diff --git a/mediapipe/python/BUILD b/mediapipe/python/BUILD
index f56e5b3d4..9d5ea26ad 100644
--- a/mediapipe/python/BUILD
+++ b/mediapipe/python/BUILD
@@ -94,6 +94,7 @@ cc_library(
         "//mediapipe/tasks/cc/vision/image_embedder:image_embedder_graph",
         "//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph",
         "//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
+        "//mediapipe/tasks/cc/vision/face_stylizer:face_stylizer_graph",
     ] + select({
         # TODO: Build text_classifier_graph and text_embedder_graph on Windows.
         "//mediapipe:windows": [],
diff --git a/mediapipe/tasks/python/test/vision/BUILD b/mediapipe/tasks/python/test/vision/BUILD
index 48ecc30b3..19d592895 100644
--- a/mediapipe/tasks/python/test/vision/BUILD
+++ b/mediapipe/tasks/python/test/vision/BUILD
@@ -114,3 +114,20 @@ py_test(
         "@com_google_protobuf//:protobuf_python",
     ],
 )
+
+py_test(
+    name = "face_stylizer_test",
+    srcs = ["face_stylizer_test.py"],
+    data = [
+        "//mediapipe/tasks/testdata/vision:test_images",
+        "//mediapipe/tasks/testdata/vision:test_models",
+    ],
+    deps = [
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/test:test_utils",
+        "//mediapipe/tasks/python/vision:face_stylizer",
+        "//mediapipe/tasks/python/vision/core:image_processing_options",
+        "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+    ],
+)
diff --git a/mediapipe/tasks/python/test/vision/face_stylizer_test.py b/mediapipe/tasks/python/test/vision/face_stylizer_test.py
new file mode 100644
index 000000000..3c39851dd
--- /dev/null
+++ b/mediapipe/tasks/python/test/vision/face_stylizer_test.py
@@ -0,0 +1,118 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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
+#
+#     http://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.
+"""Tests for face stylizer."""
+
+import enum
+import os
+from unittest import mock
+
+import numpy as np
+from absl.testing import absltest
+from absl.testing import parameterized
+
+from mediapipe.python._framework_bindings import image as image_module
+from mediapipe.tasks.python.core import base_options as base_options_module
+from mediapipe.tasks.python.test import test_utils
+from mediapipe.tasks.python.vision import face_stylizer
+from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
+
+
+_BaseOptions = base_options_module.BaseOptions
+_Image = image_module.Image
+_FaceStylizer = face_stylizer.FaceStylizer
+_FaceStylizerOptions = face_stylizer.FaceStylizerOptions
+_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
+
+_MODEL = 'face_stylizer_model_placeholder.tflite'
+_IMAGE = 'cats_and_dogs.jpg'
+_STYLIZED_IMAGE = 'stylized_image_placeholder.jpg'
+_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
+
+
+class ModelFileType(enum.Enum):
+  FILE_CONTENT = 1
+  FILE_NAME = 2
+
+
+class FaceStylizerTest(parameterized.TestCase):
+
+  def setUp(self):
+    super().setUp()
+    self.test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(
+            os.path.join(_TEST_DATA_DIR, _IMAGE)))
+    self.model_path = test_utils.get_test_data_path(
+        os.path.join(_TEST_DATA_DIR, _MODEL))
+
+  def test_create_from_file_succeeds_with_valid_model_path(self):
+    # Creates with default option and valid model file successfully.
+    with _FaceStylizer.create_from_model_path(self.model_path) as stylizer:
+      self.assertIsInstance(stylizer, _FaceStylizer)
+
+  def test_create_from_options_succeeds_with_valid_model_path(self):
+    # Creates with options containing model file successfully.
