import{s as me,o as he,n as ce}from"../chunks/scheduler.182ea377.js";import{S as _e,i as ge,g as d,s as o,r as v,A as $e,h as u,f as t,c as l,j as A,u as T,x as k,k as S,y as m,a,v as y,d as M,t as D,w as P}from"../chunks/index.abf12888.js";import{T as be}from"../chunks/Tip.230e2334.js";import{D as ae}from"../chunks/Docstring.93f6f462.js";import{C as we}from"../chunks/CodeBlock.57fe6e13.js";import{E as ve}from"../chunks/ExampleCodeBlock.658f5cd6.js";import{H as oe}from"../chunks/Heading.16916d63.js";function Te(V){let s,g='Make sure to check out the Schedulers <a href="../../using-diffusers/schedulers">guide</a> to learn how to explore the tradeoff between scheduler speed and quality, and see the <a href="../../using-diffusers/loading#reuse-components-across-pipelines">reuse components across pipelines</a> section to learn how to efficiently load the same components into multiple pipelines.';return{c(){s=d("p"),s.innerHTML=g},l(r){s=u(r,"P",{"data-svelte-h":!0}),k(s)!=="svelte-1wmc0l4"&&(s.innerHTML=g)},m(r,p){a(r,s,p)},p:ce,d(r){r&&t(s)}}}function ye(V){let s,g="Example:",r,p,c;return p=new we({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))`,wrap:!1}}),{c(){s=d("p"),s.textContent=g,r=o(),v(p.$$.fragment)},l(i){s=u(i,"P",{"data-svelte-h":!0}),k(s)!=="svelte-11lpom8"&&(s.textContent=g),r=l(i),T(p.$$.fragment,i)},m(i,h){a(i,s,h),a(i,r,h),y(p,i,h),c=!0},p:ce,i(i){c||(M(p.$$.fragment,i),c=!0)},o(i){D(p.$$.fragment,i),c=!1},d(i){i&&(t(s),t(r)),P(p,i)}}}function Me(V){let s,g,r,p,c,i,h,le='<a href="https://github.com/Harmonai-org/sample-generator" rel="nofollow">Dance Diffusion</a> is by Zach Evans.',z,B,re='Dance Diffusion is the first in a suite of generative audio tools for producers and musicians released by <a href="https://github.com/Harmonai-org" rel="nofollow">Harmonai</a>.',L,J,H,j,O,f,G,K,X,pe="Pipeline for audio generation.",ee,E,de=`This model inherits from <a href="/docs/diffusers/main/en/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a>. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, running on a particular device, etc.).`,ne,$,I,te,U,ue="The call function to the pipeline for generation.",se,x,Y,Z,R,b,C,ie,W,fe="Output class for audio pipelines.",Q,N,q;return c=new oe({props:{title:"Dance Diffusion",local:"dance-diffusion",headingTag:"h1"}}),J=new be({props:{$$slots:{default:[Te]},$$scope:{ctx:V}}}),j=new oe({props:{title:"DanceDiffusionPipeline",local:"diffusers.DanceDiffusionPipeline",headingTag:"h2"}}),G=new ae({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/main/en/api/models/unet#diffusers.UNet1DModel">UNet1DModel</a>) &#x2014;
A <code>UNet1DModel</code> to denoise the encoded audio.`,name:"unet"},{anchor:"diffusers.DanceDiffusionPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/main/en/api/schedulers/overview#diffusers.SchedulerMixin">SchedulerMixin</a>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to denoise the encoded audio latents. Can be one of
<a href="/docs/diffusers/main/en/api/schedulers/ipndm#diffusers.IPNDMScheduler">IPNDMScheduler</a>.`,name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py#L28"}}),I=new ae({props:{name:"__call__",anchor:"diffusers.DanceDiffusionPipeline.__call__",parameters:[{name:"batch_size",val:": int = 1"},{name:"num_inference_steps",val:": int = 100"},{name:"generator",val:": Union = None"},{name:"audio_length_in_s",val:": Optional = None"},{name:"return_dict",val:": bool = True"}],parametersDescription:[{anchor:"diffusers.DanceDiffusionPipeline.__call__.batch_size",description:`<strong>batch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The number of audio samples to generate.`,name:"batch_size"},{anchor:"diffusers.DanceDiffusionPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 50) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher-quality audio sample at
the expense of slower inference.`,name:"num_inference_steps"},{anchor:"diffusers.DanceDiffusionPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code>, <em>optional</em>) &#x2014;
A <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow"><code>torch.Generator</code></a> to make
generation deterministic.`,name:"generator"},{anchor:"diffusers.DanceDiffusionPipeline.__call__.audio_length_in_s",description:`<strong>audio_length_in_s</strong> (<code>float</code>, <em>optional</em>, defaults to <code>self.unet.config.sample_size/self.unet.config.sample_rate</code>) &#x2014;
