Apply suggestions from code review

Co-authored-by: Pedro Cuenca <[email protected]>

Author: sayakpaul

docs/source/en/optimization/tome.mdx

@@ -97,7 +97,7 @@ We used this script for benchmarking: [https://gist.github.com/sayakpaul/27aec6b
 
 As seen in the tables above, the speedup with `tomesd` becomes more pronounced for larger image resolutions. It is also interesting to note that with `tomesd`, it becomes possible to run the pipeline on a higher resolution, like 1024x1024. 
 
-It might be possible to speed up inference even further with [`torch.compile()`](https://huggingface.co/docs/diffusers/optimization/torch2.0). 
+It might be possible to speed up inference even further with PyTorch 2.0, including the ability to compile the UNet using [`torch.compile()`](https://huggingface.co/docs/diffusers/optimization/torch2.0). 
 
 ## Quality