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+    options = _FaceStylizerOptions(base_options=base_options)
+    with _FaceStylizer.create_from_options(options) as stylizer:
+      self.assertIsInstance(stylizer, _FaceStylizer)
+
+  def test_create_from_options_fails_with_invalid_model_path(self):
+    with self.assertRaisesRegex(
+        RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'):
+      base_options = _BaseOptions(
+          model_asset_path='/path/to/invalid/model.tflite')
+      options = _FaceStylizerOptions(base_options=base_options)
+      _FaceStylizer.create_from_options(options)
+
+  def test_create_from_options_succeeds_with_valid_model_content(self):
+    # Creates with options containing model content successfully.
+    with open(self.model_path, 'rb') as f:
+      base_options = _BaseOptions(model_asset_buffer=f.read())
+      options = _FaceStylizerOptions(base_options=base_options)
+      stylizer = _FaceStylizer.create_from_options(options)
+      self.assertIsInstance(stylizer, _FaceStylizer)
+
+  @parameterized.parameters(
+      (ModelFileType.FILE_NAME, _STYLIZED_IMAGE),
+      (ModelFileType.FILE_CONTENT, _STYLIZED_IMAGE))
+  def test_stylize(self, model_file_type, expected_detection_result_file):
+    # Creates stylizer.
+    if model_file_type is ModelFileType.FILE_NAME:
+      base_options = _BaseOptions(model_asset_path=self.model_path)
+    elif model_file_type is ModelFileType.FILE_CONTENT:
+      with open(self.model_path, 'rb') as f:
+        model_content = f.read()
+      base_options = _BaseOptions(model_asset_buffer=model_content)
+    else:
+      # Should never happen
+      raise ValueError('model_file_type is invalid.')
+
+    options = _FaceStylizerOptions(base_options=base_options)
+    stylizer = _FaceStylizer.create_from_options(options)
+
+    # Performs face stylization on the input.
+    stylized_image = stylizer.detect(self.test_image)
+    # Comparing results.
+    self.assertTrue(
+      np.array_equal(stylized_image.numpy_view(),
+                     self.test_image.numpy_view()))
+    # Closes the stylizer explicitly when the stylizer is not used in
+    # a context.
+    stylizer.close()
+
+
+if __name__ == '__main__':
+  absltest.main()
diff --git a/mediapipe/tasks/python/vision/BUILD b/mediapipe/tasks/python/vision/BUILD
index eda8e290d..8ce0ef96e 100644
--- a/mediapipe/tasks/python/vision/BUILD
+++ b/mediapipe/tasks/python/vision/BUILD
@@ -152,3 +152,22 @@ py_library(
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
     ],
 )
+
+py_library(
+    name = "face_stylizer",
+    srcs = [
+        "face_stylizer.py",
+    ],
+    deps = [
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/python:packet_creator",
+        "//mediapipe/python:packet_getter",
+        "//mediapipe/tasks/cc/vision/image_segmenter/proto:face_stylizer_graph_options_py_pb2",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+        "//mediapipe/tasks/python/core:task_info",
+        "//mediapipe/tasks/python/vision/core:base_vision_task_api",
+        "//mediapipe/tasks/python/vision/core:image_processing_options",
+        "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+    ],
+)
diff --git a/mediapipe/tasks/python/vision/face_stylizer.py b/mediapipe/tasks/python/vision/face_stylizer.py
new file mode 100644
index 000000000..cd840fe85
--- /dev/null
+++ b/mediapipe/tasks/python/vision/face_stylizer.py
@@ -0,0 +1,254 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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
+#
+#     http://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.
+"""MediaPipe face stylizer  task."""
