keras_nlp.src.layers.preprocessing.resizing_image_converter import (
+    ResizingImageConverter,
+)
+from keras_nlp.src.tests.test_case import TestCase
+
+
+class ResizingImageConverterTest(TestCase):
+    def test_resize_one(self):
+        converter = ResizingImageConverter(22, 22)
+        test_image = np.random.rand(10, 10, 3) * 255
+        shape = ops.shape(converter(test_image))
+        self.assertEqual(shape, (22, 22, 3))
+
+    def test_resize_batch(self):
+        converter = ResizingImageConverter(12, 12)
+        test_batch = np.random.rand(4, 10, 20, 3) * 255
+        shape = ops.shape(converter(test_batch))
+        self.assertEqual(shape, (4, 12, 12, 3))
+
+    def test_config(self):
+        converter = ResizingImageConverter(
+            width=12,
+            height=20,
+            crop_to_aspect_ratio=False,
+            interpolation="nearest",
+        )
+        clone = ResizingImageConverter.from_config(converter.get_config())
+        test_batch = np.random.rand(4, 10, 20, 3) * 255
+        self.assertAllClose(converter(test_batch), clone(test_batch))
diff --git a/keras_nlp/src/layers/preprocessing/start_end_packer.py b/keras_nlp/src/layers/preprocessing/start_end_packer.py
index e1712eba91..a6e7b4d068 100644
--- a/keras_nlp/src/layers/preprocessing/start_end_packer.py
+++ b/keras_nlp/src/layers/preprocessing/start_end_packer.py
@@ -18,7 +18,7 @@
     PreprocessingLayer,
 )
 from keras_nlp.src.utils.tensor_utils import convert_to_ragged_batch
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 try:
     import tensorflow as tf
@@ -155,7 +155,7 @@ def check_special_value_type(value, value_name):
         self.pad_value = pad_value
         self.return_padding_mask = return_padding_mask
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(
         self,
         inputs,
diff --git a/keras_nlp/src/layers/preprocessing/start_end_packer_test.py b/keras_nlp/src/layers/preprocessing/start_end_packer_test.py
index dc99eaa8cd..5fb77a930e 100644
--- a/keras_nlp/src/layers/preprocessing/start_end_packer_test.py
+++ b/keras_nlp/src/layers/preprocessing/start_end_packer_test.py
@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import keras
 import tensorflow as tf
 
 from keras_nlp.src.layers.preprocessing.start_end_packer import StartEndPacker
@@ -30,13 +29,10 @@ def test_dense_input(self):
     def test_bfloat16_dtype(self):
         # Core Keras has a strange bug where it converts int to floats in
         # ops.convert_to_tensor only with jax and bfloat16.
-        floatx = keras.config.floatx()
-        keras.config.set_floatx("bfloat16")
         input_data = [5, 6, 7]
         start_end_packer = StartEndPacker(sequence_length=5, dtype="bfloat16")
         output = start_end_packer(input_data)
         self.assertDTypeEqual(output, "int32")
-        keras.config.set_floatx(floatx)
 
     def test_dense_2D_input(self):
         input_data = [[5, 6, 7]]
diff --git a/keras_nlp/src/models/bart/bart_preprocessor.py b/keras_nlp/src/models/bart/bart_preprocessor.py
index dc013779a3..0f9de65fab 100644
--- a/keras_nlp/src/models/bart/bart_preprocessor.py
+++ b/keras_nlp/src/models/bart/bart_preprocessor.py
@@ -20,7 +20,7 @@
 from keras_nlp.src.models.bart.bart_backbone import BartBackbone
 from keras_nlp.src.models.bart.bart_tokenizer import BartTokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.BartPreprocessor")
@@ -174,7 +174,7 @@ def build(self, input_shape):
         )
         self.built = True
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(
         self,
         x,
diff --git a/keras_nlp/src/models/bloom/bloom_preprocessor.py b/keras_nlp/src/models/bloom/bloom_preprocessor.py
index 6c572591b7..8b2d7b2ba0 100644
--- a/keras_nlp/src/models/bloom/bloom_preprocessor.py
+++ b/keras_nlp/src/models/bloom/bloom_preprocessor.py
@@ -20,7 +20,7 @@
 from keras_nlp.src.models.bloom.bloom_backbone import BloomBackbone
 from keras_nlp.src.models.bloom.bloom_tokenizer import BloomTokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.BloomPreprocessor")
@@ -134,7 +134,7 @@ def build(self, input_shape):
         )
         self.built = True
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(
         self,
         x,
diff --git a/keras_nlp/src/models/causal_lm_preprocessor.py b/keras_nlp/src/models/causal_lm_preprocessor.py
index 1713ce6566..6a0dad3bdf 100644
--- a/keras_nlp/src/models/causal_lm_preprocessor.py
+++ b/keras_nlp/src/models/causal_lm_preprocessor.py
@@ -16,8 +16,8 @@
 from keras_nlp.src.api_export import keras_nlp_export
 from keras_nlp.src.layers.preprocessing.start_end_packer import StartEndPacker
 from keras_nlp.src.models.preprocessor import Preprocessor
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 from keras_nlp.src.utils.tensor_utils import strip_to_ragged
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.CausalLMPreprocessor")
@@ -98,7 +98,7 @@ def build(self, input_shape):
         )
         self.built = True
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(
         self,
         x,
@@ -124,7 +124,7 @@ def call(
         y, sample_weight = token_ids[..., 1:], padding_mask[..., 1:]
         return keras.utils.pack_x_y_sample_weight(x, y, sample_weight)
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def generate_preprocess(
         self,
         x,
@@ -153,7 +153,7 @@ def generate_preprocess(
             "padding_mask": padding_mask,
         }
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def generate_postprocess(
         self,
         x,
diff --git a/keras_nlp/src/models/deberta_v3/deberta_v3_masked_lm_preprocessor.py b/keras_nlp/src/models/deberta_v3/deberta_v3_masked_lm_preprocessor.py
index 8096e76e37..6823d1a8eb 100644
--- a/keras_nlp/src/models/deberta_v3/deberta_v3_masked_lm_preprocessor.py
+++ b/keras_nlp/src/models/deberta_v3/deberta_v3_masked_lm_preprocessor.py
@@ -22,7 +22,7 @@
     DebertaV3Tokenizer,
 )
 from keras_nlp.src.models.masked_lm_preprocessor import MaskedLMPreprocessor
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.DebertaV3MaskedLMPreprocessor")
@@ -119,7 +119,7 @@ class DebertaV3MaskedLMPreprocessor(MaskedLMPreprocessor):
     backbone_cls = DebertaV3Backbone
     tokenizer_cls = DebertaV3Tokenizer
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(self, x, y=None, sample_weight=None):
         output = super().call(x, y=y, sample_weight=sample_weight)
         x, y, sample_weight = keras.utils.unpack_x_y_sample_weight(output)
diff --git a/keras_nlp/src/models/deberta_v3/deberta_v3_text_classifier_preprocessor.py b/keras_nlp/src/models/deberta_v3/deberta_v3_text_classifier_preprocessor.py
index 6f1996d3b4..9634b77238 100644
--- a/keras_nlp/src/models/deberta_v3/deberta_v3_text_classifier_preprocessor.py
+++ b/keras_nlp/src/models/deberta_v3/deberta_v3_text_classifier_preprocessor.py
@@ -24,7 +24,7 @@
 from keras_nlp.src.models.text_classifier_preprocessor import (
     TextClassifierPreprocessor,
 )
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export(
@@ -156,7 +156,7 @@ class DebertaV3TextClassifierPreprocessor(TextClassifierPreprocessor):
     backbone_cls = DebertaV3Backbone
     tokenizer_cls = DebertaV3Tokenizer
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(self, x, y=None, sample_weight=None):
         output = super().call(x, y=y, sample_weight=sample_weight)
         x, y, sample_weight = keras.utils.unpack_x_y_sample_weight(output)
diff --git a/keras_nlp/src/models/distil_bert/distil_bert_masked_lm_preprocessor.py b/keras_nlp/src/models/distil_bert/distil_bert_masked_lm_preprocessor.py
index 9004fa06ba..1a69de4e50 100644
--- a/keras_nlp/src/models/distil_bert/distil_bert_masked_lm_preprocessor.py
+++ b/keras_nlp/src/models/distil_bert/distil_bert_masked_lm_preprocessor.py
@@ -22,7 +22,7 @@
     DistilBertTokenizer,
 )
 from keras_nlp.src.models.masked_lm_preprocessor import MaskedLMPreprocessor
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.DistilBertMaskedLMPreprocessor")
@@ -123,7 +123,7 @@ class DistilBertMaskedLMPreprocessor(MaskedLMPreprocessor):
     backbone_cls = DistilBertBackbone
     tokenizer_cls = DistilBertTokenizer
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(self, x, y=None, sample_weight=None):
         output = super().call(x, y=y, sample_weight=sample_weight)
         x, y, sample_weight = keras.utils.unpack_x_y_sample_weight(output)
diff --git a/keras_nlp/src/models/distil_bert/distil_bert_text_classifier_preprocessor.py b/keras_nlp/src/models/distil_bert/distil_bert_text_classifier_preprocessor.py
index 90ebe7ef3b..f4bda84a2c 100644
--- a/keras_nlp/src/models/distil_bert/distil_bert_text_classifier_preprocessor.py
+++ b/keras_nlp/src/models/distil_bert/distil_bert_text_classifier_preprocessor.py
@@ -25,7 +25,7 @@
 from keras_nlp.src.models.text_classifier_preprocessor import (
     TextClassifierPreprocessor,
 )
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export(
@@ -126,7 +126,7 @@ class DistilBertTextClassifierPreprocessor(TextClassifierPreprocessor):
     backbone_cls = DistilBertBackbone
     tokenizer_cls = DistilBertTokenizer
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(self, x, y=None, sample_weight=None):
         output = super().call(x, y=y, sample_weight=sample_weight)
         x, y, sample_weight = keras.utils.unpack_x_y_sample_weight(output)
diff --git a/keras_nlp/src/models/electra/electra_preprocessor.py b/keras_nlp/src/models/electra/electra_preprocessor.py
index 941e5d4d13..82dbb3310c 100644
--- a/keras_nlp/src/models/electra/electra_preprocessor.py
+++ b/keras_nlp/src/models/electra/electra_preprocessor.py
@@ -21,7 +21,7 @@
 from keras_nlp.src.models.electra.electra_backbone import ElectraBackbone
 from keras_nlp.src.models.electra.electra_tokenizer import ElectraTokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.ElectraPreprocessor")
@@ -142,7 +142,7 @@ def get_config(self):
         )
         return config
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(self, x, y=None, sample_weight=None):
         x = x if isinstance(x, tuple) else (x,)
         x = tuple(self.tokenizer(segment) for segment in x)
diff --git a/keras_nlp/src/models/f_net/f_net_masked_lm_preprocessor.py b/keras_nlp/src/models/f_net/f_net_masked_lm_preprocessor.py
index 7628f26ee4..3d0b625ef1 100644
--- a/keras_nlp/src/models/f_net/f_net_masked_lm_preprocessor.py
+++ b/keras_nlp/src/models/f_net/f_net_masked_lm_preprocessor.py
@@ -18,7 +18,7 @@
 from keras_nlp.src.models.f_net.f_net_backbone import FNetBackbone
 from keras_nlp.src.models.f_net.f_net_tokenizer import FNetTokenizer
 from keras_nlp.src.models.masked_lm_preprocessor import MaskedLMPreprocessor
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.FNetMaskedLMPreprocessor")
@@ -121,7 +121,7 @@ class FNetMaskedLMPreprocessor(MaskedLMPreprocessor):
     backbone_cls = FNetBackbone
     tokenizer_cls = FNetTokenizer
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(self, x, y=None, sample_weight=None):
         output = super().call(x, y=y, sample_weight=sample_weight)
         x, y, sample_weight = keras.utils.unpack_x_y_sample_weight(output)
diff --git a/keras_nlp/src/models/f_net/f_net_text_classifier_preprocessor.py b/keras_nlp/src/models/f_net/f_net_text_classifier_preprocessor.py
index c1f1f3f71e..124fc803e2 100644
--- a/keras_nlp/src/models/f_net/f_net_text_classifier_preprocessor.py
+++ b/keras_nlp/src/models/f_net/f_net_text_classifier_preprocessor.py
@@ -21,7 +21,7 @@
 from keras_nlp.src.models.text_classifier_preprocessor import (
     TextClassifierPreprocessor,
 )
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export(
@@ -124,7 +124,7 @@ class FNetTextClassifierPreprocessor(TextClassifierPreprocessor):
     backbone_cls = FNetBackbone
     tokenizer_cls = FNetTokenizer
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(self, x, y=None, sample_weight=None):
         # FNet has not padding mask.
         output = super().call(x, y=y, sample_weight=sample_weight)
diff --git a/keras_nlp/src/models/falcon/falcon_preprocessor.py b/keras_nlp/src/models/falcon/falcon_preprocessor.py
index 1c7fd3c138..491f6e5fe2 100644
--- a/keras_nlp/src/models/falcon/falcon_preprocessor.py
+++ b/keras_nlp/src/models/falcon/falcon_preprocessor.py
@@ -20,7 +20,7 @@
 from keras_nlp.src.models.falcon.falcon_backbone import FalconBackbone
 from keras_nlp.src.models.falcon.falcon_tokenizer import FalconTokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.FalconPreprocessor")
@@ -136,7 +136,7 @@ def build(self, input_shape):
         )
         self.built = True
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(
         self,
         x,
diff --git a/keras_nlp/src/models/gemma/gemma_preprocessor.py b/keras_nlp/src/models/gemma/gemma_preprocessor.py
index 745f437ecd..dcbe531b56 100644
--- a/keras_nlp/src/models/gemma/gemma_preprocessor.py
+++ b/keras_nlp/src/models/gemma/gemma_preprocessor.py
@@ -20,7 +20,7 @@
 from keras_nlp.src.models.gemma.gemma_backbone import GemmaBackbone
 from keras_nlp.src.models.gemma.gemma_tokenizer import GemmaTokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.GemmaPreprocessor")
@@ -151,7 +151,7 @@ def build(self, input_shape):
         )
         self.built = True
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(
         self,
         x,
diff --git a/keras_nlp/src/models/gpt2/gpt2_preprocessor.py b/keras_nlp/src/models/gpt2/gpt2_preprocessor.py
index b645ac7682..c9af92fcbf 100644
--- a/keras_nlp/src/models/gpt2/gpt2_preprocessor.py
+++ b/keras_nlp/src/models/gpt2/gpt2_preprocessor.py
@@ -20,7 +20,7 @@
 from keras_nlp.src.models.gpt2.gpt2_backbone import GPT2Backbone
 from keras_nlp.src.models.gpt2.gpt2_tokenizer import GPT2Tokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.GPT2Preprocessor")
@@ -136,7 +136,7 @@ def build(self, input_shape):
         )
         self.built = True
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(
         self,
         x,
diff --git a/keras_nlp/src/models/gpt_neo_x/gpt_neo_x_preprocessor.py b/keras_nlp/src/models/gpt_neo_x/gpt_neo_x_preprocessor.py
index 25875d03da..06a46ce470 100644
--- a/keras_nlp/src/models/gpt_neo_x/gpt_neo_x_preprocessor.py
+++ b/keras_nlp/src/models/gpt_neo_x/gpt_neo_x_preprocessor.py
@@ -19,7 +19,7 @@
 from keras_nlp.src.models.gpt_neo_x.gpt_neo_x_backbone import GPTNeoXBackbone
 from keras_nlp.src.models.gpt_neo_x.gpt_neo_x_tokenizer import GPTNeoXTokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.GPTNeoXPreprocessor")
@@ -94,7 +94,7 @@ def build(self, input_shape):
         )
         self.built = True
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(
         self,
         x,
diff --git a/keras_nlp/src/models/llama/llama_preprocessor.py b/keras_nlp/src/models/llama/llama_preprocessor.py
index 8b7c5772e6..75122856c6 100644
--- a/keras_nlp/src/models/llama/llama_preprocessor.py
+++ b/keras_nlp/src/models/llama/llama_preprocessor.py
@@ -18,7 +18,7 @@
 from keras_nlp.src.models.llama.llama_backbone import LlamaBackbone
 from keras_nlp.src.models.llama.llama_tokenizer import LlamaTokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.LlamaPreprocessor")
@@ -149,7 +149,7 @@ def get_config(self):
         )
         return config
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(
         self,
         x,
diff --git a/keras_nlp/src/models/masked_lm_preprocessor.py b/keras_nlp/src/models/masked_lm_preprocessor.py
index 491b715280..c4dab122ab 100644
--- a/keras_nlp/src/models/masked_lm_preprocessor.py
+++ b/keras_nlp/src/models/masked_lm_preprocessor.py
@@ -21,7 +21,7 @@
     MultiSegmentPacker,
 )
 from keras_nlp.src.models.preprocessor import Preprocessor
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.MaskedLMPreprocessor")
@@ -113,7 +113,7 @@ def build(self, input_shape):
             unselectable_token_ids=self.tokenizer.special_token_ids,
         )
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(self, x, y=None, sample_weight=None):
         x = x if isinstance(x, tuple) else (x,)
         x = tuple(self.tokenizer(segment) for segment in x)
diff --git a/keras_nlp/src/models/mistral/mistral_preprocessor.py b/keras_nlp/src/models/mistral/mistral_preprocessor.py
index 0278103c54..c6d7731722 100644
--- a/keras_nlp/src/models/mistral/mistral_preprocessor.py
+++ b/keras_nlp/src/models/mistral/mistral_preprocessor.py
@@ -19,7 +19,7 @@
 from keras_nlp.src.models.mistral.mistral_backbone import MistralBackbone
 from keras_nlp.src.models.mistral.mistral_tokenizer import MistralTokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.MistralPreprocessor")
@@ -150,7 +150,7 @@ def get_config(self):
         )
         return config
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(
         self,
         x,
diff --git a/keras_nlp/src/models/opt/opt_preprocessor.py b/keras_nlp/src/models/opt/opt_preprocessor.py
index 3b39c82cbc..0cafaaec5e 100644
--- a/keras_nlp/src/models/opt/opt_preprocessor.py
+++ b/keras_nlp/src/models/opt/opt_preprocessor.py
@@ -20,7 +20,7 @@
 from keras_nlp.src.models.opt.opt_backbone import OPTBackbone
 from keras_nlp.src.models.opt.opt_tokenizer import OPTTokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.OPTPreprocessor")
@@ -148,7 +148,7 @@ def get_config(self):
         )
         return config
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(
         self,
         x,
diff --git a/keras_nlp/src/models/pali_gemma/pali_gemma_backbone_test.py b/keras_nlp/src/models/pali_gemma/pali_gemma_backbone_test.py
index be541597cd..44d80b307d 100644
--- a/keras_nlp/src/models/pali_gemma/pali_gemma_backbone_test.py
+++ b/keras_nlp/src/models/pali_gemma/pali_gemma_backbone_test.py
@@ -11,19 +11,13 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-import os
-
 import numpy as np
+import pytest
+from keras import ops
 
