diff --git a/simple_sdxl_optimizations.md b/simple_sdxl_optimizations.md
index e61d6fd40e..847d41a4dc 100644
--- a/simple_sdxl_optimizations.md
+++ b/simple_sdxl_optimizations.md
@@ -199,7 +199,7 @@ Combined with SDPA and fp16, we can reduce the memory to 21.9GB. Other technique
 
 ## Tiny Autoencoder
 
-As previously mentioned, a VAE decodes latents into images. Naturally, this step is directly bottlenecked by the size of the VAE. So, let’s just use a smaller autoencoder! The [Tiny Autoencoder by madebyollin](https://github.com/madebyollin/taesd), available [the Hub](https://huggingface.co/madebyollin/taesdxl) is just 10MB and it is distilled from the original VAE used by SDXL.
+As previously mentioned, a VAE decodes latents into images. Naturally, this step is directly bottlenecked by the size of the VAE. So, let’s just use a smaller autoencoder! The [Tiny Autoencoder by `madebyollin`](https://github.com/madebyollin/taesd), available [the Hub](https://huggingface.co/madebyollin/taesdxl) is just 10MB and it is distilled from the original VAE used by SDXL. 
 
 ```python
 from diffusers import AutoencoderTiny