Yoach Lacombe
69e72b1dd1
* WIP modeling code and pipeline * add custom attention processor + custom activation + add to init * correct ProjectionModel forward * add stable audio to __initèè * add autoencoder and update pipeline and modeling code * add half Rope * add partial rotary v2 * add temporary modfis to scheduler * add EDM DPM Solver * remove TODOs * clean GLU * remove att.group_norm to attn processor * revert back src/diffusers/schedulers/scheduling_dpmsolver_multistep.py * refactor GLU -> SwiGLU * remove redundant args * add channel multiples in autoencoder docstrings * changes in docsrtings and copyright headers * clean pipeline * further cleaning * remove peft and lora and fromoriginalmodel * Delete src/diffusers/pipelines/stable_audio/diffusers.code-workspace * make style * dummy models * fix copied from * add fast oobleck tests * add brownian tree * oobleck autoencoder slow tests * remove TODO * fast stable audio pipeline tests * add slow tests * make style * add first version of docs * wrap is_torchsde_available to the scheduler * fix slow test * test with input waveform * add input waveform * remove some todos * create stableaudio gaussian projection + make style * add pipeline to toctree * fix copied from * make quality * refactor timestep_features->time_proj * refactor joint_attention_kwargs->cross_attention_kwargs * remove forward_chunk * move StableAudioDitModel to transformers folder * correct convert + remove partial rotary embed * apply suggestions from yiyixuxu -> removing attn.kv_heads * remove temb * remove cross_attention_kwargs * further removal of cross_attention_kwargs * remove text encoder autocast to fp16 * continue removing autocast * make style * refactor how text and audio are embedded * add paper * update example code * make style * unify projection model forward + fix device placement * make style * remove fuse qkv * apply suggestions from review * Update src/diffusers/pipelines/stable_audio/pipeline_stable_audio.py Co-authored-by: YiYi Xu <yixu310@gmail.com> * make style * smaller models in fast tests * pass sequential offloading fast tests * add docs for vae and autoencoder * make style and update example * remove useless import * add cosine scheduler * dummy classes * cosine scheduler docs * better description of scheduler --------- Co-authored-by: YiYi Xu <yixu310@gmail.com>
39 lines
2.0 KiB
Markdown
39 lines
2.0 KiB
Markdown
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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# AutoencoderOobleck
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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.
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The abstract from the paper is:
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*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.*
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## AutoencoderOobleck
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[[autodoc]] AutoencoderOobleck
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- decode
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- encode
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- all
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## OobleckDecoderOutput
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[[autodoc]] models.autoencoders.autoencoder_oobleck.OobleckDecoderOutput
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## OobleckDecoderOutput
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[[autodoc]] models.autoencoders.autoencoder_oobleck.OobleckDecoderOutput
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## AutoencoderOobleckOutput
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[[autodoc]] models.autoencoders.autoencoder_oobleck.AutoencoderOobleckOutput
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