 from keras_nlp.src.models.pali_gemma.pali_gemma_backbone import (
     PaliGemmaBackbone,
 )
-from keras_nlp.src.models.pali_gemma.pali_gemma_causal_lm_preprocessor import (
-    PaliGemmaCausalLMPreprocessor,
-)
-from keras_nlp.src.models.pali_gemma.pali_gemma_tokenizer import (
-    PaliGemmaTokenizer,
-)
 from keras_nlp.src.tests.test_case import TestCase
 
 
@@ -34,15 +28,6 @@ def setUp(self):
         self.text_sequence_length = 64
         self.image_size = 16
         self.image_sequence_length = int((self.image_size / 4) ** 2)
-
-        proto = "gemma_test_vocab.spm"
-        tokenizer = PaliGemmaTokenizer(
-            os.path.join(self.get_test_data_dir(), proto)
-        )
-        self.preprocessor = PaliGemmaCausalLMPreprocessor(
-            tokenizer, self.text_sequence_length, False, False
-        )
-
         self.init_kwargs = {
             "vocabulary_size": self.vocabulary_size,
             "image_size": self.image_size,
@@ -65,7 +50,6 @@ def setUp(self):
         dummy_text_token_ids = np.random.rand(
             self.batch_size, self.text_sequence_length
         )
-        dummy_text = ["answer en the quick brown fox"] * self.batch_size
         self.input_data = {
             "token_ids": dummy_text_token_ids,
             "images": dummy_images,
@@ -78,11 +62,6 @@ def setUp(self):
                 dtype="int32",
             ),
         }
-        self.raw_input_data = {
-            "images": dummy_images,
-            "prompts": dummy_text,
-            "responses": dummy_text,
-        }
 
     def test_backbone_basics(self):
         self.run_backbone_test(
@@ -98,15 +77,37 @@ def test_backbone_basics(self):
             run_mixed_precision_check=False,  # TODO: Set to `True`
         )
 
-    def test_pali_gemma_backbone_with_preprocessing(self):
-        model = PaliGemmaBackbone(**self.init_kwargs)
-        x, _, _ = self.preprocessor(self.raw_input_data)
-        output = model(x)
-        self.assertEqual(
-            (
-                self.batch_size,
-                self.text_sequence_length + self.image_sequence_length,
-                8,
+    @pytest.mark.large
+    def test_saved_model(self):
+        self.run_model_saving_test(
+            cls=PaliGemmaBackbone,
+            init_kwargs=self.init_kwargs,
+            input_data=self.input_data,
+        )
+
+    @pytest.mark.extra_large
+    def test_smallest_preset(self):
+        self.run_preset_test(
+            cls=PaliGemmaBackbone,
+            preset="pali_gemma_3b_mix_224",
+            input_data={
+                "token_ids": ops.array([[1169, 2068, 7586, 21831, 13]]),
+                "padding_mask": ops.ones((1, 5), dtype="int32"),
+                "response_mask": ops.zeros((1, 5), dtype="int32"),
+                "images": ops.zeros((1, 224, 224, 3), dtype="float32"),
+            },
+            expected_output_shape=(1, 261, 2048),
+            # The forward pass from a preset should be stable!
+            expected_partial_output=ops.array(
+                [-0.449851, 1.431027, -0.713446, 0.417485, -0.640859]
             ),
-            output.shape,
         )
+
+    @pytest.mark.extra_large
+    def test_all_presets(self):
+        for preset in PaliGemmaBackbone.presets:
+            self.run_preset_test(
+                cls=PaliGemmaBackbone,
+                preset=preset,
+                input_data=self.input_data,
+            )
diff --git a/keras_nlp/src/models/pali_gemma/pali_gemma_causal_lm_preprocessor.py b/keras_nlp/src/models/pali_gemma/pali_gemma_causal_lm_preprocessor.py
index 764b570fa2..01493ef454 100644
--- a/keras_nlp/src/models/pali_gemma/pali_gemma_causal_lm_preprocessor.py
+++ b/keras_nlp/src/models/pali_gemma/pali_gemma_causal_lm_preprocessor.py
@@ -17,22 +17,42 @@
 from keras_nlp.src.layers.preprocessing.multi_segment_packer import (
     MultiSegmentPacker,
 )
-from keras_nlp.src.models.gemma.gemma_causal_lm_preprocessor import (
-    GemmaCausalLMPreprocessor,
-)
+from keras_nlp.src.models.causal_lm_preprocessor import CausalLMPreprocessor
 from keras_nlp.src.models.pali_gemma.pali_gemma_backbone import (
     PaliGemmaBackbone,
 )
+from keras_nlp.src.models.pali_gemma.pali_gemma_image_converter import (
+    PaliGemmaImageConverter,
+)
 from keras_nlp.src.models.pali_gemma.pali_gemma_tokenizer import (
     PaliGemmaTokenizer,
 )
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.PaliGemmaCausalLMPreprocessor")
-class PaliGemmaCausalLMPreprocessor(GemmaCausalLMPreprocessor):
+class PaliGemmaCausalLMPreprocessor(CausalLMPreprocessor):
     backbone_cls = PaliGemmaBackbone
     tokenizer_cls = PaliGemmaTokenizer
+    image_converter_cls = PaliGemmaImageConverter
+
+    def __init__(
+        self,
+        tokenizer,
+        image_converter=None,
+        sequence_length=1024,
+        add_start_token=True,
+        add_end_token=True,
+        **kwargs,
+    ):
+        super().__init__(
+            tokenizer=tokenizer,
+            sequence_length=sequence_length,
+            add_start_token=add_start_token,
+            add_end_token=add_end_token,
+            **kwargs,
+        )
+        self.image_converter = image_converter
 
