diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml
index 0c05f0ef7ffa..ae5de5673100 100644
--- a/docs/source/en/_toctree.yml
+++ b/docs/source/en/_toctree.yml
@@ -160,6 +160,8 @@
       title: xFormers
     - local: optimization/tome
       title: Token merging
+    - local: optimization/deepcache
+      title: DeepCache
     title: General optimizations
   - sections:
     - local: using-diffusers/stable_diffusion_jax_how_to
diff --git a/docs/source/en/optimization/deepcache.md b/docs/source/en/optimization/deepcache.md
new file mode 100644
index 000000000000..3ad513357def
--- /dev/null
+++ b/docs/source/en/optimization/deepcache.md
@@ -0,0 +1,62 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# DeepCache
+[DeepCache](https://huggingface.co/papers/2312.00858) accelerates [`StableDiffusionPipeline`] and [`StableDiffusionXLPipeline`] by strategically caching and reusing high-level features while efficiently updating low-level features by taking advantage of the U-Net architecture.
+
+Start by installing [DeepCache](https://github.com/horseee/DeepCache):
+```bash
+pip install DeepCache
+```
+
+Then load and enable the [`DeepCacheSDHelper`](https://github.com/horseee/DeepCache#usage):
+
+```diff
+  import torch
+  from diffusers import StableDiffusionPipeline
+  pipe = StableDiffusionPipeline.from_pretrained('runwayml/stable-diffusion-v1-5', torch_dtype=torch.float16).to("cuda")
+
++ from DeepCache import DeepCacheSDHelper
++ helper = DeepCacheSDHelper(pipe=pipe)
++ helper.set_params(
++     cache_interval=3,
++     cache_branch_id=0,
++ )
++ helper.enable()
+
+  image = pipe("a photo of an astronaut on a moon").images[0]
+```
+
+The `set_params` method accepts two arguments: `cache_interval` and `cache_branch_id`. `cache_interval` means the frequency of feature caching, specified as the number of steps between each cache operation. `cache_branch_id` identifies which branch of the network (ordered from the shallowest to the deepest layer) is responsible for executing the caching processes. 
+Opting for a lower `cache_branch_id` or a larger `cache_interval` can lead to faster inference speed at the expense of reduced image quality (ablation experiments of these two hyperparameters can be found in the [paper](https://arxiv.org/abs/2312.00858)). Once those arguments are set, use the `enable` or `disable` methods to activate or deactivate the `DeepCacheSDHelper`.
+
+<div class="flex justify-center">
+    <img src="https://github.com/horseee/Diffusion_DeepCache/raw/master/static/images/example.png">
+</div>
+
+You can find more generated samples (original pipeline vs DeepCache) and the corresponding inference latency in the [WandB report](https://wandb.ai/horseee/DeepCache/runs/jwlsqqgt?workspace=user-horseee). The prompts are randomly selected from the [MS-COCO 2017](https://cocodataset.org/#home) dataset.
+
+## Benchmark
+
+We tested how much faster DeepCache accelerates [Stable Diffusion v2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1) with 50 inference steps on an NVIDIA RTX A5000, using different configurations for resolution, batch size, cache interval (I), and cache branch (B).
+
+| **Resolution** | **Batch size** | **Original** | **DeepCache(I=3, B=0)** | **DeepCache(I=5, B=0)** | **DeepCache(I=5, B=1)** |
+|----------------|----------------|--------------|-------------------------|-------------------------|-------------------------|
+|             512|               8|         15.96|              6.88(2.32x)|              5.03(3.18x)|              7.27(2.20x)|
+|                |               4|          8.39|              3.60(2.33x)|              2.62(3.21x)|              3.75(2.24x)|
+|                |               1|          2.61|              1.12(2.33x)|              0.81(3.24x)|              1.11(2.35x)|
+|             768|               8|         43.58|             18.99(2.29x)|             13.96(3.12x)|             21.27(2.05x)|
+|                |               4|         22.24|              9.67(2.30x)|              7.10(3.13x)|             10.74(2.07x)|
+|                |               1|          6.33|              2.72(2.33x)|              1.97(3.21x)|              2.98(2.12x)|
+|            1024|               8|        101.95|             45.57(2.24x)|             33.72(3.02x)|             53.00(1.92x)|
+|                |               4|         49.25|             21.86(2.25x)|             16.19(3.04x)|             25.78(1.91x)|
+|                |               1|         13.83|              6.07(2.28x)|              4.43(3.12x)|              7.15(1.93x)|