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docs/source/en/optimization/attention_backends.md

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Diffusers provides several optimized attention algorithms that are more memory and computationally efficient through it's *attention dispatcher*. The dispatcher acts as a router for managing and switching between different attention implementations and provides a unified interface for interacting with them.
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Available attention implementations include the following.
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Refer to the table below for an overview of the available attention families and to the [Available backends](#available-backends) section for a more complete list.
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| attention family | main feature |
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The example below demonstrates how to enable the `_flash_3_hub` implementation for FlashAttention-3 from the [kernel](https://github.com/huggingface/kernels) library, which allows you to instantly use optimized compute kernels from the Hub without requiring any setup.
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> [!TIP]
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> FlashAttention-3 is not supported for non-Hopper architectures, in which case, use FlashAttention (set_attention_backend("flash")).
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> FlashAttention-3 is not supported for non-Hopper architectures, in which case, use FlashAttention with `set_attention_backend("flash")`.
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```py
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import torch

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