diff --git a/stable_diffusion/README.md b/stable_diffusion/README.md
index 170ce6b9..edd3a0ed 100644
--- a/stable_diffusion/README.md
+++ b/stable_diffusion/README.md
@@ -65,8 +65,9 @@ Performance
 -----------
 
 The following table compares the performance of the UNet in stable diffusion.
-We report throughput in images per second for the provided `txt2image.py`
-script and the `diffusers` library using the MPS PyTorch backend.
+We report throughput in images per second **processed by the UNet** for the
+provided `txt2image.py` script and the `diffusers` library using the MPS
+PyTorch backend.
 
 At the time of writing this comparison convolutions are still some of the least
 optimized operations in MLX. Despite that, MLX still achieves **~40% higher
@@ -93,3 +94,7 @@ The above experiments were made on an M2 Ultra with PyTorch version 2.1,
 diffusers version 0.21.4 and transformers version 4.33.3. For the generation we
 used classifier free guidance which means that the above batch sizes result
 double the images processed by the UNet.
+
+Note that the above table means that it takes about 90 seconds to fully
+generate 16 images with MLX and 50 diffusion steps with classifier free
+guidance and about 120 for PyTorch.