     def build(self, input_shape):
         # Defer packer creation to `build()` so that we can be sure tokenizer
@@ -46,7 +66,7 @@ def build(self, input_shape):
         )
         self.built = True
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(
         self,
         x,
@@ -58,6 +78,8 @@ def call(
         images, prompts, responses = x["images"], x["prompts"], x["responses"]
         prompts = self.tokenizer(prompts)
         responses = self.tokenizer(responses)
+        if self.image_converter:
+            images = self.image_converter(images)
         # Pad with one extra token to account for the truncation below.
         token_ids, segment_ids = self.packer(
             (prompts, responses),
@@ -80,7 +102,7 @@ def call(
         sample_weight = response_mask[..., 1:]
         return keras.utils.pack_x_y_sample_weight(x, y, sample_weight)
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def generate_preprocess(
         self,
         x,
@@ -103,6 +125,8 @@ def generate_preprocess(
 
         images, prompts = x["images"], x["prompts"]
         prompts = self.tokenizer(prompts)
+        if self.image_converter:
+            images = self.image_converter(images)
         if "responses" in x:
             responses = self.tokenizer(x["responses"])
             segments = (prompts, responses)
diff --git a/keras_nlp/src/models/pali_gemma/pali_gemma_causal_lm_preprocessor_test.py b/keras_nlp/src/models/pali_gemma/pali_gemma_causal_lm_preprocessor_test.py
index cba98a9d97..5553bcd71a 100644
--- a/keras_nlp/src/models/pali_gemma/pali_gemma_causal_lm_preprocessor_test.py
+++ b/keras_nlp/src/models/pali_gemma/pali_gemma_causal_lm_preprocessor_test.py
@@ -20,6 +20,9 @@
 from keras_nlp.src.models.pali_gemma.pali_gemma_causal_lm_preprocessor import (
     PaliGemmaCausalLMPreprocessor,
 )
+from keras_nlp.src.models.pali_gemma.pali_gemma_image_converter import (
+    PaliGemmaImageConverter,
+)
 from keras_nlp.src.models.pali_gemma.pali_gemma_tokenizer import (
     PaliGemmaTokenizer,
 )
@@ -34,14 +37,16 @@ def setUp(self):
                 self.get_test_data_dir(), "gemma_test_vocab.spm"
             ),
         )
+        self.image_converter = PaliGemmaImageConverter(width=224, height=224)
         self.init_kwargs = {
             "tokenizer": self.tokenizer,
+            "image_converter": self.image_converter,
             "sequence_length": 8,
         }
         self.input_data = {
             "prompts": ["the quick"],
             "responses": ["brown fox"],
-            "images": [np.zeros([1, 224, 224, 3])],
+            "images": [np.zeros([512, 512, 3])],
         }
 
     def test_preprocessor_basics(self):
@@ -54,7 +59,7 @@ def test_preprocessor_basics(self):
                     "token_ids": [[1, 4, 9, 5, 7, 2, 0, 0]],
                     "padding_mask": [[1, 1, 1, 1, 1, 1, 0, 0]],
                     "response_mask": [[0, 0, 0, 1, 1, 1, 0, 0]],
-                    "images": self.input_data["images"],
+                    "images": np.zeros([1, 224, 224, 3]),
                 },
                 [[4, 9, 5, 7, 2, 0, 0, 0]],  # Labels shifted.
                 [[0, 0, 1, 1, 1, 0, 0, 0]],  # Zero out unlabeled examples.
@@ -65,7 +70,7 @@ def test_no_start_end_token(self):
         input_data = {
             "prompts": ["the quick"] * 4,
             "responses": ["brown fox"] * 4,
-            "images": [np.zeros([1, 224, 224, 3])] * 4,
+            "images": [np.zeros([512, 512, 3])] * 4,
         }
         preprocessor = PaliGemmaCausalLMPreprocessor(
             **self.init_kwargs,
@@ -76,19 +81,21 @@ def test_no_start_end_token(self):
         self.assertAllEqual(x["token_ids"], [[4, 9, 5, 7, 0, 0, 0, 0]] * 4)
         self.assertAllEqual(x["padding_mask"], [[1, 1, 1, 1, 0, 0, 0, 0]] * 4)
         self.assertAllEqual(x["response_mask"], [[0, 0, 1, 1, 0, 0, 0, 0]] * 4)
+        self.assertAllEqual(x["images"], np.zeros([4, 224, 224, 3]))
         self.assertAllEqual(y, [[9, 5, 7, 0, 0, 0, 0, 0]] * 4)
         self.assertAllEqual(sw, [[0, 1, 1, 0, 0, 0, 0, 0]] * 4)
 
     def test_generate_preprocess(self):
         input_data = {
             "prompts": "the quick",
-            "images": np.zeros([1, 224, 224, 3]),
+            "images": np.zeros([1, 512, 512, 3]),
         }
         preprocessor = PaliGemmaCausalLMPreprocessor(**self.init_kwargs)
         x = preprocessor.generate_preprocess(input_data)
         self.assertAllEqual(x["token_ids"], [1, 4, 9, 0, 0, 0, 0, 0])
         self.assertAllEqual(x["padding_mask"], [1, 1, 1, 0, 0, 0, 0, 0])
         self.assertAllEqual(x["response_mask"], [0, 0, 0, 0, 0, 0, 0, 0])
+        self.assertAllEqual(x["images"], np.zeros([1, 224, 224, 3]))
 
     def test_generate_postprocess(self):
         input_data = {
diff --git a/keras_nlp/src/models/pali_gemma/pali_gemma_causal_lm_test.py b/keras_nlp/src/models/pali_gemma/pali_gemma_causal_lm_test.py
index 51afdc5fb7..3009cbf944 100644
--- a/keras_nlp/src/models/pali_gemma/pali_gemma_causal_lm_test.py
+++ b/keras_nlp/src/models/pali_gemma/pali_gemma_causal_lm_test.py
@@ -25,6 +25,9 @@
 from keras_nlp.src.models.pali_gemma.pali_gemma_causal_lm_preprocessor import (
     PaliGemmaCausalLMPreprocessor,
 )
+from keras_nlp.src.models.pali_gemma.pali_gemma_image_converter import (
+    PaliGemmaImageConverter,
+)
 from keras_nlp.src.models.pali_gemma.pali_gemma_tokenizer import (
     PaliGemmaTokenizer,
 )
@@ -39,17 +42,17 @@ def setUp(self):
         self.dummy_text = [
             "the quick brown fox" for _ in range(self.batch_size)
         ]
-        self.dummy_images = np.random.uniform(
-            size=(self.batch_size, self.image_size, self.image_size, 3)
-        )
+        self.dummy_images = np.random.uniform(size=(self.batch_size, 20, 20, 3))
 
         proto = "gemma_test_vocab.spm"
         tokenizer = PaliGemmaTokenizer(
             os.path.join(self.get_test_data_dir(), proto)
         )
+        image_converter = PaliGemmaImageConverter(16, 16)
         self.vocabulary_size = tokenizer.vocabulary_size()
         self.preprocessor = PaliGemmaCausalLMPreprocessor(
             tokenizer,
+            image_converter,
             self.text_sequence_length,
             add_start_token=False,
             add_end_token=False,
@@ -96,7 +99,9 @@ def test_saved_model(self):
             "token_ids": np.random.rand(
                 self.batch_size, self.text_sequence_length
             ),
-            "images": self.dummy_images,
+            "images": np.ones(
+                (self.batch_size, self.image_size, self.image_size, 3)
+            ),
             "padding_mask": np.ones(
                 (self.batch_size, self.text_sequence_length),
                 dtype="int32",
diff --git a/keras_nlp/api/utils/__init__.py b/keras_nlp/src/models/pali_gemma/pali_gemma_image_converter.py
similarity index 59%
rename from keras_nlp/api/utils/__init__.py
rename to keras_nlp/src/models/pali_gemma/pali_gemma_image_converter.py
index a904b48f59..6bef25d8b4 100644
--- a/keras_nlp/api/utils/__init__.py
+++ b/keras_nlp/src/models/pali_gemma/pali_gemma_image_converter.py
@@ -11,11 +11,15 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-"""DO NOT EDIT.
+from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.layers.preprocessing.resizing_image_converter import (
+    ResizingImageConverter,
+)
+from keras_nlp.src.models.pali_gemma.pali_gemma_backbone import (
+    PaliGemmaBackbone,
+)
 
-This file was autogenerated. Do not edit it by hand,
-since your modifications would be overwritten.
-"""
 
-from keras_nlp.src.utils.tensor_utils import convert_from_tf
-from keras_nlp.src.utils.tensor_utils import convert_to_tf
+@keras_nlp_export("keras_nlp.layers.PaliGemmaImageConverter")
+class PaliGemmaImageConverter(ResizingImageConverter):
+    backbone_cls = PaliGemmaBackbone
diff --git a/keras_nlp/src/models/pali_gemma/pali_gemma_presets.py b/keras_nlp/src/models/pali_gemma/pali_gemma_presets.py
index c8791f709b..8cabcc7038 100644
--- a/keras_nlp/src/models/pali_gemma/pali_gemma_presets.py
+++ b/keras_nlp/src/models/pali_gemma/pali_gemma_presets.py
@@ -25,7 +25,7 @@
             "path": "pali_gemma",
             "model_card": "https://www.kaggle.com/models/google/paligemma",
         },
-        "kaggle_handle": "kaggle://keras/paligemma/keras/pali_gemma_3b_mix_224/1",
+        "kaggle_handle": "kaggle://keras/paligemma/keras/pali_gemma_3b_mix_224/2",
     },
     "pali_gemma_3b_mix_448": {
         "metadata": {
@@ -37,7 +37,7 @@
             "path": "pali_gemma",
             "model_card": "https://www.kaggle.com/models/google/paligemma",
         },
-        "kaggle_handle": "kaggle://keras/paligemma/keras/pali_gemma_3b_mix_448/1",
+        "kaggle_handle": "kaggle://keras/paligemma/keras/pali_gemma_3b_mix_448/2",
     },
     "pali_gemma_3b_224": {
         "metadata": {
@@ -49,7 +49,7 @@
             "path": "pali_gemma",
             "model_card": "https://www.kaggle.com/models/google/paligemma",
         },
-        "kaggle_handle": "kaggle://keras/paligemma/keras/pali_gemma_3b_224/1",
+        "kaggle_handle": "kaggle://keras/paligemma/keras/pali_gemma_3b_224/2",
     },
     "pali_gemma_3b_448": {
         "metadata": {
@@ -61,7 +61,7 @@
             "path": "pali_gemma",
             "model_card": "https://www.kaggle.com/models/google/paligemma",
         },
-        "kaggle_handle": "kaggle://keras/paligemma/keras/pali_gemma_3b_448/1",
+        "kaggle_handle": "kaggle://keras/paligemma/keras/pali_gemma_3b_448/2",
     },
     "pali_gemma_3b_896": {
         "metadata": {
@@ -73,6 +73,6 @@
             "path": "pali_gemma",
             "model_card": "https://www.kaggle.com/models/google/paligemma",
         },
-        "kaggle_handle": "kaggle://keras/paligemma/keras/pali_gemma_3b_896/1",
+        "kaggle_handle": "kaggle://keras/paligemma/keras/pali_gemma_3b_896/2",
     },
 }
diff --git a/keras_nlp/src/models/phi3/phi3_preprocessor.py b/keras_nlp/src/models/phi3/phi3_preprocessor.py
index caa5c9eab4..ce392b5088 100644
--- a/keras_nlp/src/models/phi3/phi3_preprocessor.py
+++ b/keras_nlp/src/models/phi3/phi3_preprocessor.py
@@ -18,7 +18,7 @@
 from keras_nlp.src.models.phi3.phi3_backbone import Phi3Backbone
 from keras_nlp.src.models.phi3.phi3_tokenizer import Phi3Tokenizer
 from keras_nlp.src.models.preprocessor import Preprocessor
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.Phi3Preprocessor")
@@ -150,7 +150,7 @@ def get_config(self):
         )
         return config
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(
         self,
         x,
diff --git a/keras_nlp/src/models/preprocessor.py b/keras_nlp/src/models/preprocessor.py
index e498746685..fd24ea8b47 100644
--- a/keras_nlp/src/models/preprocessor.py
+++ b/keras_nlp/src/models/preprocessor.py
@@ -44,14 +44,18 @@ class Preprocessor(PreprocessingLayer):
 
     backbone_cls = None
     tokenizer_cls = None
+    audio_converter_cls = None
+    image_converter_cls = None
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self._tokenizer = None
+        self._image_converter = None
+        self._audio_converter = None
 
     def __setattr__(self, name, value):
         # Work around torch setattr for properties.
-        if name in ["tokenizer"]:
+        if name in ["tokenizer", "audio_converter", "image_converter"]:
             return object.__setattr__(self, name, value)
         return super().__setattr__(name, value)
 
@@ -64,15 +68,54 @@ def tokenizer(self):
     def tokenizer(self, value):
         self._tokenizer = value
 
+    @property
+    def audio_converter(self):
+        """The audio converter used to preprocess audio data."""
+        return self._audio_converter
+
+    @audio_converter.setter
+    def audio_converter(self, value):
+        self._audio_converter = value
+
+    @property
+    def image_converter(self):
+        """The image converter used to preprocess image data."""
+        return self._image_converter
+
+    @image_converter.setter
+    def image_converter(self, value):
+        self._image_converter = value
+
     def get_config(self):
         config = super().get_config()
-        config["tokenizer"] = keras.layers.serialize(self.tokenizer)
+        if self.tokenizer:
+            config["tokenizer"] = keras.layers.serialize(self.tokenizer)
+        if self.audio_converter:
+            config["audio_converter"] = keras.layers.serialize(
+                self.audio_converter
+            )
+        if self.image_converter:
+            config["image_converter"] = keras.layers.serialize(
+                self.image_converter
+            )
         return config
 
     @classmethod
     def from_config(cls, config):
         if "tokenizer" in config and isinstance(config["tokenizer"], dict):
             config["tokenizer"] = keras.layers.deserialize(config["tokenizer"])
+        if "audio_converter" in config and isinstance(
+            config["audio_converter"], dict
+        ):
+            config["audio_converter"] = keras.layers.deserialize(
+                config["audio_converter"]
+            )
+        if "image_converter" in config and isinstance(
+            config["image_converter"], dict
+        ):
+            config["image_converter"] = keras.layers.deserialize(
+                config["image_converter"]
+            )
         return cls(**config)
 
     @classproperty
@@ -95,7 +138,7 @@ def from_preset(
         """Instantiate a `keras_nlp.models.Preprocessor` from a model preset.
 
         A preset is a directory of configs, weights and other file assets used
-        to save and load a pre-trained model. The `preset` can be passed as a
+        to save and load a pre-trained model. The `preset` can be passed as
         one of:
 
         1. a built-in preset identifier like `'bert_base_en'`
@@ -155,4 +198,9 @@ def save_to_preset(self, preset_dir):
             preset_dir,
             config_file=PREPROCESSOR_CONFIG_FILE,
         )
-        self.tokenizer.save_to_preset(preset_dir)
+        if self.tokenizer:
+            self.tokenizer.save_to_preset(preset_dir)
+        if self.audio_converter:
+            self.audio_converter.save_to_preset(preset_dir)
+        if self.image_converter:
+            self.image_converter.save_to_preset(preset_dir)
diff --git a/keras_nlp/src/models/roberta/roberta_masked_lm_preprocessor.py b/keras_nlp/src/models/roberta/roberta_masked_lm_preprocessor.py
index 4b3bf02b6c..369a34ce61 100644
--- a/keras_nlp/src/models/roberta/roberta_masked_lm_preprocessor.py
+++ b/keras_nlp/src/models/roberta/roberta_masked_lm_preprocessor.py
@@ -21,7 +21,7 @@
 from keras_nlp.src.models.masked_lm_preprocessor import MaskedLMPreprocessor
 from keras_nlp.src.models.roberta.roberta_backbone import RobertaBackbone
 from keras_nlp.src.models.roberta.roberta_tokenizer import RobertaTokenizer
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.RobertaMaskedLMPreprocessor")
@@ -137,7 +137,7 @@ def build(self, input_shape):
             sequence_length=self.sequence_length,
         )
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(self, x, y=None, sample_weight=None):
         output = super().call(x, y=y, sample_weight=sample_weight)
         x, y, sample_weight = keras.utils.unpack_x_y_sample_weight(output)
diff --git a/keras_nlp/src/models/roberta/roberta_text_classifier_preprocessor.py b/keras_nlp/src/models/roberta/roberta_text_classifier_preprocessor.py
index 377cc2cb75..a905156615 100644
--- a/keras_nlp/src/models/roberta/roberta_text_classifier_preprocessor.py
+++ b/keras_nlp/src/models/roberta/roberta_text_classifier_preprocessor.py
@@ -23,7 +23,7 @@
 from keras_nlp.src.models.text_classifier_preprocessor import (
     TextClassifierPreprocessor,
 )
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export(
@@ -151,7 +151,7 @@ def build(self, input_shape):
         )
         self.built = True
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(self, x, y=None, sample_weight=None):
         output = super().call(x, y=y, sample_weight=sample_weight)
         x, y, sample_weight = keras.utils.unpack_x_y_sample_weight(output)
diff --git a/keras_nlp/src/models/seq_2_seq_lm_preprocessor.py b/keras_nlp/src/models/seq_2_seq_lm_preprocessor.py
index 8da697dc99..27405f99d0 100644
--- a/keras_nlp/src/models/seq_2_seq_lm_preprocessor.py
+++ b/keras_nlp/src/models/seq_2_seq_lm_preprocessor.py
@@ -16,8 +16,8 @@
 from keras_nlp.src.api_export import keras_nlp_export
 from keras_nlp.src.layers.preprocessing.start_end_packer import StartEndPacker
 from keras_nlp.src.models.preprocessor import Preprocessor
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 from keras_nlp.src.utils.tensor_utils import strip_to_ragged
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
 
 try:
     import tensorflow as tf
@@ -114,7 +114,7 @@ def build(self, input_shape):
         )
         self.built = True
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(
         self,
         x,
@@ -154,7 +154,7 @@ def call(
         sample_weight = decoder_padding_mask[..., 1:]
         return keras.utils.pack_x_y_sample_weight(x, y, sample_weight)
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def generate_preprocess(
         self,
         x,
@@ -215,7 +215,7 @@ def generate_preprocess(
             "decoder_padding_mask": decoder_padding_mask,
         }
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def generate_postprocess(
         self,
         x,
diff --git a/keras_nlp/src/models/task.py b/keras_nlp/src/models/task.py
index 919d025d1a..8da47992f1 100644
--- a/keras_nlp/src/models/task.py
+++ b/keras_nlp/src/models/task.py
@@ -152,7 +152,7 @@ def from_preset(
         """Instantiate a `keras_nlp.models.Task` from a model preset.
 
         A preset is a directory of configs, weights and other file assets used
-        to save and load a pre-trained model. The `preset` can be passed as a
+        to save and load a pre-trained model. The `preset` can be passed as
         one of:
 
         1. a built-in preset identifier like `'bert_base_en'`
diff --git a/keras_nlp/src/models/text_classifier_preprocessor.py b/keras_nlp/src/models/text_classifier_preprocessor.py
index 6b8639d045..9b34353938 100644
--- a/keras_nlp/src/models/text_classifier_preprocessor.py
+++ b/keras_nlp/src/models/text_classifier_preprocessor.py
@@ -18,7 +18,7 @@
     MultiSegmentPacker,
 )
 from keras_nlp.src.models.preprocessor import Preprocessor
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.TextClassifierPreprocessor")
@@ -104,7 +104,7 @@ def build(self, input_shape):
             sequence_length=self.sequence_length,
         )
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(self, x, y=None, sample_weight=None):
         x = x if isinstance(x, tuple) else (x,)
         x = tuple(self.tokenizer(segment) for segment in x)
diff --git a/keras_nlp/src/models/whisper/whisper_audio_feature_extractor.py b/keras_nlp/src/models/whisper/whisper_audio_converter.py
similarity index 92%
rename from keras_nlp/src/models/whisper/whisper_audio_feature_extractor.py
rename to keras_nlp/src/models/whisper/whisper_audio_converter.py
index 0fdc4374e0..ac15be457a 100644
--- a/keras_nlp/src/models/whisper/whisper_audio_feature_extractor.py
+++ b/keras_nlp/src/models/whisper/whisper_audio_converter.py
@@ -15,24 +15,19 @@
 
 import numpy as np
 
+from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.layers.preprocessing.audio_converter import AudioConverter
+from keras_nlp.src.models.whisper.whisper_backbone import WhisperBackbone
+
 try:
     import tensorflow as tf
 except ImportError:
-    raise ImportError(
-        "To use `keras_nlp`, please install Tensorflow: `pip install tensorflow`. "
-        "The TensorFlow package is required for data preprocessing with any backend."
-    )
+    tf = None
 
-from keras_nlp.src.api_export import keras_nlp_export
-from keras_nlp.src.layers.preprocessing.preprocessing_layer import (
-    PreprocessingLayer,
-)
 
-
-@keras_nlp_export("keras_nlp.models.WhisperAudioFeatureExtractor")
-class WhisperAudioFeatureExtractor(PreprocessingLayer):
-    """
-    Whisper audio feature extractor layer.
+@keras_nlp_export("keras_nlp.layers.WhisperAudioConverter")
+class WhisperAudioConverter(AudioConverter):
+    """Whisper audio converter layer.
 
     This layer takes in a batch of audio tensors, and computes the log-mel
     spectrogram features for each audio tensor.
@@ -55,22 +50,25 @@ class WhisperAudioFeatureExtractor(PreprocessingLayer):
             `max_audio_length * sampling_rate`. Defaults to `30`.
 
     Examples:
-
     ```python
     audio_tensor = tf.ones((8000,), dtype="float32")
 
     # Compute the log-mel spectrogram.
-    whisper_audio_feature_extractor = keras_nlp.models.WhisperAudioFeatureExtractor()
-    whisper_audio_feature_extractor(audio_tensor)
+    audio_converter = keras_nlp.models.WhisperAudioConverter.from_preset(
+        "whisper_base_en",
+    )
+    audio_converter(audio_tensor)
 
     # Compute the log-mel spectrogram for a batch of audio tensors.
     audio_tensor_1 = tf.ones((8000,), dtype="float32")
-    audio_tensor_2 = tf.ones((10000,), dtype="float32"
+    audio_tensor_2 = tf.ones((10000,), dtype="float32")
     audio_tensor = tf.ragged.stack([audio_tensor_1, audio_tensor_2], axis=0)
-    whisper_audio_feature_extractor(audio_tensor)
+    audio_converter(audio_tensor)
     ```
     """
 
+    backbone_cls = WhisperBackbone
+
     def __init__(
         self,
         num_mels=80,
diff --git a/keras_nlp/src/models/whisper/whisper_audio_feature_extractor_test.py b/keras_nlp/src/models/whisper/whisper_audio_converter_test.py
similarity index 84%
rename from keras_nlp/src/models/whisper/whisper_audio_feature_extractor_test.py
rename to keras_nlp/src/models/whisper/whisper_audio_converter_test.py
index 823acbf3d1..16923787f4 100644
--- a/keras_nlp/src/models/whisper/whisper_audio_feature_extractor_test.py
+++ b/keras_nlp/src/models/whisper/whisper_audio_converter_test.py
@@ -14,13 +14,13 @@
 
 import tensorflow as tf
 
-from keras_nlp.src.models.whisper.whisper_audio_feature_extractor import (
-    WhisperAudioFeatureExtractor,
+from keras_nlp.src.models.whisper.whisper_audio_converter import (
+    WhisperAudioConverter,
 )
 from keras_nlp.src.tests.test_case import TestCase
 
 
-class WhisperAudioFeatureExtractorTest(TestCase):
+class WhisperAudioConverterTest(TestCase):
     def setUp(self):
         self.init_kwargs = {
             "num_mels": 80,
@@ -38,14 +38,14 @@ def setUp(self):
 
     def test_feature_extractor_basics(self):
         self.run_preprocessing_layer_test(
-            cls=WhisperAudioFeatureExtractor,
+            cls=WhisperAudioConverter,
             init_kwargs=self.init_kwargs,
             input_data=self.input_data,
         )
 
     def test_correctness(self):
         audio_tensor = tf.ones((2,), dtype="float32")
-        outputs = WhisperAudioFeatureExtractor(**self.init_kwargs)(audio_tensor)
+        outputs = WhisperAudioConverter(**self.init_kwargs)(audio_tensor)
 
         # Verify shape.
         self.assertEqual(outputs.shape, (5, 80))
diff --git a/keras_nlp/src/models/whisper/whisper_backbone.py b/keras_nlp/src/models/whisper/whisper_backbone.py
index 5629e05f2d..e104494b2b 100644
--- a/keras_nlp/src/models/whisper/whisper_backbone.py
+++ b/keras_nlp/src/models/whisper/whisper_backbone.py
@@ -24,7 +24,6 @@
 from keras_nlp.src.models.backbone import Backbone
 from keras_nlp.src.models.whisper.whisper_decoder import WhisperDecoder
 from keras_nlp.src.models.whisper.whisper_encoder import WhisperEncoder
-from keras_nlp.src.utils.tensor_utils import assert_tf_backend
 
 
 def whisper_kernel_initializer(stddev=0.02):
@@ -117,8 +116,6 @@ def __init__(
         dtype=None,
         **kwargs,
     ):
-        assert_tf_backend(self.__class__.__name__)
-
         # === Layers ===
         self.encoder_conv_layer_1 = keras.layers.Conv1D(
             filters=hidden_dim,
diff --git a/keras_nlp/src/models/whisper/whisper_backbone_test.py b/keras_nlp/src/models/whisper/whisper_backbone_test.py
index 0b34d95cce..33bc0e2871 100644
--- a/keras_nlp/src/models/whisper/whisper_backbone_test.py
+++ b/keras_nlp/src/models/whisper/whisper_backbone_test.py
@@ -19,7 +19,6 @@
 from keras_nlp.src.tests.test_case import TestCase
 
 
-@pytest.mark.tf_only
 class WhisperBackboneTest(TestCase):
     def setUp(self):
         self.init_kwargs = {
diff --git a/keras_nlp/src/models/whisper/whisper_preprocessor.py b/keras_nlp/src/models/whisper/whisper_preprocessor.py
deleted file mode 100644
index 8a65e6d004..0000000000
--- a/keras_nlp/src/models/whisper/whisper_preprocessor.py
+++ /dev/null
@@ -1,315 +0,0 @@
-# Copyright 2024 The KerasNLP Authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     https://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import keras
-from absl import logging
-
-from keras_nlp.src.api_export import keras_nlp_export
-from keras_nlp.src.layers.preprocessing.start_end_packer import StartEndPacker
-from keras_nlp.src.models.preprocessor import Preprocessor
-from keras_nlp.src.models.whisper.whisper_audio_feature_extractor import (
-    WhisperAudioFeatureExtractor,
-)
-from keras_nlp.src.models.whisper.whisper_backbone import WhisperBackbone
-from keras_nlp.src.models.whisper.whisper_tokenizer import WhisperTokenizer
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
-
-
-@keras_nlp_export("keras_nlp.models.WhisperPreprocessor")
-class WhisperPreprocessor(Preprocessor):
-    """A Whisper preprocessing layer which handles audio and text input.
-
-    This preprocessing layer will do three things:
-
-     1. Compute the log-mel spectrogram of the audio tensor inputs using
-        `audio_feature_extractor`.
-     2. Tokenize decoder inputs using the `tokenizer`.
-     2. Add the appropriate special tokens - `"<|startoftranscript|>", task
-        token, language token, `"<|endoftext|>"`, etc.
-     3. Construct a dictionary with keys `"encoder_features"`,
-        `"decoder_token_ids"`, `"decoder_padding_mask"` that can be passed
-        directly to a Whisper model.
-
-    Args:
-        tokenizer: A `keras_nlp.models.WhisperTokenizer` instance.
-        audio_feature_extractor: A
-            `keras_nlp.models.WhisperAudioFeatureExtractor` instance or `None`.
-            If `None` a feature extractor with default parameters will be
-            created.
-        decoder_sequence_length: The length of the packed decoder inputs.
-        language: string, language token. Should only be passed if your
-            tokenizer is multilingual.
-        task: string, task name. One of `"transcribe"`, `"translate"`. Should
-            only be passed if your tokenizer is multilingual.
-        no_timestamps: bool. If True, `"<|no_timestamps|>"` will be added as a
-            special token to your input.
-
-    Call arguments:
-        x: A dictionary with `"encoder_audio"` and `"decoder_text"` as its keys.
-            `"encoder_audio"` should correspond to the input audio tensor.
-            `"decoder_text"` should be a tensor of single string sequences.
-            Inputs may be batched or unbatched. Raw python inputs will be
-            converted to tensors.
-        y: Any label data. Will be passed through unaltered.
-        sample_weight: Any label weight data. Will be passed through unaltered.
-
-    Examples:
-
-    Directly calling the layer on data.
-    ```python
-    preprocessor = keras_nlp.models.WhisperPreprocessor.from_preset(
-        "whisper_tiny_en",
-    )
-
-    # Preprocess unbatched inputs.
-    input_data = {
-        "encoder_audio": tf.ones((200,)),
-        "decoder_text": "The quick brown fox jumped.",
-    }
-    preprocessor(input_data)
-
-    # Preprocess batched inputs.
-    input_data = {
-        "encoder_audio": tf.ones((2, 200)),
-        "decoder_text": ["The quick brown fox jumped.", "Call me Ishmael."],
-    }
-    preprocessor(input_data)
-
-    # Custom audio feature extractor and vocabulary.
-    audio_feature_extractor = keras_nlp.models.WhisperAudioFeatureExtractor(
-        num_mels=80,
-        num_fft_bins=400,
-        stride=100,
-        sampling_rate=100,
-        max_audio_length=5,
-    )
-
-    features = ["a quick fox.", "a fox quick."]
-    vocab = {"<|endoftext|>": 0, "a": 4, "Ġquick": 5, "Ġfox": 6}
-    merges = ["Ġ q", "u i", "c k", "ui ck", "Ġq uick"]
-    merges += ["Ġ f", "o x", "Ġf ox"]
-    special_tokens = {
-        "<|startoftranscript|>": 9,
-        "<|endoftext|>": 10,
-        "<|notimestamps|>": 11,
-        "<|transcribe|>": 12,
-        "<|translate|>": 13,
-    }
-
-    tokenizer = keras_nlp.models.WhisperTokenizer(
-        vocabulary=vocab,
-        merges=merges,
-        special_tokens=special_tokens,
-    )
-    preprocessor = keras_nlp.models.WhisperPreprocessor(
-        audio_feature_extractor=audio_feature_extractor,
-        tokenizer=tokenizer,
-    )
-
-    input_data = {
-        "encoder_audio": tf.ones((200,)),
-        "decoder_text": "The quick brown fox jumped.",
-    }
-    preprocessor(input_data)
-    ```
-
-    Mapping with `tf.data.Dataset`.
-    ```python
-    preprocessor = keras_nlp.models.WhisperPreprocessor.from_preset(
-        "whisper_tiny_en")
-
-    # Map labeled single sentences.
-    features = {
-        "encoder_audio": tf.ones((2, 200)),
-        "decoder_text": ["The quick brown fox jumped.", "Call me Ishmael."],
-    }
-    labels = tf.constant(["True", "False"])
-    ds = tf.data.Dataset.from_tensor_slices((features, labels))
-    ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)
-
-    # Map unlabeled single sentences.
-    features = {
-        "encoder_audio": tf.ones((2, 200)),
-        "decoder_text": ["The quick brown fox jumped.", "Call me Ishmael."],
-    }
-    ds = tf.data.Dataset.from_tensor_slices(features)
-    ds = ds.map(preprocessor, num_parallel_calls=tf.data.AUTOTUNE)
-    ```
-    """
-
-    backbone_cls = WhisperBackbone
-    tokenizer_cls = WhisperTokenizer
-
-    def __init__(
-        self,
-        tokenizer,
-        audio_feature_extractor=None,
-        decoder_sequence_length=448,
-        language=None,
-        task=None,
-        no_timestamps=True,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        if audio_feature_extractor is None:
-            audio_feature_extractor = WhisperAudioFeatureExtractor()
-        self.audio_feature_extractor = audio_feature_extractor
-        self.tokenizer = tokenizer
-        self.decoder_packer = None
-        self.decoder_sequence_length = decoder_sequence_length
-        self.language = language
-        self.task = task
-        self.no_timestamps = no_timestamps
-
-    def build(self, input_shape):
-        # Defer packer creation to `build()` so that we can be sure tokenizer
-        # assets have loaded when restoring a saved model.
-
-        # Create list of tokens to be prepended to decoder inputs.
-        bos_tokens = [self.tokenizer.bos_token_id]
-        if self.tokenizer.language_tokens is not None:
-            if (
-                self.language is None
-                or self.language not in self.tokenizer.language_tokens
-            ):
-                raise ValueError(
-                    "You must pass a non-None value for `language` when using "
-                    "a multilingual tokenizer. The value must be one of "
-                    f'{",".join(self.tokenizer.language_tokens.keys())}. '
-                    f"Received: language={self.language}."
-                )
-            if self.task is None or self.task not in [
-                "transcribe",
-                "translate",
-            ]:
-                raise ValueError(
-                    "You must pass a non-None value for `task` when using "
-                    "a multilingual tokenizer. The value must be one of "
-                    '`"transcribe"`, `"translate"`. '
-                    f"Received: task={self.task}."
