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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
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