tfx: "Type already exists with different properties error" when running TFX 0.21.2 and TFX 0.23.0 in same kubeflow

One of the users of my kubeflow cluster is using TFX 0.21.4 and another is using TFX 0.23.0

After any pipeline is run that uses TFX 0.23.0, the ones that use TFX 0.21.4 fail with the following error during BigQueryExampleGen

Traceback (most recent call last):
  File "/tfx-src/tfx/orchestration/kubeflow/container_entrypoint.py", line 382, in <module>
    main()
  File "/tfx-src/tfx/orchestration/kubeflow/container_entrypoint.py", line 375, in main
    execution_info = launcher.launch()
  File "/tfx-src/tfx/orchestration/launcher/base_component_launcher.py", line 197, in launch
    self._exec_properties)
  File "/tfx-src/tfx/orchestration/launcher/base_component_launcher.py", line 166, in _run_driver
    component_info=self._component_info)
  File "/tfx-src/tfx/components/base/base_driver.py", line 289, in pre_execution
    contexts=contexts)
  File "/tfx-src/tfx/orchestration/metadata.py", line 601, in update_execution
    registered_artifacts_ids=registered_output_artifact_ids))
  File "/tfx-src/tfx/orchestration/metadata.py", line 538, in _artifact_and_event_pairs
    a.set_mlmd_artifact_type(self._prepare_artifact_type(a.artifact_type))
  File "/tfx-src/tfx/orchestration/metadata.py", line 184, in _prepare_artifact_type
    artifact_type=artifact_type, can_add_fields=True)
  File "/opt/venv/lib/python3.6/site-packages/ml_metadata/metadata_store/metadata_store.py", line 268, in put_artifact_type
    self._call('PutArtifactType', request, response)
  File "/opt/venv/lib/python3.6/site-packages/ml_metadata/metadata_store/metadata_store.py", line 131, in _call
    return self._call_method(method_name, request, response)
  File "/opt/venv/lib/python3.6/site-packages/ml_metadata/metadata_store/metadata_store.py", line 162, in _call_method
    raise _make_exception(e.details(), e.code().value[0])
tensorflow.python.framework.errors_impl.AlreadyExistsError: Type already exists with different properties.

I completely wiped out my kubeflow cluster/metadata to test from scratch, and the same thing happens. Is there any way to avoid that short of forcing everyone to be on the same tfx version?