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[docs] Migrate syntax (#12390)
* change syntax * make style
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‎docs/source/en/api/configuration.md‎

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@@ -14,11 +14,8 @@ specific language governing permissions and limitations under the License.
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Schedulers from [`~schedulers.scheduling_utils.SchedulerMixin`] and models from [`ModelMixin`] inherit from [`ConfigMixin`] which stores all the parameters that are passed to their respective `__init__` methods in a JSON-configuration file.
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<Tip>
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To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `hf auth login`.
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</Tip>
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> [!TIP]
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> To use private or [gated](https://huggingface.co/docs/hub/models-gated#gated-models) models, log-in with `hf auth login`.
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## ConfigMixin
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‎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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</Tip>
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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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## IPAdapterMixin
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‎docs/source/en/api/loaders/lora.md‎

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- [`QwenImageLoraLoaderMixin`] provides similar functions for [Qwen Image](https://huggingface.co/docs/diffusers/main/en/api/pipelines/qwen)
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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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</Tip>
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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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## LoraBaseMixin
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‎docs/source/en/api/loaders/peft.md‎

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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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<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.
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</Tip>
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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.
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## PeftAdapterMixin
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‎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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</Tip>
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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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## 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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</Tip>
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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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## 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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</Tip>
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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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## UNet2DConditionLoadersMixin
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‎docs/source/en/api/models/consistency_decoder_vae.md‎

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The original codebase can be found at [openai/consistencydecoder](https://github.com/openai/consistencydecoder).
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<Tip warning={true}>
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Inference is only supported for 2 iterations as of now.
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</Tip>
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> [!WARNING]
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> Inference is only supported for 2 iterations as of now.
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The pipeline could not have been contributed without the help of [madebyollin](https://github.com/madebyollin) and [mrsteyk](https://github.com/mrsteyk) from [this issue](https://github.com/openai/consistencydecoder/issues/1).
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‎docs/source/en/api/models/transformer2d.md‎

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When the input is **discrete**:
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It is assumed one of the input classes is the masked latent pixel. The predicted classes of the unnoised image don't contain a prediction for the masked pixel because the unnoised image cannot be masked.
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> [!TIP]
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> It is assumed one of the input classes is the masked latent pixel. The predicted classes of the unnoised image don't contain a prediction for the masked pixel because the unnoised image cannot be masked.
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1. Convert input (classes of latent pixels) to embeddings and apply positional embeddings.
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2. Apply the Transformer blocks in the standard way.

‎docs/source/en/api/outputs.md‎

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```
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To check a specific pipeline or model output, refer to its corresponding API documentation.
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> [!TIP]
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> To check a specific pipeline or model output, refer to its corresponding API documentation.
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## BaseOutput
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