[docs] Improve navigation by stevhliu · Pull Request #14537 · huggingface/diffusers · GitHub
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[docs] Improve navigation #14537

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stevhliu merged 4 commits into from
Sep 11, 2026
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[docs] Improve navigation #14537

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252 changes: 134 additions & 118 deletions docs/source/en/_toctree.yml
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2 changes: 2 additions & 0 deletions docs/source/en/training/dreambooth.md
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Expand Up @@ -14,6 +14,8 @@ 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).

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).

This guide will explore the [train_dreambooth.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py) script to help you become more familiar with it, and how you can adapt it for your own use-case.
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2 changes: 2 additions & 0 deletions docs/source/en/training/text_inversion.md
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Expand Up @@ -14,6 +14,8 @@ 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).

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).

This guide will explore the [textual_inversion.py](https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py) script to help you become more familiar with it, and how you can adapt it for your own use-case.
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2 changes: 2 additions & 0 deletions docs/source/en/using-diffusers/dreambooth.md
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Expand Up @@ -16,6 +16,8 @@ specific language governing permissions and limitations under the License.

DreamBooth checkpoints are typically a few GBs in size because it contains the full model weights.

To train one, see [Train DreamBooth](../training/dreambooth).

Load the DreamBooth checkpoint with [`~DiffusionPipeline.from_pretrained`] and include the unique identifier in the prompt to activate its generation.

```py
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2 changes: 2 additions & 0 deletions docs/source/en/using-diffusers/textual_inversion_inference.md
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