> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nemo/automodel/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nemo/automodel/_mcp/server.

# nemo_automodel.components.models.hunyuan_image3.release

The release's VAE and prompt format, loaded from the checkpoint's remote code.

The release ships its VAE, tokenizer wrapper and image processor as remote code inside the checkpoint
(`trust_remote_code`); they are not vendored here. Preprocessing and sampling both go through this module, so the
latents and token sequences a model is trained on are the ones it is sampled with.

## Module Contents

### Classes

| Name                                                                                                                    | Description                                                              |
| ----------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------ |
| [`HunyuanImage3PromptTokenizer`](#nemo_automodel-components-models-hunyuan_image3-release-HunyuanImage3PromptTokenizer) | Token ids of the release's text-to-image sequence around the image span. |

### Functions

| Name                                                                                            | Description                                                                              |
| ----------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------- |
| [`load_release_vae`](#nemo_automodel-components-models-hunyuan_image3-release-load_release_vae) | Build the release VAE (`AutoencoderKLConv3D`) and load the checkpoint's `vae.*` weights. |

### API

```python
class nemo_automodel.components.models.hunyuan_image3.release.HunyuanImage3PromptTokenizer(
    wrapper: typing.Any,
    image_processor: typing.Any,
    image_base_size: int,
    sequence_template: str
)
```

Token ids of the release's text-to-image sequence around the image span.

**Parameters:**

**`wrapper`** `Any`

The release `TokenizerWrapper`.

---

**`image_processor`** `Any`

The release `HunyuanImage3ImageProcessor` (resolution group and image token grid).

---

**`image_base_size`** `int`

`image_base_size` of the checkpoint config.

---

**`sequence_template`** `str`

`sequence_template` of the checkpoint's generation config.

---

```python
nemo_automodel.components.models.hunyuan_image3.release.HunyuanImage3PromptTokenizer.__call__(
    prompt: str,
    height: int,
    width: int
) -> dict[str, torch.Tensor]
```

Return the token ids around the image span for one prompt and image size.

The sequence is the release's `gen_image` chat template with classifier-free guidance (`bot_task` auto,
no system prompt), which is also what its `generate_image` samples with.

**Returns:** `dict[str, torch.Tensor]`

`prompt_input_ids` / `uncond_prompt_input_ids`: 1D long ids before the image span, ending in

```python
nemo_automodel.components.models.hunyuan_image3.release.HunyuanImage3PromptTokenizer.from_pretrained(
    model_dir: str,
    config: typing.Any
) -> nemo_automodel.components.models.hunyuan_image3.release.HunyuanImage3PromptTokenizer
```

classmethod

Load the release tokenizer wrapper and image processor from `model_dir`.

**Parameters:**

**`model_dir`** `str`

Local checkpoint directory.

---

**`config`** `Any`

The checkpoint config (needs `image_base_size`).

---

```python
nemo_automodel.components.models.hunyuan_image3.release.HunyuanImage3PromptTokenizer.target_size(
    width: int,
    height: int
) -> tuple[int, int]
```

Snap `width` x `height` to the release's resolution group (33 ratios around `image_base_size`).

**Returns:** `tuple[int, int]`

`(width, height)` of the closest size the release generates; its `<img_ratio_*>` token names it.

```python
nemo_automodel.components.models.hunyuan_image3.release.load_release_vae(
    model_dir: str,
    vae_config: dict[str, typing.Any],
    device: str | torch.device
) -> torch.nn.Module
```

Build the release VAE (`AutoencoderKLConv3D`) and load the checkpoint's `vae.*` weights.

**Parameters:**

**`model_dir`** `str`

Local checkpoint directory.

---

**`vae_config`** `dict[str, Any]`

The `vae` section of the checkpoint config.

---

**`device`** `str | torch.device`

Device for the returned VAE.

---

**Returns:** `torch.nn.Module`

The VAE in fp32 and eval mode.