-                )
-
-            bos_tokens += [self.tokenizer.language_tokens[self.language]]
-
-            special_token_dict = self.tokenizer._special_token_dict
-            if self.task == "transcribe":
-                bos_tokens += [special_token_dict["<|transcribe|>"]]
-            elif self.task == "translate":
-                bos_tokens += [special_token_dict["<|translate|>"]]
-        else:
-            if self.language is not None:
-                logging.info(
-                    "`tokenizer` is monolingual, and `language` has a "
-                    "non-`None` value. Setting `language` to `None`."
-                )
-                self.language = None
-            if self.task is not None:
-                logging.info(
-                    "`tokenizer` is monolingual, and `task` has a "
-                    "non-`None` value. Setting `task` to `None`."
-                )
-                self.task = None
-
-        if self.no_timestamps:
-            bos_tokens += [self.tokenizer.no_timestamps_token_id]
-
-        # TODO: Use `MultiSegmentPacker` instead of `StartEndPacker` once we
-        # want to move to multi-segment packing and have improved
-        # `MultiSegmentPacker`'s performance.
-        self.decoder_packer = StartEndPacker(
-            start_value=bos_tokens,
-            end_value=self.tokenizer.eos_token_id,
-            pad_value=self.tokenizer.pad_token_id,
-            sequence_length=self.decoder_sequence_length,
-            return_padding_mask=True,
-        )
-
-    @tf_preprocessing_function
-    def call(self, x, y=None, sample_weight=None, decoder_sequence_length=None):
-        if not (
-            isinstance(x, dict)
-            and ["encoder_audio", "decoder_text"] == list(x.keys())
-        ):
-            raise ValueError(
-                '`x` must be a dictionary, containing the keys `"encoder_audio"`'
-                f' and `"decoder_text"`. Received x={x}.'
-            )
-
-        encoder_features = self.audio_feature_extractor(x["encoder_audio"])
-        decoder_sequence_length = (
-            decoder_sequence_length or self.decoder_sequence_length
-        )
-        decoder_inputs = self.tokenizer(x["decoder_text"])
-        decoder_token_ids, decoder_padding_mask = self.decoder_packer(
-            decoder_inputs,
-            sequence_length=decoder_sequence_length,
-        )
-
-        x = {
-            "encoder_features": encoder_features,
-            "decoder_token_ids": decoder_token_ids,
-            "decoder_padding_mask": decoder_padding_mask,
-        }
-
-        return keras.utils.pack_x_y_sample_weight(x, y, sample_weight)
-
-    def get_config(self):
-        config = super().get_config()
-        config.update(
-            {
-                "audio_feature_extractor": keras.layers.serialize(
-                    self.audio_feature_extractor
-                ),
-                "decoder_sequence_length": self.decoder_sequence_length,
-                "language": self.language,
-                "task": self.task,
-                "no_timestamps": self.no_timestamps,
-            }
-        )
-        return config
-
-    @classmethod
-    def from_config(cls, config):
-        if "tokenizer" in config and isinstance(config["tokenizer"], dict):
-            config["tokenizer"] = keras.layers.deserialize(config["tokenizer"])
-
-        if "audio_feature_extractor" in config and isinstance(
-            config["audio_feature_extractor"], dict
-        ):
-            config["audio_feature_extractor"] = keras.layers.deserialize(
-                config["audio_feature_extractor"]
-            )
-
-        return cls(**config)
-
-    @property
-    def decoder_sequence_length(self):
-        """The padded length of decoder input sequences."""
-        return self._decoder_sequence_length
-
-    @decoder_sequence_length.setter
-    def decoder_sequence_length(self, value):
-        self._decoder_sequence_length = value
-        if self.decoder_packer is not None:
-            self.decoder_packer.sequence_length = value
-
-    @property
-    def sequence_length(self):
-        """Alias for `decoder_sequence_length`."""
-        return self.decoder_sequence_length
-
-    @sequence_length.setter
-    def sequence_length(self, value):
-        self.decoder_sequence_length = value
diff --git a/keras_nlp/src/models/whisper/whisper_preprocessor_test.py b/keras_nlp/src/models/whisper/whisper_preprocessor_test.py
deleted file mode 100644
index 9721ddcad6..0000000000
--- a/keras_nlp/src/models/whisper/whisper_preprocessor_test.py
+++ /dev/null
@@ -1,85 +0,0 @@
-# Copyright 2024 The KerasNLP Authors
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     https://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import numpy as np
-
-from keras_nlp.src.models.whisper.whisper_audio_feature_extractor import (
-    WhisperAudioFeatureExtractor,
-)
-from keras_nlp.src.models.whisper.whisper_preprocessor import (
-    WhisperPreprocessor,
-)
-from keras_nlp.src.models.whisper.whisper_tokenizer import WhisperTokenizer
-from keras_nlp.src.tests.test_case import TestCase
-
-
-class WhisperPreprocessorTest(TestCase):
-    def setUp(self):
-        self.audio_feature_extractor = WhisperAudioFeatureExtractor(
-            num_mels=80,
-            num_fft_bins=400,
-            stride=100,
-            sampling_rate=100,
-            max_audio_length=5,
-        )
-        self.vocab = ["air", "Ġair", "plane", "Ġat", "port"]
-        self.vocab = dict([(token, i) for i, token in enumerate(self.vocab)])
-        self.merges = ["Ġ a", "Ġ t", "Ġ i", "Ġ b", "a i", "p l", "n e"]
-        self.merges += ["Ġa t", "p o", "r t", "Ġt h", "ai r", "pl a", "po rt"]
-        self.merges += ["Ġai r", "Ġa i", "pla ne"]
-        self.special_tokens = {
-            "<|startoftranscript|>": 9,
-            "<|endoftext|>": 10,
-            "<|notimestamps|>": 11,
-            "<|transcribe|>": 12,
-            "<|translate|>": 13,
-        }
-        self.language_tokens = {
-            "<|en|>": 14,
-            "<|fr|>": 15,
-        }
-        self.tokenizer = WhisperTokenizer(
-            vocabulary=self.vocab,
-            merges=self.merges,
-            special_tokens=self.special_tokens,
-            language_tokens=self.language_tokens,
-        )
-        self.init_kwargs = {
-            "audio_feature_extractor": self.audio_feature_extractor,
-            "tokenizer": self.tokenizer,
-            "decoder_sequence_length": 12,
-            "language": "<|en|>",
-            "task": "translate",
-        }
-        self.input_data = {
-            "encoder_audio": np.ones((2, 200)),
-            "decoder_text": [" airplane at airport", " airplane at"],
-        }
-
-    def test_feature_extractor_basics(self):
-        self.run_preprocessor_test(
-            cls=WhisperPreprocessor,
-            init_kwargs=self.init_kwargs,
-            input_data=self.input_data,
-            token_id_key="decoder_token_ids",
-        )
-
-    def test_sequence_length_override(self):
-        input_data = {
-            "encoder_audio": np.ones((200,)),
-            "decoder_text": " airplane at airport",
-        }
-        preprocessor = WhisperPreprocessor(**self.init_kwargs)
-        x = preprocessor(input_data, decoder_sequence_length=6)
-        self.assertAllEqual(x["decoder_token_ids"], [9, 14, 13, 11, 1, 10])
diff --git a/keras_nlp/src/models/whisper/whisper_presets.py b/keras_nlp/src/models/whisper/whisper_presets.py
index 6684ac793f..1a7844bbbc 100644
--- a/keras_nlp/src/models/whisper/whisper_presets.py
+++ b/keras_nlp/src/models/whisper/whisper_presets.py
@@ -25,7 +25,7 @@
             "path": "whisper",
             "model_card": "https://github.com/openai/whisper/blob/main/model-card.md",
         },
-        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_tiny_en/2",
+        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_tiny_en/3",
     },
     "whisper_base_en": {
         "metadata": {
@@ -38,7 +38,7 @@
             "path": "whisper",
             "model_card": "https://github.com/openai/whisper/blob/main/model-card.md",
         },
-        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_base_en/2",
+        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_base_en/3",
     },
     "whisper_small_en": {
         "metadata": {
@@ -51,7 +51,7 @@
             "path": "whisper",
             "model_card": "https://github.com/openai/whisper/blob/main/model-card.md",
         },
-        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_small_en/2",
+        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_small_en/3",
     },
     "whisper_medium_en": {
         "metadata": {
@@ -64,7 +64,7 @@
             "path": "whisper",
             "model_card": "https://github.com/openai/whisper/blob/main/model-card.md",
         },
-        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_medium_en/2",
+        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_medium_en/3",
     },
     "whisper_tiny_multi": {
         "metadata": {
@@ -77,7 +77,7 @@
             "path": "whisper",
             "model_card": "https://github.com/openai/whisper/blob/main/model-card.md",
         },
-        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_tiny_multi/2",
+        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_tiny_multi/3",
     },
     "whisper_base_multi": {
         "metadata": {
@@ -90,7 +90,7 @@
             "path": "whisper",
             "model_card": "https://github.com/openai/whisper/blob/main/model-card.md",
         },
-        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_base_multi/2",
+        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_base_multi/3",
     },
     "whisper_small_multi": {
         "metadata": {
@@ -103,7 +103,7 @@
             "path": "whisper",
             "model_card": "https://github.com/openai/whisper/blob/main/model-card.md",
         },
-        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_small_multi/2",
+        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_small_multi/3",
     },
     "whisper_medium_multi": {
         "metadata": {
@@ -116,7 +116,7 @@
             "path": "whisper",
             "model_card": "https://github.com/openai/whisper/blob/main/model-card.md",
         },
-        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_medium_multi/2",
+        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_medium_multi/3",
     },
     "whisper_large_multi": {
         "metadata": {
@@ -129,7 +129,7 @@
             "path": "whisper",
             "model_card": "https://github.com/openai/whisper/blob/main/model-card.md",
         },
-        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_large_multi/2",
+        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_large_multi/3",
     },
     "whisper_large_multi_v2": {
         "metadata": {
@@ -143,6 +143,6 @@
             "path": "whisper",
             "model_card": "https://github.com/openai/whisper/blob/main/model-card.md",
         },
-        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_large_multi_v2/2",
+        "kaggle_handle": "kaggle://keras/whisper/keras/whisper_large_multi_v2/3",
     },
 }
diff --git a/keras_nlp/src/models/xlm_roberta/xlm_roberta_masked_lm_preprocessor.py b/keras_nlp/src/models/xlm_roberta/xlm_roberta_masked_lm_preprocessor.py
