# AutoencoderKLLTX2Audio

[LTX-2](https://huggingface.co/Lightricks/LTX-2) 中使用的带 KL 损失的 3D 变分自编码器（VAE）模型由 Lightricks 提出。该模型用于音频潜在表示的编码和解码。

可以使用以下代码片段加载该模型。

```python
from diffusers import AutoencoderKLLTX2Audio

vae = AutoencoderKLLTX2Audio.from_pretrained("Lightricks/LTX-2", subfolder="vae", dtype=torch.float32).to("cuda")  # or "mps", "xpu", "cpu"
```

## AutoencoderKLLTX2Audio[[diffusers.AutoencoderKLLTX2Audio]]

#### diffusers.AutoencoderKLLTX2Audio[[diffusers.AutoencoderKLLTX2Audio]]

```python
diffusers.AutoencoderKLLTX2Audio(base_channels: int = 128, output_channels: int = 2, ch_mult: tuple = (1, 2, 4), num_res_blocks: int = 2, attn_resolutions: tuple[int, ...] | None = None, in_channels: int = 2, resolution: int = 256, latent_channels: int = 8, norm_type: str = 'pixel', causality_axis: str | None = 'height', dropout: float = 0.0, mid_block_add_attention: bool = False, sample_rate: int = 16000, mel_hop_length: int = 160, is_causal: bool = True, mel_bins: int | None = 64, double_z: bool = True)
```

[源代码](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/autoencoders/autoencoder_kl_ltx2_audio.py#L668)

LTX2 音频 VAE，用于编码和解码音频潜在表示。

#### encode[[diffusers.AutoencoderKLLTX2Audio.encode]]

```python
encode(x: Tensor, return_dict: bool = True)
```

[源代码](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/autoencoders/autoencoder_kl_ltx2_audio.py#L759)

#### decode[[diffusers.AutoencoderKLLTX2Audio.decode]]

```python
decode(z: Tensor, return_dict: bool = True)
```

[源代码](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/autoencoders/autoencoder_kl_ltx2_audio.py#L775)

#### forward[[diffusers.AutoencoderKLLTX2Audio.forward]]

```python
forward(sample: Tensor, sample_posterior: bool = False, return_dict: bool = True, generator: typing.Optional[torch.Generator] = None)
```

[源代码](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/autoencoders/autoencoder_kl_ltx2_audio.py#L788)

**参数：**

sample (`torch.Tensor`) : 输入样本。

sample_posterior (`bool`，*可选*，默认为 `False`)：是否从后验分布中采样。

return_dict (`bool`，*可选*，默认为 `True`)：是否返回 `DecoderOutput` 而不是普通元组。

generator (`torch.Generator`，*可选*)：一个 [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html)，用于使采样过程具有确定性。

**返回：** `DecoderOutput` 或 `tuple`

如果 `return_dict` 为 True，则返回 `DecoderOutput`，否则返回普通的 `tuple`。