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[docs] Add a note of _keep_in_fp32_modules (#11851)

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* update

* Update docs/source/en/using-diffusers/schedulers.md

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>

* Update schedulers.md

---------

Co-authored-by: Steven Liu <59462357+stevhliu@users.noreply.github.com>
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Aryan
2025-07-03 04:21:57 +05:30
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docs/source/en/using-diffusers/schedulers.md
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@@ -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)).