index 8feb7d674f..d1f028aac4 100644
--- a/keras_nlp/src/models/xlm_roberta/xlm_roberta_masked_lm_preprocessor.py
+++ b/keras_nlp/src/models/xlm_roberta/xlm_roberta_masked_lm_preprocessor.py
@@ -25,7 +25,7 @@
 from keras_nlp.src.models.xlm_roberta.xlm_roberta_tokenizer import (
     XLMRobertaTokenizer,
 )
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export("keras_nlp.models.XLMRobertaMaskedLMPreprocessor")
@@ -140,7 +140,7 @@ def build(self, input_shape):
         )
         self.built = True
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(self, x, y=None, sample_weight=None):
         output = super().call(x, y=y, sample_weight=sample_weight)
         x, y, sample_weight = keras.utils.unpack_x_y_sample_weight(output)
diff --git a/keras_nlp/src/models/xlm_roberta/xlm_roberta_text_classifier_preprocessor.py b/keras_nlp/src/models/xlm_roberta/xlm_roberta_text_classifier_preprocessor.py
index 756b935dd0..52984e9ad7 100644
--- a/keras_nlp/src/models/xlm_roberta/xlm_roberta_text_classifier_preprocessor.py
+++ b/keras_nlp/src/models/xlm_roberta/xlm_roberta_text_classifier_preprocessor.py
@@ -28,7 +28,7 @@
 from keras_nlp.src.models.xlm_roberta.xlm_roberta_tokenizer import (
     XLMRobertaTokenizer,
 )
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export(
@@ -169,7 +169,7 @@ def build(self, input_shape):
         )
         self.built = True
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(self, x, y=None, sample_weight=None):
         output = super().call(x, y=y, sample_weight=sample_weight)
         x, y, sample_weight = keras.utils.unpack_x_y_sample_weight(output)
diff --git a/keras_nlp/src/tokenizers/byte_pair_tokenizer.py b/keras_nlp/src/tokenizers/byte_pair_tokenizer.py
index 26096f5ec4..5eecf4cbfc 100644
--- a/keras_nlp/src/tokenizers/byte_pair_tokenizer.py
+++ b/keras_nlp/src/tokenizers/byte_pair_tokenizer.py
@@ -31,7 +31,7 @@
 from keras_nlp.src.utils.tensor_utils import convert_to_ragged_batch
 from keras_nlp.src.utils.tensor_utils import is_int_dtype
 from keras_nlp.src.utils.tensor_utils import is_string_dtype
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 try:
     import tensorflow as tf
@@ -534,7 +534,7 @@ def _check_vocabulary(self):
                 "layer."
             )
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def tokenize(self, inputs):
         self._check_vocabulary()
         if self.add_prefix_space:
@@ -598,7 +598,7 @@ def process_unseen_tokens():
 
         return tokens
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def detokenize(self, inputs):
         self._check_vocabulary()
         inputs, unbatched, rectangular = convert_to_ragged_batch(inputs)
diff --git a/keras_nlp/src/tokenizers/byte_tokenizer.py b/keras_nlp/src/tokenizers/byte_tokenizer.py
index 594b2c2ffc..6b70df35ce 100644
--- a/keras_nlp/src/tokenizers/byte_tokenizer.py
+++ b/keras_nlp/src/tokenizers/byte_tokenizer.py
@@ -26,7 +26,7 @@
 from keras_nlp.src.tokenizers import tokenizer
 from keras_nlp.src.utils.tensor_utils import convert_to_ragged_batch
 from keras_nlp.src.utils.tensor_utils import is_int_dtype
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 try:
     import tensorflow_text as tf_text
@@ -212,7 +212,7 @@ def get_vocabulary(self):
             vocab[chr(i)] = i
         return vocab
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def tokenize(self, inputs):
         unbatched = inputs.shape.rank == 0
         if unbatched:
@@ -243,7 +243,7 @@ def tokenize(self, inputs):
             tokens = tf.squeeze(tokens, 0)
         return tokens
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def detokenize(self, inputs):
         inputs, unbatched, rectangular = convert_to_ragged_batch(inputs)
         # Remove trailing padding tokens, so that trailing "\x00" bytes don't
diff --git a/keras_nlp/src/tokenizers/sentence_piece_tokenizer.py b/keras_nlp/src/tokenizers/sentence_piece_tokenizer.py
index 37d609128b..ea9f4d6f4a 100644
--- a/keras_nlp/src/tokenizers/sentence_piece_tokenizer.py
+++ b/keras_nlp/src/tokenizers/sentence_piece_tokenizer.py
@@ -31,8 +31,8 @@
 from keras_nlp.src.utils.tensor_utils import convert_to_ragged_batch
 from keras_nlp.src.utils.tensor_utils import is_int_dtype
 from keras_nlp.src.utils.tensor_utils import is_string_dtype
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 from keras_nlp.src.utils.tensor_utils import tensor_to_list
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
 
 try:
     import tensorflow_text as tf_text
@@ -235,7 +235,7 @@ def _check_vocabulary(self):
                 "sure to pass a `proto` argument when creating the layer."
             )
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def tokenize(self, inputs):
         self._check_vocabulary()
         unbatched = inputs.shape.rank == 0
@@ -262,7 +262,7 @@ def tokenize(self, inputs):
             tf.ensure_shape(tokens, shape=[self.sequence_length])
         return tokens
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def detokenize(self, inputs):
         self._check_vocabulary()
         inputs, unbatched, rectangular = convert_to_ragged_batch(inputs)
diff --git a/keras_nlp/src/tokenizers/tokenizer.py b/keras_nlp/src/tokenizers/tokenizer.py
index f1304c8413..2f8a5c9549 100644
--- a/keras_nlp/src/tokenizers/tokenizer.py
+++ b/keras_nlp/src/tokenizers/tokenizer.py
@@ -27,7 +27,7 @@
 from keras_nlp.src.utils.preset_utils import save_serialized_object
 from keras_nlp.src.utils.preset_utils import save_tokenizer_assets
 from keras_nlp.src.utils.python_utils import classproperty
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 
 @keras_nlp_export(
@@ -201,7 +201,7 @@ def save_to_preset(self, preset_dir):
         )
         save_tokenizer_assets(self, preset_dir)
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def call(self, inputs, *args, training=None, **kwargs):
         return self.tokenize(inputs, *args, **kwargs)
 
@@ -231,7 +231,7 @@ def from_preset(
         """Instantiate a `keras_nlp.models.Tokenizer` from a model preset.
 
         A preset is a directory of configs, weights and other file assets used
-        to save and load a pre-trained model. The `preset` can be passed as a
+        to save and load a pre-trained model. The `preset` can be passed as
         one of:
 
         1. a built-in preset identifier like `'bert_base_en'`
diff --git a/keras_nlp/src/tokenizers/unicode_codepoint_tokenizer.py b/keras_nlp/src/tokenizers/unicode_codepoint_tokenizer.py
index 16115fa199..30bf31afc1 100644
--- a/keras_nlp/src/tokenizers/unicode_codepoint_tokenizer.py
+++ b/keras_nlp/src/tokenizers/unicode_codepoint_tokenizer.py
@@ -17,7 +17,7 @@
 from keras_nlp.src.tokenizers import tokenizer
 from keras_nlp.src.utils.tensor_utils import convert_to_ragged_batch
 from keras_nlp.src.utils.tensor_utils import is_int_dtype
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 try:
     import tensorflow as tf
@@ -284,7 +284,7 @@ def get_vocabulary(self):
             vocab[chr(i)] = i
         return vocab
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def tokenize(self, inputs):
         unbatched = inputs.shape.rank == 0
         if unbatched:
@@ -321,7 +321,7 @@ def tokenize(self, inputs):
 
         return tokens
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def detokenize(self, inputs):
         inputs, unbatched, rectangular = convert_to_ragged_batch(inputs)
         inputs = tf.ragged.boolean_mask(inputs, tf.not_equal(inputs, 0))
diff --git a/keras_nlp/src/tokenizers/word_piece_tokenizer.py b/keras_nlp/src/tokenizers/word_piece_tokenizer.py
index 8336ffe83b..f228afae2a 100644
--- a/keras_nlp/src/tokenizers/word_piece_tokenizer.py
+++ b/keras_nlp/src/tokenizers/word_piece_tokenizer.py
@@ -23,7 +23,7 @@
 from keras_nlp.src.utils.tensor_utils import convert_to_ragged_batch
 from keras_nlp.src.utils.tensor_utils import is_int_dtype
 from keras_nlp.src.utils.tensor_utils import is_string_dtype
-from keras_nlp.src.utils.tensor_utils import tf_preprocessing_function
+from keras_nlp.src.utils.tensor_utils import preprocessing_function
 
 try:
     import tensorflow as tf
@@ -470,7 +470,7 @@ def _check_vocabulary(self):
                 "to pass a `vocabulary` argument when creating the layer."
             )
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def tokenize(self, inputs):
         self._check_vocabulary()
         unbatched = inputs.shape.rank == 0
@@ -515,7 +515,7 @@ def tokenize(self, inputs):
 
         return tokens
 
-    @tf_preprocessing_function
+    @preprocessing_function
     def detokenize(self, inputs):
         self._check_vocabulary()
         inputs, unbatched, rectangular = convert_to_ragged_batch(inputs)
diff --git a/keras_nlp/src/utils/preset_utils.py b/keras_nlp/src/utils/preset_utils.py
index 146f64ce12..21feaec0ae 100644
--- a/keras_nlp/src/utils/preset_utils.py
+++ b/keras_nlp/src/utils/preset_utils.py
@@ -60,6 +60,8 @@
 # Config file names.
 CONFIG_FILE = "config.json"
 TOKENIZER_CONFIG_FILE = "tokenizer.json"
+AUDIO_CONVERTER_CONFIG_FILE = "audio_converter.json"
+IMAGE_CONVERTER_CONFIG_FILE = "image_converter.json"
 TASK_CONFIG_FILE = "task.json"
 PREPROCESSOR_CONFIG_FILE = "preprocessor.json"
 METADATA_FILE = "metadata.json"
@@ -656,6 +658,14 @@ def load_tokenizer(self, cls, **kwargs):
         """Load a tokenizer layer from the preset."""
         raise NotImplementedError
 
+    def load_audio_converter(self, cls, **kwargs):
+        """Load an audio converter layer from the preset."""
+        raise NotImplementedError
+
+    def load_image_converter(self, cls, **kwargs):
+        """Load an image converter layer from the preset."""
+        raise NotImplementedError
+
     def load_task(self, cls, load_weights, load_task_extras, **kwargs):
         """Load a task model from the preset.
 
@@ -683,8 +693,16 @@ def load_preprocessor(self, cls, load_task_extras, **kwargs):
         arguments. This allow us to support transformers checkpoints by
         only converting the backbone and tokenizer.
         """
-        if "tokenizer" not in kwargs:
+        if "tokenizer" not in kwargs and cls.tokenizer_cls:
             kwargs["tokenizer"] = self.load_tokenizer(cls.tokenizer_cls)
+        if "audio_converter" not in kwargs and cls.audio_converter_cls:
+            kwargs["audio_converter"] = self.load_audio_converter(
+                cls.audio_converter_cls
+            )
+        if "image_converter" not in kwargs and cls.image_converter_cls:
+            kwargs["image_converter"] = self.load_image_converter(
+                cls.image_converter_cls
+            )
         return cls(**kwargs)
 
 
@@ -705,6 +723,14 @@ def load_tokenizer(self, cls, **kwargs):
         tokenizer.load_preset_assets(self.preset)
         return tokenizer
 
+    def load_audio_converter(self, cls, **kwargs):
+        converter_config = load_json(self.preset, AUDIO_CONVERTER_CONFIG_FILE)
+        return load_serialized_object(converter_config, **kwargs)
+
+    def load_image_converter(self, cls, **kwargs):
+        converter_config = load_json(self.preset, IMAGE_CONVERTER_CONFIG_FILE)
+        return load_serialized_object(converter_config, **kwargs)
+
     def load_task(self, cls, load_weights, load_task_extras, **kwargs):
         # If there is no `task.json` or it's for the wrong class delegate to the