+
+import dataclasses
+from typing import Callable, Mapping, Optional
+
+from mediapipe.python import packet_creator
+from mediapipe.python import packet_getter
+from mediapipe.python._framework_bindings import image as image_module
+from mediapipe.python._framework_bindings import packet as packet_module
+from mediapipe.tasks.cc.vision.face_stylizer.proto import face_stylizer_graph_options_pb2
+from mediapipe.tasks.python.core import base_options as base_options_module
+from mediapipe.tasks.python.core import task_info as task_info_module
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+from mediapipe.tasks.python.vision.core import base_vision_task_api
+from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
+from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+
+_BaseOptions = base_options_module.BaseOptions
+_FaceStylizerGraphOptionsProto = face_stylizer_graph_options_pb2.FaceStylizerGraphOptions
+_RunningMode = running_mode_module.VisionTaskRunningMode
+_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
+_TaskInfo = task_info_module.TaskInfo
+
+_STYLIZED_IMAGE_NAME = 'stylized_image'
+_STYLIZED_IMAGE_TAG = 'STYLIZED_IMAGE'
+_NORM_RECT_STREAM_NAME = 'norm_rect_in'
+_NORM_RECT_TAG = 'NORM_RECT'
+_IMAGE_IN_STREAM_NAME = 'image_in'
+_IMAGE_OUT_STREAM_NAME = 'image_out'
+_IMAGE_TAG = 'IMAGE'
+_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.face_stylizer.FaceStylizerGraph'
+_MICRO_SECONDS_PER_MILLISECOND = 1000
+
+
+@dataclasses.dataclass
+class FaceStylizerOptions:
+  """Options for the face stylizer task.
+
+  Attributes:
+    base_options: Base options for the face stylizer task.
+    running_mode: The running mode of the task. Default to the image mode.
+      Face stylizer task has three running modes:
+      1) The image mode for stylizing faces on single image inputs.
+      2) The video mode for stylizing faces on the decoded frames of a video.
+      3) The live stream mode for stylizing faces on a live stream of input
+         data, such as from camera.
+    result_callback: The user-defined result callback for processing live stream
+      data. The result callback should only be specified when the running mode
+      is set to the live stream mode.
+  """
+  base_options: _BaseOptions
+  running_mode: _RunningMode = _RunningMode.IMAGE
+  result_callback: Optional[
+      Callable[[image_module.Image, image_module.Image, int],
+               None]] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _FaceStylizerGraphOptionsProto:
+    """Generates an FaceStylizerOptions protobuf object."""
+    base_options_proto = self.base_options.to_pb2()
+    base_options_proto.use_stream_mode = False if self.running_mode == _RunningMode.IMAGE else True
+    return _FaceStylizerGraphOptionsProto(base_options=base_options_proto)
+
+
+class FaceStylizer(base_vision_task_api.BaseVisionTaskApi):
+  """Class that performs face stylization on images."""
+
+  @classmethod
+  def create_from_model_path(cls, model_path: str) -> 'FaceStylizer':
+    """Creates an `FaceStylizer` object from a TensorFlow Lite model and the default `FaceStylizerOptions`.
+
+    Note that the created `FaceDetector` instance is in image mode, for
+    stylizing faces on single image inputs.
+
+    Args:
+      model_path: Path to the model.
+
+    Returns:
+      `FaceStylizer` object that's created from the model file and the default
+      `FaceStylizerOptions`.
+
+    Raises:
+      ValueError: If failed to create `FaceStylizer` object from the provided
+        file such as invalid file path.
+      RuntimeError: If other types of error occurred.
+    """
+    base_options = _BaseOptions(model_asset_path=model_path)
+    options = FaceStylizerOptions(
+        base_options=base_options, running_mode=_RunningMode.IMAGE)
+    return cls.create_from_options(options)
+
+  @classmethod
+  def create_from_options(cls,
+                          options: FaceStylizerOptions) -> 'FaceStylizer':
+    """Creates the `FaceStylizer` object from face stylizer options.
+
+    Args:
+      options: Options for the face stylizer task.
+
+    Returns:
+      `FaceStylizer` object that's created from `options`.
+
+    Raises:
+      ValueError: If failed to create `FaceStylizer` object from
+        `FaceStylizerOptions` such as missing the model.
+      RuntimeError: If other types of error occurred.
