diff --git a/docs/source/en/training/distributed_inference.md b/docs/source/en/training/distributed_inference.md
index cd642d6aca07..0e1eb7962bf7 100644
--- a/docs/source/en/training/distributed_inference.md
+++ b/docs/source/en/training/distributed_inference.md
@@ -177,7 +177,7 @@ transformer = FluxTransformer2DModel.from_pretrained(
 ```
 
 > [!TIP]
-> At any point, you can try `print(pipeline.hf_device_map)` to see how the various models are distributed across devices. This is useful for tracking the device placement of the models.
+> At any point, you can try `print(pipeline.hf_device_map)` to see how the various models are distributed across devices. This is useful for tracking the device placement of the models. You can also try `print(transformer.hf_device_map)` to see how the transformer model is sharded across devices.
 
 Add the transformer model to the pipeline for denoising, but set the other model-level components like the text encoders and VAE to `None` because you don't need them yet.