diffusers/pr_13966/en/api/models/autoencoderkl_audio_ltx_2.md

AutoencoderKLLTX2Audio

The 3D variational autoencoder (VAE) model with KL loss used in 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.

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 to make sampling deterministic.DecoderOutput or tupleIf return_dict is True, a DecoderOutput is returned, otherwise a plain tuple is returned.