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# AutoModel

The [`AutoModel`] class automatically detects and loads the correct model class (UNet, transformer, VAE) from a `config.json` file. You don't need to know the specific model class name ahead of time. It supports data types and device placement, and works across model types and libraries.

The example below loads a transformer from Diffusers and a text encoder from Transformers. Use the `subfolder` parameter to specify where to load the `config.json` file from.

```py
import torch
from diffusers import AutoModel, DiffusionPipeline

transformer = AutoModel.from_pretrained(
    "Qwen/Qwen-Image", subfolder="transformer", torch_dtype=torch.bfloat16, device_map="cuda"
)

text_encoder = AutoModel.from_pretrained(
    "Qwen/Qwen-Image", subfolder="text_encoder", torch_dtype=torch.bfloat16, device_map="cuda"
)
```

## Custom models

[`AutoModel`] also loads models from the [Hub](https://huggingface.co/models) that aren't included in Diffusers. Set `trust_remote_code=True` in [`AutoModel.from_pretrained`] to load custom models.

A custom model repository needs a Python module with the model class, and a `config.json` with an `auto_map` entry that maps `"AutoModel"` to `"module_file.ClassName"`.

```
custom/custom-transformer-model/
├── config.json
├── my_model.py
└── diffusion_pytorch_model.safetensors
```

The `config.json` includes the `auto_map` field pointing to the custom class.

```json
{
  "auto_map": {
    "AutoModel": "my_model.MyCustomModel"
  }
}
```

Then load it with `trust_remote_code=True`.

```py
import torch
from diffusers import AutoModel

transformer = AutoModel.from_pretrained(
    "custom/custom-transformer-model", trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="cuda"
)
```

For a real-world example, [Overworld/Waypoint-1-Small](https://huggingface.co/Overworld/Waypoint-1-Small/tree/main/transformer) hosts a custom `WorldModel` class across several modules in its `transformer` subfolder.

```
transformer/
├── config.json          # auto_map: "model.WorldModel"
├── model.py
├── attn.py
├── nn.py
├── cache.py
├── quantize.py
├── __init__.py
└── diffusion_pytorch_model.safetensors
```

```py
import torch
from diffusers import AutoModel

transformer = AutoModel.from_pretrained(
    "Overworld/Waypoint-1-Small", subfolder="transformer", trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="cuda"
)
```

If the custom model inherits from the [`ModelMixin`] class, it gets access to the same features as Diffusers model classes, like [regional compilation](../optimization/fp16#regional-compilation) and [group offloading](../optimization/memory#group-offloading).

> [!WARNING]
> As a precaution with `trust_remote_code=True`, pass a commit hash to the `revision` argument in [`AutoModel.from_pretrained`] to make sure the code hasn't been updated with new malicious code (unless you fully trust the model owners).
>
> ```py
> transformer = AutoModel.from_pretrained(
>     "Overworld/Waypoint-1-Small", subfolder="transformer", trust_remote_code=True, revision="a3d8cb2"
> )
> ```

### Saving custom models

Use [`~ConfigMixin.register_for_auto_class`] to add the `auto_map` entry to `config.json` automatically when saving. This avoids having to manually edit the config file.

```py
# my_model.py
from diffusers import ModelMixin, ConfigMixin

class MyCustomModel(ModelMixin, ConfigMixin):
    ...

MyCustomModel.register_for_auto_class("AutoModel")

model = MyCustomModel(...)
model.save_pretrained("./my_model")
```

The saved `config.json` will include the `auto_map` field.

```json
{
  "auto_map": {
    "AutoModel": "my_model.MyCustomModel"
  }
}
```

> [!NOTE]
> Learn more about implementing custom models in the [Community components](../using-diffusers/custom_pipeline_overview#community-components) guide.