diff --git a/docs/source/en/tutorials/fast_diffusion.md b/docs/source/en/tutorials/fast_diffusion.md
index cc83fdd997..7392d93547 100644
--- a/docs/source/en/tutorials/fast_diffusion.md
+++ b/docs/source/en/tutorials/fast_diffusion.md
@@ -315,4 +315,26 @@ Applying dynamic quantization improves the latency from 2.52 seconds to 2.43 sec
 
 <img src="https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/progressive-acceleration-sdxl/SDXL%2C_Batch_Size%3A_1%2C_Steps%3A_30_5.png" width=500>
 
-</div>
\ No newline at end of file
+</div>
+
+## Misc
+
+### No graph breaks during torch.compile
+
+Ensuring that the underlying model/method can be fully compiled is crucial for performance (torch.compile with fullgraph=True). This means having no graph breaks. We did this for the UNet and VAE by changing how we access the returning variables. Consider the following example: 
+
+```diff
+- latents = unet(
+-	latents, timestep=timestep, encoder_hidden_states=prompt_embeds
+-).sample
+
++ latents = unet(
++	latents, timestep=timestep, encoder_hidden_states=prompt_embeds, return_dict=False
++)[0]
+```
+
+### Getting rid of GPU syncs after compilation
+
+During the iterative reverse diffusion process, we [call](https://github.com/huggingface/diffusers/blob/1d686bac8146037e97f3fd8c56e4063230f71751/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py#L1228) `step()` on the scheduler each time after the denoiser predicts the less noisy latent embeddings. Inside `step()`, the `sigmas` variable is [indexed](https://github.com/huggingface/diffusers/blob/1d686bac8146037e97f3fd8c56e4063230f71751/src/diffusers/schedulers/scheduling_euler_discrete.py#L476). If the `sigmas` array is placed on the GPU, indexing causes a communication sync between the CPU and GPU. This causes a latency, and it becomes more evident when the denoiser has already been compiled. 
+
+But if the `sigmas` array always stays on the CPU (refer to [this line](https://github.com/huggingface/diffusers/blob/35a969d297cba69110d175ee79c59312b9f49e1e/src/diffusers/schedulers/scheduling_euler_discrete.py#L240)), this sync doesn’t take place, hence improved latency. In general, any CPU <-> GPU communication sync should be none or be kept to a bare minimum as it can impact inference latency.