diff --git a/clearml/binding/frameworks/tensorflow_bind.py b/clearml/binding/frameworks/tensorflow_bind.py
index a3d15d16..3b94f943 100644
--- a/clearml/binding/frameworks/tensorflow_bind.py
+++ b/clearml/binding/frameworks/tensorflow_bind.py
@@ -6,7 +6,8 @@ from collections import defaultdict
 from functools import partial
 from io import BytesIO
 from mimetypes import guess_extension
-from typing import Any
+from tempfile import mkstemp
+from typing import Any, Union
 
 import numpy as np
 import six
@@ -23,6 +24,11 @@ try:
 except ImportError:
     MessageToDict = None
 
+try:
+    from PIL import GifImagePlugin  # noqa
+except ImportError:
+    pass
+
 
 class TensorflowBinding(object):
     @classmethod
@@ -360,6 +366,17 @@ class EventTrainsWriter(object):
                 imdata = base64.b64decode(img_str)
             output = BytesIO(imdata)
             im = Image.open(output)
+            # if this is a GIF store as is
+            if getattr(im, 'is_animated'):
+                output.close()
+                fd, temp_file = mkstemp(
+                    suffix=guess_extension(im.get_format_mimetype()) if hasattr(im, 'get_format_mimetype')
+                    else ".{}".format(str(im.format).lower())
+                )
+                os.write(fd, imdata)
+                os.close(fd)
+                return temp_file
+
             image = np.asarray(im)
             output.close()
             if height is not None and height > 0 and width is not None and width > 0:
@@ -389,7 +406,7 @@ class EventTrainsWriter(object):
         return val
 
     def _add_image_numpy(self, tag, step, img_data_np, max_keep_images=None):
-        # type: (str, int, np.ndarray, int) -> ()
+        # type: (str, int, Union[None, np.ndarray, str], int) -> ()
         # only report images every specific interval
         if step % self.image_report_freq != 0:
             return None
@@ -403,6 +420,18 @@ class EventTrainsWriter(object):
                                           force_add_prefix=self._logger._get_tensorboard_series_prefix())
         step = self._fix_step_counter(title, series, step)
 
+        # check if this is a local temp file
+        if isinstance(img_data_np, str):
+            self._logger.report_image(
+                title=title,
+                series=series,
+                iteration=step,
+                local_path=img_data_np,
+                delete_after_upload=True,
+                max_image_history=self.max_keep_images if max_keep_images is None else max_keep_images,
+            )
+            return
+
         if img_data_np.dtype != np.uint8:
             # assume scale 0-1
             img_data_np = (img_data_np * 255).astype(np.uint8)