import{S as us,i as ds,s as cs,e as i,k as m,w as B,t as f,M as hs,c as t,d as n,m as _,a as o,x as G,h as u,b as p,G as s,g as h,y as A,q as j,o as I,B as x,v as ms,L as _s}from"../../../chunks/vendor-hf-doc-builder.js";import{T as gs}from"../../../chunks/Tip-hf-doc-builder.js";import{D as Re}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as vs}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as He}from"../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as ws}from"../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function bs(F){let r,$,d,c,g,a,v,T;return{c(){r=i("p"),$=f("Make sure to check out the Schedulers "),d=i("a"),c=f("guide"),g=f(" to learn how to explore the tradeoff between scheduler speed and quality, and see the "),a=i("a"),v=f("reuse components across pipelines"),T=f(" section to learn how to efficiently load the same components into multiple pipelines."),this.h()},l(k){r=t(k,"P",{});var w=o(r);$=u(w,"Make sure to check out the Schedulers "),d=t(w,"A",{href:!0});var y=o(d);c=u(y,"guide"),y.forEach(n),g=u(w," to learn how to explore the tradeoff between scheduler speed and quality, and see the "),a=t(w,"A",{href:!0});var D=o(a);v=u(D,"reuse components across pipelines"),D.forEach(n),T=u(w," section to learn how to efficiently load the same components into multiple pipelines."),w.forEach(n),this.h()},h(){p(d,"href","/using-diffusers/schedulers"),p(a,"href","/using-diffusers/loading#reuse-components-across-pipelines")},m(k,w){h(k,r,w),s(r,$),s(r,d),s(d,c),s(r,g),s(r,a),s(a,v),s(r,T)},d(k){k&&n(r)}}}function $s(F){let r,$,d,c,g;return c=new vs({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
<span class="hljs-keyword">from</span> scipy.io.wavfile <span class="hljs-keyword">import</span> write

model_id = <span class="hljs-string">&quot;harmonai/maestro-150k&quot;</span>
pipe = DiffusionPipeline.from_pretrained(model_id)
pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

audios = pipe(audio_length_in_s=<span class="hljs-number">4.0</span>).audios

<span class="hljs-comment"># To save locally</span>
<span class="hljs-keyword">for</span> i, audio <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(audios):
    write(<span class="hljs-string">f&quot;maestro_test_<span class="hljs-subst">{i}</span>.wav&quot;</span>, pipe.unet.sample_rate, audio.transpose())

<span class="hljs-comment"># To dislay in google colab</span>
<span class="hljs-keyword">import</span> IPython.display <span class="hljs-keyword">as</span> ipd

<span class="hljs-keyword">for</span> audio <span class="hljs-keyword">in</span> audios:
    display(ipd.Audio(audio, rate=pipe.unet.sample_rate))`}}),{c(){r=i("p"),$=f("Example:"),d=m(),B(c.$$.fragment)},l(a){r=t(a,"P",{});var v=o(r);$=u(v,"Example:"),v.forEach(n),d=_(a),G(c.$$.fragment,a)},m(a,v){h(a,r,v),s(r,$),h(a,d,v),A(c,a,v),g=!0},p:_s,i(a){g||(j(c.$$.fragment,a),g=!0)},o(a){I(c.$$.fragment,a),g=!1},d(a){a&&n(r),a&&n(d),x(c,a)}}}function ys(F){let r,$,d,c,g,a,v,T,k,w,y,D,De,Pe,pe,N,Te,O,ke,Me,fe,Z,Ee,z,Je,Be,ue,S,de,M,V,se,Y,Ge,ne,Ae,ce,b,C,je,ie,Ie,xe,L,Ne,K,Ze,Se,Ve,P,R,Xe,te,Ue,We,X,he,E,U,ae,H,Oe,oe,ze,me,J,q,Ye,re,Ce,_e;return a=new He({}),S=new gs({props:{$$slots:{default:[bs]},$$scope:{ctx:F}}}),Y=new He({}),C=new Re({props:{name:"class diffusers.DanceDiffusionPipeline",anchor:"diffusers.DanceDiffusionPipeline",parameters:[{name:"unet",val:""},{name:"scheduler",val:""}],parametersDescription:[{anchor:"diffusers.DanceDiffusionPipeline.unet",description:`<strong>unet</strong> (<a href="/docs/diffusers/v0.19.0/en/api/models/unet#diffusers.UNet1DModel">UNet1DModel</a>) &#x2014;
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  href="/docs/diffusers/v0.19.0/en/api/pipelines/dance_diffusion#diffusers.AudioPipelineOutput"
>AudioPipelineOutput</a> is returned, otherwise a <code>tuple</code> is
returned where the first element is a list with the generated audio.</p>
`,returnType:`
<p><a
  href="/docs/diffusers/v0.19.0/en/api/pipelines/dance_diffusion#diffusers.AudioPipelineOutput"
>AudioPipelineOutput</a> or <code>tuple</code></p>
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List of denoised audio samples of a NumPy array of shape <code>(batch_size, num_channels, sample_rate)</code>.`,name:"audios"}],source:"https://github.com/huggingface/diffusers/blob/v0.19.0/src/diffusers/pipelines/pipeline_utils.py#L127"}}),{c(){r=i("meta"),$=m(),d=i("h1"),c=i("a"),g=i("span"),B(a.$$.fragment),v=m(),T=i("span"),k=f("Dance Diffusion"),w=m(),y=i("p"),D=i("a"),De=f("Dance Diffusion"),Pe=f(" is by Zach Evans."),pe=m(),N=i("p"),Te=f("Dance Diffusion is the first in a suite of generative audio tools for producers and musicians released by "),O=i("a"),ke=f("Harmonai"),Me=f("."),fe=m(),Z=i("p"),Ee=f("The original codebase of this implementation can be found at "),z=i("a"),Je=f("Harmonai-org"),Be=f("."),ue=m(),B(S.$$.fragment),de=m(),M=i("h2"),V=i("a"),se=i("span"),B(Y.$$.fragment),Ge=m(),ne=i("span"),Ae=f("DanceDiffusionPipeline"),ce=m(),b=i("div"),B(C.$$.fragment),je=m(),ie=i("p"),Ie=f("Pipeline for audio generation."),xe=m(),L=i("p"),Ne=f("This model inherits from "),K=i("a"),Ze=f("DiffusionPipeline"),Se=f(`. Check the superclass documentation for the generic methods
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