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.github/workflows/build_pr_documentation.yml

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cancel-in-progress: true
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jobs:
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check-links:
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runs-on: ubuntu-latest
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steps:
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- name: Checkout repository
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uses: actions/checkout@v4
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- name: Set up Python
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uses: actions/setup-python@v5
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with:
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python-version: '3.10'
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- name: Install uv
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run: |
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curl -LsSf https://astral.sh/uv/install.sh | sh
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echo "$HOME/.cargo/bin" >> $GITHUB_PATH
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- name: Install doc-builder
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run: |
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uv pip install --system git+https://github.com/huggingface/doc-builder.git@main
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- name: Check documentation links
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run: |
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uv run doc-builder check-links docs/source/en
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build:
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needs: check-links
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uses: huggingface/doc-builder/.github/workflows/build_pr_documentation.yml@main
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with:
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commit_sha: ${{ github.event.pull_request.head.sha }}

.gitignore

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.vs
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.vscode
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# Cursor
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.cursor
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# Pycharm
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.idea
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README.md

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<tr style="border-top: 2px solid black">
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<td>Text-guided Image Inpainting</td>
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<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/inpaint">Stable Diffusion Inpainting</a></td>
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<td><a href="https://huggingface.co/runwayml/stable-diffusion-inpainting"> runwayml/stable-diffusion-inpainting </a></td>
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<td><a href="https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting"> stable-diffusion-v1-5/stable-diffusion-inpainting </a></td>
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</tr>
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<tr style="border-top: 2px solid black">
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<td>Image Variation</td>

docs/source/en/_toctree.yml

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isExpanded: false
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sections:
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- local: using-diffusers/weighted_prompts
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title: Prompt techniques
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title: Prompting
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- local: using-diffusers/create_a_server
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title: Create a server
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- local: using-diffusers/batched_inference

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

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[IP-Adapter](https://hf.co/papers/2308.06721) is a lightweight adapter that enables prompting a diffusion model with an image. This method decouples the cross-attention layers of the image and text features. The image features are generated from an image encoder.
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> [!TIP]
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> Learn how to load an IP-Adapter checkpoint and image in the IP-Adapter [loading](../../using-diffusers/loading_adapters#ip-adapter) guide, and you can see how to use it in the [usage](../../using-diffusers/ip_adapter) guide.
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> Learn how to load and use an IP-Adapter checkpoint and image in the [IP-Adapter](../../using-diffusers/ip_adapter) guide,.
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## IPAdapterMixin
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docs/source/en/api/loaders/lora.md

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- [`LoraBaseMixin`] provides a base class with several utility methods to fuse, unfuse, unload, LoRAs and more.
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> [!TIP]
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> To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
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> To learn more about how to load LoRA weights, see the [LoRA](../../tutorials/using_peft_for_inference) loading guide.
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## LoraBaseMixin
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[[autodoc]] loaders.lora_pipeline.QwenImageLoraLoaderMixin
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## KandinskyLoraLoaderMixin
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[[autodoc]] loaders.lora_pipeline.KandinskyLoraLoaderMixin
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## LoraBaseMixin
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[[autodoc]] loaders.lora_base.LoraBaseMixin

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

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# PEFT
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Diffusers supports loading adapters such as [LoRA](../../using-diffusers/loading_adapters) with the [PEFT](https://huggingface.co/docs/peft/index) library with the [`~loaders.peft.PeftAdapterMixin`] class. This allows modeling classes in Diffusers like [`UNet2DConditionModel`], [`SD3Transformer2DModel`] to operate with an adapter.
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Diffusers supports loading adapters such as [LoRA](../../tutorials/using_peft_for_inference) with the [PEFT](https://huggingface.co/docs/peft/index) library with the [`~loaders.peft.PeftAdapterMixin`] class. This allows modeling classes in Diffusers like [`UNet2DConditionModel`], [`SD3Transformer2DModel`] to operate with an adapter.
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> [!TIP]
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> Refer to the [Inference with PEFT](../../tutorials/using_peft_for_inference.md) tutorial for an overview of how to use PEFT in Diffusers for inference.

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

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[`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.
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> [!TIP]
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> To learn more about how to load Textual Inversion embeddings, see the [Textual Inversion](../../using-diffusers/loading_adapters#textual-inversion) loading guide.
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> To learn more about how to load Textual Inversion embeddings, see the [Textual Inversion](../../using-diffusers/textual_inversion_inference) loading guide.
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## TextualInversionLoaderMixin
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docs/source/en/api/loaders/transformer_sd3.md

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The [`SD3Transformer2DLoadersMixin`] class currently only loads IP-Adapter weights, but will be used in the future to save weights and load LoRAs.
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> [!TIP]
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> To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
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> To learn more about how to load LoRA weights, see the [LoRA](../../tutorials/using_peft_for_inference) loading guide.
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## SD3Transformer2DLoadersMixin
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docs/source/en/api/loaders/unet.md

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The [`UNet2DConditionLoadersMixin`] class provides functions for loading and saving weights, fusing and unfusing LoRAs, disabling and enabling LoRAs, and setting and deleting adapters.
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> [!TIP]
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> To learn more about how to load LoRA weights, see the [LoRA](../../using-diffusers/loading_adapters#lora) loading guide.
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> To learn more about how to load LoRA weights, see the [LoRA](../../tutorials/using_peft_for_inference) guide.
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## UNet2DConditionLoadersMixin
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