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Small update to "Next steps" section (#3443)

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Small update to "Next steps" section:

- PyTorch 2 is recommended.
- Updated improvement figures.
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Pedro Cuenca
2023-05-16 20:35:47 +02:00
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docs/source/en/stable_diffusion.mdx
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@@ -266,6 +266,6 @@ image_grid(images)
In this tutorial, you learned how to optimize a [`DiffusionPipeline`] for computational and memory efficiency as well as improving the quality of generated outputs. If you're interested in making your pipeline even faster, take a look at the following resources:
- Enable [xFormers](./optimization/xformers) memory efficient attention mechanism for faster speed and reduced memory consumption.
- Learn how in [PyTorch 2.0](./optimization/torch2.0), [`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html) can yield 2-9% faster inference speed.
- Many optimization techniques for inference are also included in this memory and speed [guide](./optimization/fp16), such as memory offloading.
- Learn how [PyTorch 2.0](./optimization/torch2.0) and [`torch.compile`](https://pytorch.org/docs/stable/generated/torch.compile.html) can yield 5 - 300% faster inference speed.
- If you can't use PyTorch 2, we recommend you install [xFormers](./optimization/xformers). Its memory-efficient attention mechanism works great with PyTorch 1.13.1 for faster speed and reduced memory consumption.
- Other optimization techniques, such as model offloading, are covered in [this guide](./optimization/fp16).