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# nemo_automodel.components.flow_matching.adapters.simple

Simple transformer model adapter for FlowMatching Pipeline.

This adapter supports simple transformer models with a basic interface,
such as Wan-style models.

## Module Contents

### Classes

| Name                                                                                      | Description                                              |
| ----------------------------------------------------------------------------------------- | -------------------------------------------------------- |
| [`SimpleAdapter`](#nemo_automodel-components-flow_matching-adapters-simple-SimpleAdapter) | Model adapter for simple transformer models (e.g., Wan). |

### API

```python
class nemo_automodel.components.flow_matching.adapters.simple.SimpleAdapter()
```

**Bases:** [ModelAdapter](/nemo-automodel/nemo_automodel/components/flow_matching/adapters/base#nemo_automodel-components-flow_matching-adapters-base-ModelAdapter)

Model adapter for simple transformer models (e.g., Wan).

These models use a simple interface with:

* hidden\_states: noisy latents
* timestep: timestep values
* encoder\_hidden\_states: text embeddings

Expected batch keys:

* text\_embeddings: Text encoder output \[B, seq\_len, dim]

```python
nemo_automodel.components.flow_matching.adapters.simple.SimpleAdapter.forward(
    model: torch.nn.Module,
    inputs: typing.Dict[str, typing.Any]
) -> torch.Tensor
```

Execute forward pass for simple transformer model.

**Parameters:**

Transformer model

Dictionary from prepare\_inputs()

**Returns:** `torch.Tensor`

Model prediction tensor

```python
nemo_automodel.components.flow_matching.adapters.simple.SimpleAdapter.prepare_inputs(
    context: nemo_automodel.components.flow_matching.adapters.base.FlowMatchingContext
) -> typing.Dict[str, typing.Any]
```

Prepare inputs for simple transformer model.

**Parameters:**

FlowMatchingContext with batch data

**Returns:** `Dict[str, Any]`

Dictionary containing: