# Dance Diffusion

[Dance Diffusion](https://github.com/Harmonai-org/sample-generator) is by Zach Evans.

Dance Diffusion is the first in a suite of generative audio tools for producers and musicians released by [Harmonai](https://github.com/Harmonai-org).


> [!TIP]
> Make sure to check out the Schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-a-pipeline) section to learn how to efficiently load the same components into multiple pipelines.

## DanceDiffusionPipeline[[diffusers.DanceDiffusionPipeline]]
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<docstring><name>class diffusers.DanceDiffusionPipeline</name><anchor>diffusers.DanceDiffusionPipeline</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py#L37</source><parameters>[{"name": "unet", "val": ": UNet1DModel"}, {"name": "scheduler", "val": ": SchedulerMixin"}]</parameters><paramsdesc>- **unet** ([UNet1DModel](/docs/diffusers/main/en/api/models/unet#diffusers.UNet1DModel)) --
  A `UNet1DModel` to denoise the encoded audio.
- **scheduler** ([SchedulerMixin](/docs/diffusers/main/en/api/schedulers/overview#diffusers.SchedulerMixin)) --
  A scheduler to be used in combination with `unet` to denoise the encoded audio latents. Can be one of
  [IPNDMScheduler](/docs/diffusers/main/en/api/schedulers/ipndm#diffusers.IPNDMScheduler).</paramsdesc><paramgroups>0</paramgroups></docstring>

Pipeline for audio generation.

This model inherits from [DiffusionPipeline](/docs/diffusers/main/en/api/pipelines/overview#diffusers.DiffusionPipeline). Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).





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<docstring><name>__call__</name><anchor>diffusers.DanceDiffusionPipeline.__call__</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py#L59</source><parameters>[{"name": "batch_size", "val": ": int = 1"}, {"name": "num_inference_steps", "val": ": int = 100"}, {"name": "generator", "val": ": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"}, {"name": "audio_length_in_s", "val": ": typing.Optional[float] = None"}, {"name": "return_dict", "val": ": bool = True"}]</parameters><paramsdesc>- **batch_size** (`int`, *optional*, defaults to 1) --
  The number of audio samples to generate.
- **num_inference_steps** (`int`, *optional*, defaults to 50) --
  The number of denoising steps. More denoising steps usually lead to a higher-quality audio sample at
  the expense of slower inference.
- **generator** (`torch.Generator`, *optional*) --
  A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make
  generation deterministic.
- **audio_length_in_s** (`float`, *optional*, defaults to `self.unet.config.sample_size/self.unet.config.sample_rate`) --
  The length of the generated audio sample in seconds.
- **return_dict** (`bool`, *optional*, defaults to `True`) --
  Whether or not to return a [AudioPipelineOutput](/docs/diffusers/main/en/api/pipelines/dance_diffusion#diffusers.AudioPipelineOutput) instead of a plain tuple.</paramsdesc><paramgroups>0</paramgroups><rettype>[AudioPipelineOutput](/docs/diffusers/main/en/api/pipelines/dance_diffusion#diffusers.AudioPipelineOutput) or `tuple`</rettype><retdesc>If `return_dict` is `True`, [AudioPipelineOutput](/docs/diffusers/main/en/api/pipelines/dance_diffusion#diffusers.AudioPipelineOutput) is returned, otherwise a `tuple` is
returned where the first element is a list with the generated audio.</retdesc></docstring>

The call function to the pipeline for generation.



<ExampleCodeBlock anchor="diffusers.DanceDiffusionPipeline.__call__.example">

Example:

```py
from diffusers import DiffusionPipeline
from scipy.io.wavfile import write

model_id = "harmonai/maestro-150k"
pipe = DiffusionPipeline.from_pretrained(model_id)
pipe = pipe.to("cuda")

audios = pipe(audio_length_in_s=4.0).audios

# To save locally
for i, audio in enumerate(audios):
    write(f"maestro_test_{i}.wav", pipe.unet.sample_rate, audio.transpose())

# To display in google colab
import IPython.display as ipd

for audio in audios:
    display(ipd.Audio(audio, rate=pipe.unet.sample_rate))
```

</ExampleCodeBlock>






</div></div>

## AudioPipelineOutput[[diffusers.AudioPipelineOutput]]
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<docstring><name>class diffusers.AudioPipelineOutput</name><anchor>diffusers.AudioPipelineOutput</anchor><source>https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/pipeline_utils.py#L132</source><parameters>[{"name": "audios", "val": ": ndarray"}]</parameters><paramsdesc>- **audios** (`np.ndarray`) --
  List of denoised audio samples of a NumPy array of shape `(batch_size, num_channels, sample_rate)`.</paramsdesc><paramgroups>0</paramgroups></docstring>

Output class for audio pipelines.




</div>

<EditOnGithub source="https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/pipelines/dance_diffusion.md" />