diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml
index 795b023809..ac3e02a27f 100644
--- a/docs/source/en/_toctree.yml
+++ b/docs/source/en/_toctree.yml
@@ -50,6 +50,8 @@
       title: Textual inversion
     - local: training/distributed_inference
       title: Distributed inference with multiple GPUs
+    - local: using-diffusers/distilled_sd
+      title: Distilled Stable Diffusion inference
     - local: using-diffusers/reusing_seeds
       title: Improve image quality with deterministic generation
     - local: using-diffusers/control_brightness
diff --git a/docs/source/en/using-diffusers/distilled_sd.md b/docs/source/en/using-diffusers/distilled_sd.md
new file mode 100644
index 0000000000..7653300b92
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+++ b/docs/source/en/using-diffusers/distilled_sd.md
@@ -0,0 +1,121 @@
+# Distilled Stable Diffusion inference
+
+[[open-in-colab]]
+
+Stable Diffusion inference can be a computationally intensive process because it must iteratively denoise the latents to generate an image. To reduce the computational burden, you can use a *distilled* version of the Stable Diffusion model from [Nota AI](https://huggingface.co/nota-ai). The distilled version of their Stable Diffusion model eliminates some of the residual and attention blocks from the UNet, reducing the model size by 51% and improving latency on CPU/GPU by 43%.
+
+<Tip>
+
+Read this [blog post](https://huggingface.co/blog/sd_distillation) to learn more about how knowledge distillation training works to produce a faster, smaller, and cheaper generative model.
+
+</Tip>
+
+Let's load the distilled Stable Diffusion model and compare it against the original Stable Diffusion model:
+
+```py
+from diffusers import StableDiffusionPipeline
+import torch
+
+distilled = StableDiffusionPipeline.from_pretrained(
+    "nota-ai/bk-sdm-small", torch_dtype=torch.float16, use_safetensors=True,
+).to("cuda")
+
+original = StableDiffusionPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16, use_safetensors=True,
+).to("cuda")
+```
+
+Given a prompt, get the inference time for the original model:
+
+```py
+import time
+
+seed = 2023
+generator = torch.manual_seed(seed)
+
+NUM_ITERS_TO_RUN = 3
+NUM_INFERENCE_STEPS = 25
+NUM_IMAGES_PER_PROMPT = 4
+
+prompt = "a golden vase with different flowers"
+
+start = time.time_ns()
+for _ in range(NUM_ITERS_TO_RUN):
+    images = original(
+        prompt,
+        num_inference_steps=NUM_INFERENCE_STEPS,
+        generator=generator,
+        num_images_per_prompt=NUM_IMAGES_PER_PROMPT
+    ).images
+end = time.time_ns()
+original_sd = f"{(end - start) / 1e6:.1f}"
+
+print(f"Execution time -- {original_sd} ms\n")
+"Execution time -- 45781.5 ms"
+```
+
+Time the distilled model inference:
+
+```py
+start = time.time_ns()
+for _ in range(NUM_ITERS_TO_RUN):
+    images = distilled(
+        prompt,
+        num_inference_steps=NUM_INFERENCE_STEPS,
+        generator=generator,
+        num_images_per_prompt=NUM_IMAGES_PER_PROMPT
+    ).images
+end = time.time_ns()
+
+distilled_sd = f"{(end - start) / 1e6:.1f}"
+print(f"Execution time -- {distilled_sd} ms\n")
+"Execution time -- 29884.2 ms"
+```
+
+<div class="flex gap-4">
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/original_sd.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">original Stable Diffusion (45781.5 ms)</figcaption>
+  </div>
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/distilled_sd.png"/>
+    <figcaption class="mt-2 text-center text-sm text-gray-500">distilled Stable Diffusion (29884.2 ms)</figcaption>
+  </div>
+</div>
+
+## Tiny AutoEncoder
+
+To speed inference up even more, use a tiny distilled version of the [Stable Diffusion VAE](https://huggingface.co/sayakpaul/taesdxl-diffusers) to denoise the latents into images. Replace the VAE in the distilled Stable Diffusion model with the tiny VAE:
+
+```py
+from diffusers import AutoencoderTiny
+
+distilled.vae = AutoencoderTiny.from_pretrained(
+    "sayakpaul/taesd-diffusers", torch_dtype=torch.float16, use_safetensors=True,
+).to("cuda")
+```
+
+Time the distilled model and distilled VAE inference:
+
+```py
+start = time.time_ns()
+for _ in range(NUM_ITERS_TO_RUN):
+    images = distilled(
+        prompt,
+        num_inference_steps=NUM_INFERENCE_STEPS,
+        generator=generator,
+        num_images_per_prompt=NUM_IMAGES_PER_PROMPT
+    ).images
+end = time.time_ns()
+
+distilled_tiny_sd = f"{(end - start) / 1e6:.1f}"
+print(f"Execution time -- {distilled_tiny_sd} ms\n")
+"Execution time -- 27165.7 ms"
+```
+
+<div class="flex justify-center">
+  <div>
+    <img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/distilled_sd_vae.png" />
+    <figcaption class="mt-2 text-center text-sm text-gray-500">distilled Stable Diffusion + Tiny AutoEncoder (27165.7 ms)</figcaption>
+  </div>
+</div>