# AutoencoderKLLTX2Audio

The 3D variational autoencoder (VAE) model with KL loss used in [LTX-2](https://huggingface.co/Lightricks/LTX-2) was introduced by Lightricks. This is for encoding and decoding audio latent representations.

The model can be loaded with the following code snippet.

```python
from diffusers import AutoencoderKLLTX2Audio

vae = AutoencoderKLLTX2Audio.from_pretrained("Lightricks/LTX-2", subfolder="vae", torch_dtype=torch.float32).to("cuda")
```

## AutoencoderKLLTX2Audio[[diffusers.AutoencoderKLLTX2Audio]]

LTX2 audio VAE for encoding and decoding audio latent representations.

- **sample** (`torch.Tensor`) -- Input sample.
- **sample_posterior** (`bool`, *optional*, defaults to `False`) --
  Whether to sample from the posterior.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a `DecoderOutput` instead of a plain tuple.
- **generator** (`torch.Generator`, *optional*) --
  A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make sampling
  deterministic.`DecoderOutput` or `tuple`If `return_dict` is True, a `DecoderOutput` is returned, otherwise a plain `tuple` is returned.