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[docs] Add a note of _keep_in_fp32_modules #11851

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Jul 2, 2025
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[docs] Add a note of _keep_in_fp32_modules #11851

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a-r-r-o-w Jul 2, 2025
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Update docs/source/en/using-diffusers/schedulers.md
a-r-r-o-w Jul 2, 2025
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Update schedulers.md
stevhliu Jul 2, 2025
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12 changes: 12 additions & 0 deletions docs/source/en/using-diffusers/schedulers.md
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Expand Up @@ -242,3 +242,15 @@ unet = UNet2DConditionModel.from_pretrained(
)
unet.save_pretrained("./local-unet", variant="non_ema")
```

Use the `torch_dtype` argument in [`~ModelMixin.from_pretrained`] to specify the dtype to load a model in.

```py
from diffusers import AutoModel

unet = AutoModel.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", subfolder="unet", torch_dtype=torch.float16
)
```

You can also use the [torch.Tensor.to](https://docs.pytorch.org/docs/stable/generated/torch.Tensor.to.html) method to convert to the specified dtype on the fly. It converts *all* weights unlike the `torch_dtype` argument that respects the `_keep_in_fp32_modules`. This is important for models whose layers must remain in fp32 for numerical stability and best generation quality (see example [here](https://github.com/huggingface/diffusers/blob/f864a9a352fa4a220d860bfdd1782e3e5af96382/src/diffusers/models/transformers/transformer_wan.py#L374)).