diffusers/docs/source/en/api/models/autoencoder_oobleck.md

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# AutoencoderOobleck

The Oobleck variational autoencoder (VAE) model with KL loss was introduced in [Stability-AI/stable-audio-tools](https://github.com/Stability-AI/stable-audio-tools) and [Stable Audio Open](https://huggingface.co/papers/2407.14358) by Stability AI. The model is used in 🤗 Diffusers to encode audio waveforms into latents and to decode latent representations into audio waveforms.

The abstract from the paper is:

*Open generative models are vitally important for the community, allowing for fine-tunes and serving as baselines when presenting new models. However, most current text-to-audio models are private and not accessible for artists and researchers to build upon. Here we describe the architecture and training process of a new open-weights text-to-audio model trained with Creative Commons data. Our evaluation shows that the model's performance is competitive with the state-of-the-art across various metrics. Notably, the reported FDopenl3 results (measuring the realism of the generations) showcase its potential for high-quality stereo sound synthesis at 44.1kHz.*

## AutoencoderOobleck

[[autodoc]] AutoencoderOobleck
    - decode
    - encode
    - all

## OobleckDecoderOutput

[[autodoc]] models.autoencoders.autoencoder_oobleck.OobleckDecoderOutput

## OobleckDecoderOutput

[[autodoc]] models.autoencoders.autoencoder_oobleck.OobleckDecoderOutput

## AutoencoderOobleckOutput

[[autodoc]] models.autoencoders.autoencoder_oobleck.AutoencoderOobleckOutput