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# Attention backends
> [!TIP]
> The attention dispatcher is an experimental feature. Please open an issue if you have any feedback or encounter any problems.
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.
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.
| attention family | main feature |
|---|---|
| FlashAttention | minimizes memory reads/writes through tiling and recomputation |
| SageAttention | quantizes attention to int8 |
| PyTorch native | built-in PyTorch implementation using [scaled_dot_product_attention](./fp16#scaled-dot-product-attention) |
| xFormers | memory-efficient attention with support for various attention kernels |
This guide will show you how to set and use the different attention backends.
## set_attention_backend
The [`~ModelMixin.set_attention_backend`] method iterates through all the modules in the model and sets the appropriate attention backend to use. The attention backend setting persists until [`~ModelMixin.reset_attention_backend`] is called.
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.
> [!TIP]
> FlashAttention-3 is not supported for non-Hopper architectures, in which case, use FlashAttention with `set_attention_backend("flash")`.
cinematic film still of a cat sipping a margarita in a pool in Palm Springs, California
highly detailed, high budget hollywood movie, cinemascope, moody, epic, gorgeous, film grain
"""
pipeline(prompt).images[0]
```
To restore the default attention backend, call [`~ModelMixin.reset_attention_backend`].
```py
pipeline.transformer.reset_attention_backend()
```
## attention_backend context manager
The [attention_backend](https://github.com/huggingface/diffusers/blob/5e181eddfe7e44c1444a2511b0d8e21d177850a0/src/diffusers/models/attention_dispatch.py#L225) context manager temporarily sets an attention backend for a model within the context. Outside the context, the default attention (PyTorch's native scaled dot product attention) is used. This is useful if you want to use different backends for different parts of a pipeline or if you want to test the different backends.
