diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml
index d1f5aefc..f6725c4f 100644
--- a/.github/workflows/python-app.yml
+++ b/.github/workflows/python-app.yml
@@ -43,7 +43,7 @@ jobs:
       run: |
         curl -Os https://cli.codecov.io/latest/linux/codecov
         chmod +x codecov
-        ./codecov --verbose upload-process --fail-on-error -t ${{ secrets.CODECOV_TOKEN }} -n 'service'-${{ github.run_id }} -F service -f coverage.xml
+        ./codecov --verbose upload-process --fail-on-error -n 'service'-${{ github.run_id }} -F service -f coverage.xml
 
   test:
     needs: build
diff --git a/tensorboardX/summary.py b/tensorboardX/summary.py
index 549a03aa..f46c5529 100644
--- a/tensorboardX/summary.py
+++ b/tensorboardX/summary.py
@@ -1,4 +1,3 @@
-
 import logging
 import os
 import re as _re
@@ -368,33 +367,56 @@ def video(tag, tensor, fps=4, dataformats="NTCHW"):
 
 
 def make_video(tensor, fps):
+    import tempfile
+    from importlib.metadata import PackageNotFoundError
+    from importlib.metadata import version as get_version
+
+    from packaging.version import Version
     try:
-        import moviepy  # noqa: F401
-    except ImportError:
-        print('add_video needs package moviepy')
+        moviepy_version = Version(get_version("moviepy"))
+    except PackageNotFoundError:
+        logger.error("moviepy is not installed.")
         return
+
     try:
-        from moviepy import editor as mpy
+        # moviepy v2+
+        from moviepy import ImageSequenceClip
     except ImportError:
-        print("moviepy is installed, but can't import moviepy.editor.",
-              "Some packages could be missing [imageio, requests]")
-        return
-
-    import tempfile
-
-    import moviepy.version
-
+        try:
+            # Fallback for all moviepy versions
+            from moviepy.video.io.ImageSequenceClip import ImageSequenceClip
+        except ImportError as e:
+            logger.error(
+                "Can't create video. moviepy is installed, but can't import moviepy.video.io.ImageSequenceClip due to %r",
+                e,
+            )
+            return
+
+    # Warn about potential moviepy and imageio version incompatibility
+    imageio_version = Version(get_version("imageio"))
+    if moviepy_version >= Version("2") and imageio_version < Version("2.29"):
+        logger.error(
+            "You are using moviepy >= 2.0.0 and imageio < 2.29.0. "
+            "This combination is known to cause issues when writing videos. "
+            "Please upgrade imageio to 2.29 or later, or use moviepy < 2.0.0."
+        )
     t, h, w, c = tensor.shape
 
+    # Convert to RGB if moviepy v2/imageio>2.27 is used; 1-channel input is not supported.
+    if c == 1 and (
+        moviepy_version >= Version("2")
+        or imageio_version > Version("2.27")
+    ):
+        tensor = np.repeat(tensor, 3, axis=-1)
     # encode sequence of images into gif string
-    clip = mpy.ImageSequenceClip(list(tensor), fps=fps)
+    clip = ImageSequenceClip(list(tensor), fps=fps)
     with tempfile.NamedTemporaryFile(suffix='.gif', delete=False) as fp:
         filename = fp.name
 
-        if moviepy.version.__version__.startswith("0."):
+        if moviepy_version < Version("1.0.0"):
             logger.warning('Upgrade to moviepy >= 1.0.0 to supress the progress bar.')
             clip.write_gif(filename, verbose=False)
-        elif moviepy.version.__version__.startswith("1."):
+        elif moviepy_version < Version("2.0.0dev1"):
             # moviepy >= 1.0.0 use logger=None to suppress output.
             clip.write_gif(filename, verbose=False, logger=None)
         else:
diff --git a/tests/expect/test_summary.test_video.expect.gif b/tests/expect/test_summary.test_video.expect.gif
new file mode 100644
index 00000000..0ba79c49
Binary files /dev/null and b/tests/expect/test_summary.test_video.expect.gif differ
diff --git a/tests/test_summary.py b/tests/test_summary.py
index 950f9bec..69a485e8 100644
--- a/tests/test_summary.py
+++ b/tests/test_summary.py
@@ -1,10 +1,14 @@
 from __future__ import absolute_import, division, print_function, unicode_literals
+
+import io
 from tensorboardX import summary
 from .expect_reader import compare_proto, write_proto
 import numpy as np
 import pytest
 import unittest
 import torch
+
+from PIL import Image, ImageSequence
 # compare_proto = write_proto  # massive update expect
 
 def tensor_N(shape, dtype=float):
@@ -76,12 +80,40 @@ def test_image_with_four_channel_batched(self):
     def test_image_without_channel(self):
         compare_proto(summary.image('dummy', tensor_N(shape=(8, 8)), dataformats='HW'), self)
 
+    @staticmethod
+    def _iter_gif(encoded_image):
+        image_io = io.BytesIO(encoded_image)
+        im = Image.open(image_io, )
+        for frame in ImageSequence.Iterator(im):
+            yield frame.getchannel(0)
+
+    @staticmethod
+    def _load_expected_test_video():
+        with Image.open("tests/expect/test_summary.test_video.expect.gif") as im:
+            return list(ImageSequence.Iterator(im))
+
+    def assert_grayscale(self, image) -> None:
+        channels = image.split()
+        c0colors = channels[0].getcolors()
+        for c in channels[1:]:
+            self.assertEqual(c0colors, c.getcolors())
+
     def test_video(self):
         try:
             import moviepy
         except ImportError:
-            return
-        compare_proto(summary.video('dummy', tensor_N(shape=(4, 3, 1, 8, 8))), self)
+            self.skipTest('moviepy not installed')
+        t1 = tensor_N(shape=(4, 3, 1, 8, 8))
+        v1 = summary.video("dummy", t1)
+        frames = list(self._iter_gif(v1.value[0].image.encoded_image_string))
+        self.assertEqual(len(frames), 3)
+        prepared = self._load_expected_test_video()
+        for image, expected in zip(frames, prepared):
+            self.assert_grayscale(image)
+            self.assert_grayscale(expected)
+            self.assertEqual(
+                image.getchannel(0).getcolors(), expected.getchannel(0).getcolors()
+            )
         summary.video('dummy', tensor_N(shape=(16, 48, 1, 28, 28)))
         summary.video('dummy', tensor_N(shape=(20, 7, 1, 8, 8)))