         # super class loader.
diff --git a/keras_nlp/src/utils/tensor_utils.py b/keras_nlp/src/utils/tensor_utils.py
index 4806400f2f..94594202ab 100644
--- a/keras_nlp/src/utils/tensor_utils.py
+++ b/keras_nlp/src/utils/tensor_utils.py
@@ -18,10 +18,9 @@
 import threading
 
 import keras
+import numpy as np
 from keras import ops
 
-from keras_nlp.src.api_export import keras_nlp_export
-
 try:
     import tensorflow as tf
     import tensorflow_text as tf_text
@@ -47,7 +46,7 @@ def in_no_convert_scope():
     return NO_CONVERT_COUNTER.count > 0
 
 
-def tf_preprocessing_function(fn):
+def preprocessing_function(fn):
     """Wraps a preprocessing function to handle tf tensor conversion."""
     if tf is None:
         return fn
@@ -59,33 +58,33 @@ def tf_preprocessing_function(fn):
 
             @functools.wraps(fn)
             def wrapper(self, x, **kwargs):
-                x = convert_to_tf(x)
+                x = convert_preprocessing_inputs(x)
                 with no_convert_scope():
                     x = fn(self, x, **kwargs)
-                return convert_from_tf(x)
+                return convert_preprocessing_outputs(x)
 
         else:
 
             @functools.wraps(fn)
             def wrapper(self, x, y=None, sample_weight=None, **kwargs):
-                x, y, sample_weight = convert_to_tf((x, y, sample_weight))
+                x, y, sample_weight = convert_preprocessing_inputs(
+                    (x, y, sample_weight)
+                )
                 with no_convert_scope():
                     x = fn(self, x, y=y, sample_weight=sample_weight, **kwargs)
-                return convert_from_tf(x)
+                return convert_preprocessing_outputs(x)
 
         return wrapper
 
 
-@keras_nlp_export("keras_nlp.utils.convert_to_tf")
-def convert_to_tf(x):
-    """Convert raw inputs to tf inputs for preprocessing.
+def convert_preprocessing_inputs(x):
+    """Convert raw inputs for preprocessing.
 
     This function is used to convert raw inputs (strings, lists, `np.ndarray`s,
-    `jax.Array`s, `torch.Tensor`s) to tensorflow inputs for use with `tf.data`
-    and KerasNLP preprocessing layers. It will convert ragged inputs and string
-    inputs `tf.RaggedTensor` and `tf.Tensor` types. This will automatically be
-    called when running preprocessing layers or `keras_nlp.models.Task`s with
-    preprocessing included.
+    `jax.Array`s, `torch.Tensor`s, etc) to a canonical format for
+    preprocessing layers. All inputs will be converted to backend tensors if
+    possible, except ragged inputs and string inputs which be converted to tf
+    tensors regardless of backend.
 
     `tuple` and `list` elements are handled differently by this function. A
     `tuple` is assumed to enumerate separate inputs, and a `list` is assumed to
@@ -97,11 +96,11 @@ def convert_to_tf(x):
     ```python
     # Two ragged arrays of token ids.
     x = ([[1, 2, 3], [4, 5]], [[1, 2], [3, 4, 5]])
-    keras_nlp.utils.convert_to_tf(x)
+    keras_nlp.utils.convert_preprocessing_inputs(x)
 
     # A batch of three samples each with two string segments.
     x = (["hi", "hello", "hey"], ["bye", "later", "so long"])
-    keras_nlp.utils.convert_to_tf(x)
+    keras_nlp.utils.convert_preprocessing_inputs(x)
 
     # A batch of features in a dictionary.
     x = {
@@ -109,31 +108,57 @@ def convert_to_tf(x):
         "images": np.ones((3, 64, 64, 3)),
         "labels": [1, 0, 1],
     }
-    keras_nlp.utils.convert_to_tf(x)
+    keras_nlp.utils.convert_preprocessing_inputs(x)
     ```
     """
+    if not tf.executing_eagerly() or in_no_convert_scope():
+        return x
+
     if isinstance(x, dict):
-        return {k: convert_to_tf(x[k]) for k, v in x.items()}
+        return {k: convert_preprocessing_inputs(x[k]) for k, v in x.items()}
     if isinstance(x, tuple):
-        return tuple(convert_to_tf(v) for v in x)
-    if isinstance(x, list):
-        return tf.ragged.constant(x)
+        return tuple(convert_preprocessing_inputs(v) for v in x)
     if isinstance(x, str):
         return tf.constant(x)
+    if isinstance(x, list):
+        try:
+            numpy_x = np.array(x)
+        except ValueError as e:
+            # If numpy conversion failed, try converting to a ragged array.
+            try:
+                return tf.ragged.constant(x)
+            except ValueError:
+                # If ragged conversion failed return to the numpy error.
+                raise e
+        # If we have a string input, use tf.tensor.
+        if numpy_x.dtype.type is np.str_:
+            return tf.convert_to_tensor(x)
+        # Numpy will default to int64, int32 works with more ops.
+        if numpy_x.dtype == np.int64:
+            numpy_x = numpy_x.astype(np.int32)
+        # We have non-ragged, non-string input. Use backbend type.
+        x = ops.convert_to_tensor(numpy_x)
+        # Torch will complain about device placement for GPU tensors.
+        if keras.config.backend() == "torch":
+            x = x.cpu()
+        return x
     if is_tensor_type(x):
+        # String or ragged types we keep as tf.
+        if isinstance(x, tf.RaggedTensor) or x.dtype == tf.string:
+            return x
+        # If we have a string input, use tf.tensor.
+        if isinstance(x, np.ndarray) and x.dtype.type is np.str_:
+            return tf.convert_to_tensor(x)
+        x = ops.convert_to_tensor(x)
         # Torch will complain about device placement for GPU tensors.
         if keras.config.backend() == "torch":
-            import torch
-
-            if isinstance(x, torch.Tensor):
-                x = x.cpu()
-        return tf.convert_to_tensor(x)
+            x = x.cpu()
+        return x
     return x
 
 
-@keras_nlp_export("keras_nlp.utils.convert_from_tf")
-def convert_from_tf(x):
-    """Convert tf outputs after preprocessing to a backend agnostic format.
+def convert_preprocessing_outputs(x):
+    """Convert outputs after preprocessing to a backend agnostic format.
 
     This function is used to convert `tf.Tensor` and `tf.RaggedTensor` output
     from preprocessing layers to either:
@@ -150,11 +175,11 @@ def convert_from_tf(x):
     ```python
     # Two ragged arrays of token ids.
     x = tf.ragged.constant([[1, 2, 3], [4, 5]])
-    keras_nlp.utils.convert_from_tf(x)
+    keras_nlp.utils.convert_preprocessing_outputs(x)
 
     # A batch of three samples each with two string segments.
     x = (tf.constant["hi", "yo", "hey"]), tf.constant(["bye", "ciao", ""]))
-    keras_nlp.utils.convert_from_tf(x)
+    keras_nlp.utils.convert_preprocessing_outputs(x)
 
     # A batch of features in a dictionary.
     x = {
@@ -162,7 +187,7 @@ def convert_from_tf(x):
         "images": tf.ones((3, 64, 64, 3)),
         "labels": tf.constant([1, 0, 1]),
     }
-    keras_nlp.utils.convert_from_tf(x)
+    keras_nlp.utils.convert_preprocessing_outputs(x)
     ```
     """
     if not tf.executing_eagerly() or in_no_convert_scope():
@@ -210,6 +235,8 @@ def tensor_to_list(inputs):
 
 def convert_to_ragged_batch(inputs):
     """Ensure a tf.Tensor is a ragged rank 2 tensor."""
+    if not isinstance(inputs, (tf.RaggedTensor, tf.Tensor)):
+        inputs = tf.convert_to_tensor(inputs)
     unbatched = inputs.shape.rank == 1
     rectangular = isinstance(inputs, tf.Tensor)
     if unbatched:
diff --git a/keras_nlp/src/utils/tensor_utils_test.py b/keras_nlp/src/utils/tensor_utils_test.py
index 478f05c9ad..c0f34595c2 100644
--- a/keras_nlp/src/utils/tensor_utils_test.py
+++ b/keras_nlp/src/utils/tensor_utils_test.py
@@ -19,45 +19,44 @@
 
 from keras_nlp.src.tests.test_case import TestCase
 from keras_nlp.src.utils.tensor_utils import any_equal
-from keras_nlp.src.utils.tensor_utils import convert_from_tf
+from keras_nlp.src.utils.tensor_utils import convert_preprocessing_inputs
+from keras_nlp.src.utils.tensor_utils import convert_preprocessing_outputs
 from keras_nlp.src.utils.tensor_utils import convert_to_ragged_batch
-from keras_nlp.src.utils.tensor_utils import convert_to_tf
 from keras_nlp.src.utils.tensor_utils import is_tensor_type
 from keras_nlp.src.utils.tensor_utils import tensor_to_list
 
 
-class ConvertTf(TestCase):
+class ConvertHelpers(TestCase):
     def test_basics(self):
         inputs = ops.array([1, 2, 3])
         # Convert to tf.
-        outputs = convert_to_tf(inputs)
-        self.assertIsInstance(outputs, tf.Tensor)
-        self.assertAllEqual(outputs, tf.constant(ops.convert_to_numpy(inputs)))
+        outputs = convert_preprocessing_inputs(inputs)
+        self.assertAllEqual(outputs, ops.array(inputs))
         # Convert from tf.
-        outputs = convert_from_tf(outputs)
+        outputs = convert_preprocessing_outputs(outputs)
         self.assertTrue(is_tensor_type(outputs))
         self.assertAllEqual(outputs, inputs)
 
     def test_strings(self):
         inputs = ["one", "two"]
         # Convert to tf.
-        outputs = convert_to_tf(inputs)
+        outputs = convert_preprocessing_inputs(inputs)
         self.assertIsInstance(outputs, tf.Tensor)
         self.assertAllEqual(outputs, tf.constant(inputs))
         # Convert from tf.
-        outputs = convert_from_tf(outputs)
+        outputs = convert_preprocessing_outputs(outputs)
         self.assertIsInstance(outputs, list)
         self.assertEqual(outputs, inputs)
 
     def test_ragged(self):
         inputs = [np.ones((1, 3)), np.ones((1, 2))]
         # Convert to tf.
-        outputs = convert_to_tf(inputs)
+        outputs = convert_preprocessing_inputs(inputs)
         self.assertIsInstance(outputs, tf.RaggedTensor)
         print(outputs, inputs)
         self.assertAllEqual(outputs, tf.ragged.constant(inputs))
         # Convert from tf.
-        outputs = convert_from_tf(outputs)
+        outputs = convert_preprocessing_outputs(outputs)
         self.assertIsInstance(outputs, list)
         self.assertEqual(outputs, [[[1, 1, 1]], [[1, 1]]])
 
@@ -72,14 +71,14 @@ def test_composite(self):
             [3, 4],
         )
 
-        outputs = convert_to_tf(inputs)
+        outputs = convert_preprocessing_inputs(inputs)
         self.assertIsInstance(outputs[0]["text"], tf.Tensor)
         self.assertIsInstance(outputs[0]["images"], tf.RaggedTensor)
         self.assertIsInstance(outputs[0]["ragged_ints"], tf.RaggedTensor)
-        self.assertIsInstance(outputs[1], tf.Tensor)
-        self.assertIsInstance(outputs[2], tf.Tensor)
+        self.assertTrue(is_tensor_type(outputs[1]))
+        self.assertTrue(is_tensor_type(outputs[2]))
 
-        outputs = convert_from_tf(outputs)
+        outputs = convert_preprocessing_outputs(outputs)
         self.assertIsInstance(outputs[0]["text"], list)
         self.assertIsInstance(outputs[0]["images"], list)
         self.assertIsInstance(outputs[0]["ragged_ints"], list)
diff --git a/keras_nlp/src/utils/transformers/preset_loader.py b/keras_nlp/src/utils/transformers/preset_loader.py
index 1a1ce928ba..7e93140979 100644
--- a/keras_nlp/src/utils/transformers/preset_loader.py
+++ b/keras_nlp/src/utils/transformers/preset_loader.py
@@ -71,3 +71,7 @@ def load_backbone(self, cls, load_weights, **kwargs):
 
     def load_tokenizer(self, cls, **kwargs):
         return self.converter.convert_tokenizer(cls, self.preset, **kwargs)
+
+    def load_image_converter(self, cls, **kwargs):
+        # TODO: set image size for pali gemma checkpoints.
+        return None