+    """
+
+    def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
+      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
+        return
+      image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
+      stylized_image_packet = output_packets[_STYLIZED_IMAGE_NAME]
+      options.result_callback(
+        stylized_image_packet, image,
+        stylized_image_packet.timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
+
+    task_info = _TaskInfo(
+        task_graph=_TASK_GRAPH_NAME,
+        input_streams=[
+            ':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME]),
+            ':'.join([_NORM_RECT_TAG, _NORM_RECT_STREAM_NAME]),
+        ],
+        output_streams=[
+            ':'.join([_STYLIZED_IMAGE_TAG, _STYLIZED_IMAGE_NAME]),
+            ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME])
+        ],
+        task_options=options)
+    return cls(
+        task_info.generate_graph_config(
+            enable_flow_limiting=options.running_mode ==
+            _RunningMode.LIVE_STREAM), options.running_mode,
+        packets_callback if options.result_callback else None)
+
+  def stylize(
+      self,
+      image: image_module.Image,
+      image_processing_options: Optional[_ImageProcessingOptions] = None
+  ) -> image_module.Image:
+    """Performs face stylization on the provided MediaPipe Image.
+
+    Only use this method when the FaceStylizer is created with the image
+    running mode.
+
+    Args:
+      image: MediaPipe Image.
+      image_processing_options: Options for image processing.
+
+    Returns:
+      The stylized image.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If face stylization failed to run.
+    """
+    normalized_rect = self.convert_to_normalized_rect(image_processing_options)
+    output_packets = self._process_image_data({
+        _IMAGE_IN_STREAM_NAME:
+            packet_creator.create_image(image),
+        _NORM_RECT_STREAM_NAME:
+            packet_creator.create_proto(normalized_rect.to_pb2())
+    })
+    return output_packets[_STYLIZED_IMAGE_NAME]
+
+  def stylize_for_video(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None
+  ) -> image_module.Image:
+    """Performs face stylization on the provided video frames.
+
+    Only use this method when the FaceStylizer is created with the video
+    running mode. It's required to provide the video frame's timestamp (in
+    milliseconds) along with the video frame. The input timestamps should be
+    monotonically increasing for adjacent calls of this method.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input video frame in milliseconds.
+      image_processing_options: Options for image processing.
+
+    Returns:
+      The stylized image.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If face stylization failed to run.
+    """
+    normalized_rect = self.convert_to_normalized_rect(image_processing_options)
+    output_packets = self._process_video_data({
+        _IMAGE_IN_STREAM_NAME:
+            packet_creator.create_image(image).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
+        _NORM_RECT_STREAM_NAME:
+            packet_creator.create_proto(normalized_rect.to_pb2()).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
+    })
+    return output_packets[_STYLIZED_IMAGE_NAME]
+
+  def stylize_async(
+      self,
+      image: image_module.Image,
+      timestamp_ms: int,
+      image_processing_options: Optional[_ImageProcessingOptions] = None
+  ) -> None:
+    """Sends live image data (an Image with a unique timestamp) to perform face stylization.
+
+    Only use this method when the FaceStylizer is created with the live stream
+    running mode. The input timestamps should be monotonically increasing for
+    adjacent calls of this method. This method will return immediately after the
+    input image is accepted. The results will be available via the
+    `result_callback` provided in the `FaceStylizerOptions`. The
+    `stylize_async` method is designed to process live stream data such as camera
+    input. To lower the overall latency, face stylizer may drop the input
+    images if needed. In other words, it's not guaranteed to have output per
+    input image.
+
+    The `result_callback` provides:
+      - The stylized image.
+      - The input image that the face stylizer runs on.
+      - The input timestamp in milliseconds.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input image in milliseconds.
+      image_processing_options: Options for image processing.
+
+    Raises:
+      ValueError: If the current input timestamp is smaller than what the face
+        stylizer has already processed.
+    """
+    normalized_rect = self.convert_to_normalized_rect(image_processing_options)
+    self._send_live_stream_data({
+        _IMAGE_IN_STREAM_NAME:
+            packet_creator.create_image(image).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND),
+        _NORM_RECT_STREAM_NAME:
+            packet_creator.create_proto(normalized_rect.to_pb2()).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
+    })