The length of the generated audio sample in seconds.`,name:"audio_length_in_s"},{anchor:"diffusers.DanceDiffusionPipeline.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to return a <a href="/docs/diffusers/main/en/api/pipelines/dance_diffusion#diffusers.AudioPipelineOutput">AudioPipelineOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/dance_diffusion/pipeline_dance_diffusion.py#L49",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If <code>return_dict</code> is <code>True</code>, <a
  href="/docs/diffusers/main/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:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/diffusers/main/en/api/pipelines/dance_diffusion#diffusers.AudioPipelineOutput"
>AudioPipelineOutput</a> or <code>tuple</code></p>
`}}),x=new ve({props:{anchor:"diffusers.DanceDiffusionPipeline.__call__.example",$$slots:{default:[ye]},$$scope:{ctx:V}}}),Z=new oe({props:{title:"AudioPipelineOutput",local:"diffusers.AudioPipelineOutput",headingTag:"h2"}}),C=new ae({props:{name:"class diffusers.AudioPipelineOutput",anchor:"diffusers.AudioPipelineOutput",parameters:[{name:"audios",val:": ndarray"}],parametersDescription:[{anchor:"diffusers.AudioPipelineOutput.audios",description:`<strong>audios</strong> (<code>np.ndarray</code>) &#x2014;
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/main/src/diffusers/pipelines/pipeline_utils.py#L114"}}),{c(){s=d("meta"),g=o(),r=d("p"),p=o(),v(c.$$.fragment),i=o(),h=d("p"),h.innerHTML=le,z=o(),B=d("p"),B.innerHTML=re,L=o(),v(J.$$.fragment),H=o(),v(j.$$.fragment),O=o(),f=d("div"),v(G.$$.fragment),K=o(),X=d("p"),X.textContent=pe,ee=o(),E=d("p"),E.innerHTML=de,ne=o(),$=d("div"),v(I.$$.fragment),te=o(),U=d("p"),U.textContent=ue,se=o(),v(x.$$.fragment),Y=o(),v(Z.$$.fragment),R=o(),b=d("div"),v(C.$$.fragment),ie=o(),W=d("p"),W.textContent=fe,Q=o(),N=d("p"),this.h()},l(e){const n=$e("svelte-u9bgzb",document.head);s=u(n,"META",{name:!0,content:!0}),n.forEach(t),g=l(e),r=u(e,"P",{}),A(r).forEach(t),p=l(e),T(c.$$.fragment,e),i=l(e),h=u(e,"P",{"data-svelte-h":!0}),k(h)!=="svelte-z4ffbo"&&(h.innerHTML=le),z=l(e),B=u(e,"P",{"data-svelte-h":!0}),k(B)!=="svelte-dzyais"&&(B.innerHTML=re),L=l(e),T(J.$$.fragment,e),H=l(e),T(j.$$.fragment,e),O=l(e),f=u(e,"DIV",{class:!0});var _=A(f);T(G.$$.fragment,_),K=l(_),X=u(_,"P",{"data-svelte-h":!0}),k(X)!=="svelte-1jvczvp"&&(X.textContent=pe),ee=l(_),E=u(_,"P",{"data-svelte-h":!0}),k(E)!=="svelte-496sm0"&&(E.innerHTML=de),ne=l(_),$=u(_,"DIV",{class:!0});var w=A($);T(I.$$.fragment,w),te=l(w),U=u(w,"P",{"data-svelte-h":!0}),k(U)!=="svelte-50j04k"&&(U.textContent=ue),se=l(w),T(x.$$.fragment,w),w.forEach(t),_.forEach(t),Y=l(e),T(Z.$$.fragment,e),R=l(e),b=u(e,"DIV",{class:!0});var F=A(b);T(C.$$.fragment,F),ie=l(F),W=u(F,"P",{"data-svelte-h":!0}),k(W)!=="svelte-19ryw33"&&(W.textContent=fe),F.forEach(t),Q=l(e),N=u(e,"P",{}),A(N).forEach(t),this.h()},h(){S(s,"name","hf:doc:metadata"),S(s,"content",De),S($,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),S(f,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),S(b,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,n){m(document.head,s),a(e,g,n),a(e,r,n),a(e,p,n),y(c,e,n),a(e,i,n),a(e,h,n),a(e,z,n),a(e,B,n),a(e,L,n),y(J,e,n),a(e,H,n),y(j,e,n),a(e,O,n),a(e,f,n),y(G,f,null),m(f,K),m(f,X),m(f,ee),m(f,E),m(f,ne),m(f,$),y(I,$,null),m($,te),m($,U),m($,se),y(x,$,null),a(e,Y,n),y(Z,e,n),a(e,R,n),a(e,b,n),y(C,b,null),m(b,ie),m(b,W),a(e,Q,n),a(e,N,n),q=!0},p(e,[n]){const _={};n&2&&(_.$$scope={dirty:n,ctx:e}),J.$set(_);const w={};n&2&&(w.$$scope={dirty:n,ctx:e}),x.$set(w)},i(e){q||(M(c.$$.fragment,e),M(J.$$.fragment,e),M(j.$$.fragment,e),M(G.$$.fragment,e),M(I.$$.fragment,e),M(x.$$.fragment,e),M(Z.$$.fragment,e),M(C.$$.fragment,e),q=!0)},o(e){D(c.$$.fragment,e),D(J.$$.fragment,e),D(j.$$.fragment,e),D(G.$$.fragment,e),D(I.$$.fragment,e),D(x.$$.fragment,e),D(Z.$$.fragment,e),D(C.$$.fragment,e),q=!1},d(e){e&&(t(g),t(r),t(p),t(i),t(h),t(z),t(B),t(L),t(H),t(O),t(f),t(Y),t(R),t(b),t(Q),t(N)),t(s),P(c,e),P(J,e),P(j,e),P(G),P(I),P(x),P(Z,e),P(C)}}}const De='{"title":"Dance Diffusion","local":"dance-diffusion","sections":[{"title":"DanceDiffusionPipeline","local":"diffusers.DanceDiffusionPipeline","sections":[],"depth":2},{"title":"AudioPipelineOutput","local":"diffusers.AudioPipelineOutput","sections":[],"depth":2}],"depth":1}';function Pe(V){return he(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Ze extends _e{constructor(s){super(),ge(this,s,Pe,Me,me,{})}}export{Ze as component};
