diffusers/pr_13803/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]]

diffusers.AutoencoderKLLTX2Audio[[diffusers.AutoencoderKLLTX2Audio]]

Source

LTX2 audio VAE for encoding and decoding audio latent representations.

encodediffusers.AutoencoderKLLTX2Audio.encodehttps://github.com/huggingface/diffusers/blob/vr_13803/src/diffusers/models/autoencoders/autoencoder_kl_ltx2_audio.py#L759[{"name": "x", "val": ": Tensor"}, {"name": "return_dict", "val": ": bool = True"}]

decode[[diffusers.AutoencoderKLLTX2Audio.decode]]

Source

forward[[diffusers.AutoencoderKLLTX2Audio.forward]]

Source

Parameters:

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.