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Update torch2.0.mdx (huggingface#2748)
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docs/source/en/optimization/torch2.0.mdx

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@@ -89,7 +89,7 @@ pip install --pre torch torchvision --index-url https://download.pytorch.org/whl
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## Benchmark
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We conducted a simple benchmark on different GPUs to compare vanilla attention, xFormers, `torch.nn.functional.scaled_dot_product_attention` and `torch.compile+torch.nn.functional.scaled_dot_product_attention`.
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For the benchmark we used the the [stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) model with 50 steps. The `xFormers` benchmark is done using the `torch==1.13.1` version, while the accelerated transformers optimizations are tested using nightly versions of PyTorch 2.0. The tables below summarize the results we got.
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For the benchmark we used the [stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) model with 50 steps. The `xFormers` benchmark is done using the `torch==1.13.1` version, while the accelerated transformers optimizations are tested using nightly versions of PyTorch 2.0. The tables below summarize the results we got.
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The `Speed over xformers` columns denote the speed-up gained over `xFormers` using the `torch.compile+torch.nn.functional.scaled_dot_product_attention`.
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(1) Batch Size >= 32 requires enable_vae_slicing() because of https://github.com/pytorch/pytorch/issues/81665
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This is required for PyTorch 1.13.1, and also for PyTorch 2.0 and batch size of 64
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(1) Batch Size >= 32 requires enable_vae_slicing() because of https://github.com/pytorch/pytorch/issues/81665.
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This is required for PyTorch 1.13.1, and also for PyTorch 2.0 and batch size of 64.
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For more details about how this benchmark was run, please refer to [this PR](https://github.com/huggingface/diffusers/pull/2303).
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For more details about how this benchmark was run, please refer to [this PR](https://github.com/huggingface/diffusers/pull/2303).

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