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.github/workflows/pr_modular_tests.yml

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run: |
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python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
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python -m uv pip install -e [quality,test]
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pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
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pip uninstall transformers -y && pip uninstall huggingface_hub -y && python -m uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
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pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
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- name: Environment

.github/workflows/pr_tests.yml

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run: |
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python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
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python -m uv pip install -e [quality,test]
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pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
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pip uninstall transformers -y && pip uninstall huggingface_hub -y && python -m uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
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pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
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python -m uv pip install -e [quality,test]
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# TODO (sayakpaul, DN6): revisit `--no-deps`
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python -m pip install -U peft@git+https://github.com/huggingface/peft.git --no-deps
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python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
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python -m uv pip install -U tokenizers
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pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git --no-deps
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pip uninstall transformers -y && pip uninstall huggingface_hub -y && python -m uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
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- name: Environment
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.github/workflows/pr_tests_gpu.yml

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python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
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python -m uv pip install -e [quality,test]
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pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
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pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
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pip uninstall transformers -y && pip uninstall huggingface_hub -y && python -m uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
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- name: Environment
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python -m uv pip install -e [quality,test]
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python -m uv pip install peft@git+https://github.com/huggingface/peft.git
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pip uninstall accelerate -y && python -m uv pip install -U accelerate@git+https://github.com/huggingface/accelerate.git
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pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
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pip uninstall transformers -y && pip uninstall huggingface_hub -y && python -m uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
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- name: Environment
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- name: Install dependencies
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python -m venv /opt/venv && export PATH="/opt/venv/bin:$PATH"
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pip uninstall transformers -y && python -m uv pip install -U transformers@git+https://github.com/huggingface/transformers.git --no-deps
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pip uninstall transformers -y && pip uninstall huggingface_hub -y && python -m uv pip install --prerelease allow -U transformers@git+https://github.com/huggingface/transformers.git
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python -m uv pip install -e [quality,test,training]
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- name: Environment

docs/source/en/_toctree.yml

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- local: using-diffusers/schedulers
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title: Schedulers
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- local: using-diffusers/other-formats
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title: Model files and layouts
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title: Model formats
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- local: using-diffusers/push_to_hub
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title: Sharing pipelines and models
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title: Reduce memory usage
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- local: optimization/speed-memory-optims
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title: Compiling and offloading quantized models
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- local: api/parallel
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title: Parallel inference
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- title: Community optimizations
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sections:
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- local: optimization/pruna
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title: Outputs
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- local: api/quantization
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title: Quantization
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- local: api/parallel
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title: Parallel inference
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- title: Modular
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- local: api/modular_diffusers/pipeline

docs/source/en/api/configuration.md

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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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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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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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> 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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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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## 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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> 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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