diff --git a/docs/source/en/_toctree.yml b/docs/source/en/_toctree.yml
index 32bca81b6aea..b33989aed0e1 100644
--- a/docs/source/en/_toctree.yml
+++ b/docs/source/en/_toctree.yml
@@ -58,12 +58,6 @@
     title: Batch inference
   - local: training/distributed_inference
     title: Distributed inference
-  - local: using-diffusers/scheduler_features
-    title: Scheduler features
-  - local: using-diffusers/callback
-    title: Pipeline callbacks
-  - local: using-diffusers/image_quality
-    title: Controlling image quality
 
 - title: Inference optimization
   isExpanded: false
@@ -92,6 +86,8 @@
       title: xDiT
     - local: optimization/para_attn
       title: ParaAttention
+    - local: using-diffusers/image_quality
+      title: FreeU
 
 - title: Hybrid Inference
   isExpanded: false
diff --git a/docs/source/en/using-diffusers/image_quality.md b/docs/source/en/using-diffusers/image_quality.md
index 517d985190c9..29ce483d5ecc 100644
--- a/docs/source/en/using-diffusers/image_quality.md
+++ b/docs/source/en/using-diffusers/image_quality.md
@@ -10,13 +10,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 specific language governing permissions and limitations under the License.
 -->
 
-# Controlling image quality
-
-The components of a diffusion model, like the UNet and scheduler, can be optimized to improve the quality of generated images leading to better details. These techniques are especially useful if you don't have the resources to simply use a larger model for inference. You can enable these techniques during inference without any additional training.
-
-This guide will show you how to turn these techniques on in your pipeline and how to configure them to improve the quality of your generated images.
-
-## Details
+# FreeU
 
 [FreeU](https://hf.co/papers/2309.11497) improves image details by rebalancing the UNet's backbone and skip connection weights. The skip connections can cause the model to overlook some of the backbone semantics which may lead to unnatural image details in the generated image. This technique does not require any additional training and can be applied on the fly during inference for tasks like image-to-image and text-to-video.
 
@@ -139,7 +133,7 @@ export_to_video(video_frames, "teddy_bear.mp4", fps=10)
 </hfoption>
 </hfoptions>
 
-Call the [`pipelines.StableDiffusionMixin.disable_freeu`] method to disable FreeU.
+Call the [`~pipelines.StableDiffusionMixin.disable_freeu`] method to disable FreeU.
 
 ```py
 pipeline.disable_freeu()