[docs] Reorganize table of contents

docs/source/en/_toctree.yml

@@ -11,6 +11,8 @@
 - sections:
   - local: tutorials/tutorial_overview
     title: Overview
+  - local: using-diffusers/write_own_pipeline
+    title: Understanding models and schedulers
   - local: tutorials/basic_training
     title: Train a diffusion model
   title: Tutorials
@@ -19,21 +21,17 @@
     - local: using-diffusers/loading_overview
       title: Overview
     - local: using-diffusers/loading
-      title: Loading Pipelines, Models, and Schedulers
+      title: Load pipelines, models, and schedulers
     - local: using-diffusers/schedulers
-      title: Using different Schedulers
-    - local: using-diffusers/configuration
-      title: Configuring Pipelines, Models, and Schedulers
+      title: Load and compare different schedulers
     - local: using-diffusers/custom_pipeline_overview
-      title: Loading and Adding Custom Pipelines
+      title: Load and add custom pipelines
     - local: using-diffusers/kerascv
-      title: Using KerasCV Stable Diffusion Checkpoints in Diffusers
+      title: Load KerasCV Stable Diffusion checkpoints
     title: Loading & Hub
   - sections:
     - local: using-diffusers/pipeline_overview
       title: Overview
-    - local: using-diffusers/write_own_pipeline
-      title: Understanding models and schedulers
     - local: using-diffusers/unconditional_image_generation
       title: Unconditional Image Generation
     - local: using-diffusers/conditional_image_generation
@@ -44,8 +42,6 @@
       title: Text-Guided Image-Inpainting
     - local: using-diffusers/depth2img
       title: Text-Guided Depth-to-Image
-    - local: using-diffusers/controlling_generation
-      title: Controlling generation
     - local: using-diffusers/reusing_seeds
       title: Reusing seeds for deterministic generation
     - local: using-diffusers/reproducibility
@@ -59,6 +55,20 @@
     - local: using-diffusers/weighted_prompts
       title: Weighting Prompts
     title: Pipelines for Inference
+  - sections:
+    - local: training/overview
+      title: Overview
+    - local: training/unconditional_training
+      title: Unconditional image generation
+    - local: training/text_inversion
+      title: Textual Inversion
+    - local: training/dreambooth
+      title: DreamBooth
+    - local: training/text2image
+      title: Text-to-image
+    - local: training/lora
+      title: Low-Rank Adaptation of Large Language Models (LoRA)
+    title: Training
   - sections:
     - local: using-diffusers/rl
       title: Reinforcement Learning
@@ -86,23 +96,11 @@
   - local: optimization/habana
     title: Habana Gaudi
   title: Optimization/Special Hardware
-- sections:
-  - local: training/overview
-    title: Overview
-  - local: training/unconditional_training
-    title: Unconditional image generation
-  - local: training/text_inversion
-    title: Textual Inversion
-  - local: training/dreambooth
-    title: DreamBooth
-  - local: training/text2image
-    title: Text-to-image
-  - local: training/lora
-    title: Low-Rank Adaptation of Large Language Models (LoRA)
-  title: Training
 - sections:
   - local: conceptual/philosophy
     title: Philosophy
+  - local: using-diffusers/controlling_generation
+    title: Controlled generation
   - local: conceptual/contribution
     title: How to contribute?
   - local: conceptual/ethical_guidelines

docs/source/en/api/configuration.mdx

@@ -12,12 +12,14 @@ specific language governing permissions and limitations under the License.
 
 # Configuration
 
-In Diffusers, schedulers of type [`schedulers.scheduling_utils.SchedulerMixin`], and models of type [`ModelMixin`] inherit from [`ConfigMixin`] which conveniently takes care of storing all parameters that are 
-passed to the respective `__init__` methods in a JSON-configuration file.
+Schedulers from [`~schedulers.scheduling_utils.SchedulerMixin`] and models from [`ModelMixin`] inherit from [`ConfigMixin`] which conveniently takes care of storing all the parameters that are 
+passed to their respective `__init__` methods in a JSON-configuration file.
 
 ## ConfigMixin
 
 [[autodoc]] ConfigMixin
 	- load_config
 	- from_config
 	- save_config
+	- to_json_file
+	- to_json_string

docs/source/en/using-diffusers/controlling_generation.mdx

@@ -10,7 +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 generation of diffusion models
+# Controlled generation
 
 Controlling outputs generated by diffusion models has been long pursued by the community and is now an active research topic. In many popular diffusion models, subtle changes in inputs, both images and text prompts, can drastically change outputs. In an ideal world we want to be able to control how semantics are preserved and changed.