[docs] Adapters (#14842)

* lora

* legacy adapters

* ip adapter

* controlnet

* feedback

Author: stevhliu

docs/source/en/_toctree.yml

@@ -43,12 +43,10 @@
       title: IP-Adapter
     - local: using-diffusers/controlnet
       title: ControlNet
-    - local: using-diffusers/t2i_adapter
-      title: T2I-Adapter
     - local: using-diffusers/dreambooth
       title: DreamBooth
-    - local: using-diffusers/textual_inversion_inference
-      title: Textual inversion
+    - local: using-diffusers/legacy_adapters
+      title: Legacy adapters
     title: Adapters and personalization
   title: Inference
 - isExpanded: true

docs/source/en/api/loaders/textual_inversion.md

@@ -17,7 +17,7 @@ Textual Inversion is a training method for personalizing models by learning new
 [`TextualInversionLoaderMixin`] provides a function for loading Textual Inversion embeddings from Diffusers and Automatic1111 into the text encoder and loading a special token to activate the embeddings.
 
 > [!TIP]
-> To learn more about how to load Textual Inversion embeddings, see the [Textual Inversion](../../using-diffusers/textual_inversion_inference) loading guide.
+> To learn more about how to load Textual Inversion embeddings, see the [Textual Inversion](../../using-diffusers/legacy_adapters#textual-inversion) loading guide.
 
 ## TextualInversionLoaderMixin
 

docs/source/en/api/pipelines/latent_consistency_models.md

@@ -458,7 +458,7 @@ image
 
 ### T2I-Adapter
 
-[T2I-Adapter](../../using-diffusers/t2i_adapter) is an even more lightweight adapter than ControlNet, that provides an additional input to condition a pretrained model with. It is faster than ControlNet but the results may be slightly worse.
+[T2I-Adapter](../../using-diffusers/legacy_adapters#t2i-adapter) is an even more lightweight adapter than ControlNet, that provides an additional input to condition a pretrained model with. It is faster than ControlNet but the results may be slightly worse.
 
 You can find additional T2I-Adapter checkpoints trained on other inputs in [TencentArc's](https://hf.co/TencentARC) repository.
 

docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_xl.md

@@ -426,7 +426,7 @@ image
     <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/sdxl-double-prompt.png" alt="generated image of an astronaut in a jungle in the style of a van gogh painting"/>
 </div>
 
-The dual text-encoders also support textual inversion embeddings that need to be loaded separately as explained in the [textual inversion](../../../using-diffusers/textual_inversion_inference) guide.
+The dual text-encoders also support textual inversion embeddings that need to be loaded separately as explained in the [textual inversion](../../../using-diffusers/legacy_adapters#textual-inversion) guide.
 
 ## Optimizations
 

docs/source/en/training/dreambooth.md

@@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License.
 
 [DreamBooth](https://huggingface.co/papers/2208.12242) is a training technique that updates the entire diffusion model by training on just a few images of a subject or style. It works by associating a special word in the prompt with the example images.
 
-To load a trained checkpoint for inference, see [Load a DreamBooth adapter for inference](../using-diffusers/dreambooth).
+To load a trained checkpoint for inference, see [Load a DreamBooth model for inference](../using-diffusers/dreambooth).
 
 If you're training on a GPU with limited vRAM, you should try enabling the `gradient_checkpointing` and `mixed_precision` parameters in the training command. You can also reduce your memory footprint by using memory-efficient attention with [xFormers](../optimization/xformers).
 
@@ -550,4 +550,5 @@ Training the DeepFloyd IF model can be challenging, but here are some tips that
 
 Congratulations on training your DreamBooth model! To learn more about how to use your new model, the following guide may be helpful:
 
-- Learn how to [load a DreamBooth](../using-diffusers/dreambooth) model for inference if you trained your model with LoRA.
+- Learn how to [load a DreamBooth checkpoint](../using-diffusers/dreambooth) for inference.
+- If you trained with LoRA, see [LoRA for inference](../tutorials/using_peft_for_inference).

docs/source/en/training/text_inversion.md

@@ -14,7 +14,7 @@ specific language governing permissions and limitations under the License.
 
 [Textual Inversion](https://hf.co/papers/2208.01618) is a training technique for personalizing image generation models with just a few example images of what you want it to learn. This technique works by learning and updating the text embeddings (the new embeddings are tied to a special word you must use in the prompt) to match the example images you provide.
 
-For inference with trained embeddings, see [Textual inversion inference](../using-diffusers/textual_inversion_inference).
+For inference with trained embeddings, see [Textual inversion inference](../using-diffusers/legacy_adapters#textual-inversion).
 
 If you're training on a GPU with limited vRAM, you should try enabling the `gradient_checkpointing` and `mixed_precision` parameters in the training command. You can also reduce your memory footprint by using memory-efficient attention with [xFormers](../optimization/xformers).
 
@@ -205,4 +205,4 @@ image.save("cat-train.png")
 
 Congratulations on training your own Textual Inversion model! 🎉 To learn more about how to use your new model, the following guides may be helpful:
 
-- Learn how to [load Textual Inversion embeddings](../using-diffusers/textual_inversion_inference) and also use them as negative embeddings.
+- Learn how to [load Textual Inversion embeddings](../using-diffusers/legacy_adapters#textual-inversion) and also use them as negative embeddings.