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Apr 2, 2025
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[docs] torch_dtype map #11194

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17 changes: 17 additions & 0 deletions docs/source/en/using-diffusers/loading.md
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Expand Up @@ -95,6 +95,23 @@ Use the Space below to gauge a pipeline's memory requirements before you downloa
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### Specifying Component-Specific Data Types

You can customize the data types for individual sub-models by passing a dictionary to the `torch_dtype` parameter. This allows you to load different components of a pipeline in different floating point precisions. For instance, if you want to load the transformer with `torch.bfloat16` and all other components with `torch.float16`, you can pass a dictionary mapping:

```python
from diffusers import HunyuanVideoPipeline
import torch

pipe = HunyuanVideoPipeline.from_pretrained(
"hunyuanvideo-community/HunyuanVideo",
torch_dtype={'transformer': torch.bfloat16, 'default': torch.float16},
)
print(pipe.transformer.dtype, pipe.vae.dtype) # (torch.bfloat16, torch.float16)
```

If a component is not explicitly specified in the dictionary and no `default` is provided, it will be loaded with `torch.float32`.

### Local pipeline

To load a pipeline locally, use [git-lfs](https://git-lfs.github.com/) to manually download a checkpoint to your local disk.
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