diff --git a/pytorch_forecasting/models/base_model.py b/pytorch_forecasting/models/base_model.py
index a82196cf..a12f5da6 100644
--- a/pytorch_forecasting/models/base_model.py
+++ b/pytorch_forecasting/models/base_model.py
@@ -950,6 +950,18 @@ def log_interval(self) -> float:
         else:
             return self.hparams.log_val_interval
 
+    def _logger_supports(self, method: str) -> bool:
+        """Whether logger supports method.
+
+        Returns
+        -------
+        supports_method : bool
+            True if attribute self.logger.experiment.method exists, False otherwise.
+        """
+        if not hasattr(self, "logger") or not hasattr(self.logger, "experiment"):
+            return False
+        return hasattr(self.logger.experiment, method)
+
     def log_prediction(
         self, x: Dict[str, torch.Tensor], out: Dict[str, torch.Tensor], batch_idx: int, **kwargs
     ) -> None:
@@ -976,6 +988,10 @@ def log_prediction(
             if not mpl_available:
                 return None  # don't log matplotlib plots if not available
 
+            # Don't log figures if add_figure is not available
+            if not self._logger_supports("add_figure"):
+                return None
+
             for idx in log_indices:
                 fig = self.plot_prediction(x, out, idx=idx, add_loss_to_title=True, **kwargs)
                 tag = f"{self.current_stage} prediction"
@@ -1146,7 +1162,8 @@ def log_gradient_flow(self, named_parameters: Dict[str, torch.Tensor]) -> None:
 
         mpl_available = _check_matplotlib("log_gradient_flow", raise_error=False)
 
-        if not mpl_available:
+        # Don't log figures if matplotlib or add_figure is not available
+        if not mpl_available or not self._logger_supports("add_figure"):
             return None
 
         import matplotlib.pyplot as plt
diff --git a/pytorch_forecasting/models/nbeats/__init__.py b/pytorch_forecasting/models/nbeats/__init__.py
index 149b4fbc..6a4b5caf 100644
--- a/pytorch_forecasting/models/nbeats/__init__.py
+++ b/pytorch_forecasting/models/nbeats/__init__.py
@@ -265,7 +265,8 @@ def log_interpretation(self, x, out, batch_idx):
         """
         mpl_available = _check_matplotlib("log_interpretation", raise_error=False)
 
-        if not mpl_available:
+        # Don't log figures if matplotlib or add_figure is not available
+        if not mpl_available or not self._logger_supports("add_figure"):
             return None
 
         label = ["val", "train"][self.training]
diff --git a/pytorch_forecasting/models/nhits/__init__.py b/pytorch_forecasting/models/nhits/__init__.py
index 68816f22..94f85cfa 100644
--- a/pytorch_forecasting/models/nhits/__init__.py
+++ b/pytorch_forecasting/models/nhits/__init__.py
@@ -541,7 +541,8 @@ def log_interpretation(self, x, out, batch_idx):
         """
         mpl_available = _check_matplotlib("log_interpretation", raise_error=False)
 
-        if not mpl_available:
+        # Don't log figures if matplotlib or add_figure is not available
+        if not mpl_available or not self._logger_supports("add_figure"):
             return None
 
         label = ["val", "train"][self.training]
diff --git a/pytorch_forecasting/models/temporal_fusion_transformer/__init__.py b/pytorch_forecasting/models/temporal_fusion_transformer/__init__.py
index cc506612..983b5a72 100644
--- a/pytorch_forecasting/models/temporal_fusion_transformer/__init__.py
+++ b/pytorch_forecasting/models/temporal_fusion_transformer/__init__.py
@@ -818,7 +818,8 @@ def log_interpretation(self, outputs):
 
         mpl_available = _check_matplotlib("log_interpretation", raise_error=False)
 
-        if not mpl_available:
+        # Don't log figures if matplotlib or add_figure is not available
+        if not mpl_available or not self._logger_supports("add_figure"):
             return None
 
         import matplotlib.pyplot as plt
@@ -857,6 +858,11 @@ def log_embeddings(self):
         """
         Log embeddings to tensorboard
         """
+
+        # Don't log embeddings if add_embedding is not available
+        if not self._logger_supports("add_embedding"):
+            return None
+
         for name, emb in self.input_embeddings.items():
             labels = self.hparams.embedding_labels[name]
             self.logger.experiment.add_embedding(