add more benchmark numbers #2900
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sayakpaul
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Jun 10, 2025
diffusers-quantization.md
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| @@ -450,7 +450,7 @@ For more information check out the [Layerwise casting docs](https://huggingface. | |||
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| Most of these quantization backends can be combined with the memory optimization techniques offered in Diffusers. Let's explore CPU offloading, group offloading, and `torch.compile`. You can learn more about these techniques in the [Diffusers documentation](https://huggingface.co/docs/diffusers/main/en/optimization/memory). | |||
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| > **Note:** At the time of writing, bnb + `torch.compile` also works if bnb is installed from source and using pytorch nightly or with fullgraph=False. | |||
| > **Note:** At the time of writing, bnb + `torch.compile` works if bnb is installed from source and using pytorch nightly or with fullgraph=False. | |||
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https://github.com/bitsandbytes-foundation/bitsandbytes/releases/tag/0.46.0 is done so bitsandbytes==0.46.0 should work with PyTorch nightly.
diffusers-quantization.md
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| @@ -565,6 +592,13 @@ pipe = FluxPipeline.from_pretrained( | |||
| | int8_weight_only | 17.020 GB | 22.473 GB | 8 seconds | ~851 seconds | | |||
| | float8_weight_only | 17.016 GB | 22.115 GB | 8 seconds | ~545 seconds | | |||
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| **bitsandbytes + `torch.compile`**: **Note:** To enable compatibility with torch.compile, make sure you're using the latest version of bitsandbytes and PyTorch nightlies (2.8) | |||
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Mention the hardware this was obtained on.
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Not sure what hardware this was obtained on? was it A100?
Co-authored-by: Sayak Paul <spsayakpaul@gmail.com>
Hmm, I'm not sure. RTX 4090 is probably more common for developers, but could make blogpost more complex |
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In the diffusion world, RTX 4090 is more common than A100, H100, etc., actually. But okay without. |
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