diffusers/main/en/_app/immutable/nodes/67.e3930cc1.js

import{s as _e,o as ge,n as he}from"../chunks/scheduler.8c3d61f6.js";import{S as $e,i as be,g as d,s as o,r as b,A as we,h as u,f as t,c as r,j as S,u as w,x as k,k as z,y as m,a as i,v,d as T,t as y,w as M}from"../chunks/index.da70eac4.js";import{T as ve}from"../chunks/Tip.1d9b8c37.js";import{D as re}from"../chunks/Docstring.ee4b6913.js";import{C as Te}from"../chunks/CodeBlock.00a903b3.js";import{E as ye}from"../chunks/ExampleCodeBlock.f7bd2c1f.js";import{H as le,E as Me}from"../chunks/EditOnGithub.1e64e623.js";function De(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(l){s=u(l,"P",{"data-svelte-h":!0}),k(s)!=="svelte-1wmc0l4"&&(s.innerHTML=g)},m(l,p){i(l,s,p)},p:he,d(l){l&&t(s)}}}function Pe(V){let s,g="Example:",l,p,c;return p=new Te({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,l=o(),b(p.$$.fragment)},l(a){s=u(a,"P",{"data-svelte-h":!0}),k(s)!=="svelte-11lpom8"&&(s.textContent=g),l=r(a),w(p.$$.fragment,a)},m(a,h){i(a,s,h),i(a,l,h),v(p,a,h),c=!0},p:he,i(a){c||(T(p.$$.fragment,a),c=!0)},o(a){y(p.$$.fragment,a),c=!1},d(a){a&&(t(s),t(l)),M(p,a)}}}function Je(V){let s,g,l,p,c,a,h,pe='<a href="https://github.com/Harmonai-org/sample-generator" rel="nofollow">Dance Diffusion</a> is by Zach Evans.',L,B,de='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>.',H,J,O,j,Y,f,G,ne,X,ue="Pipeline for audio generation.",te,U,fe=`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.).`,se,$,I,ie,W,ce="The call function to the pipeline for generation.",ae,x,R,Z,Q,D,E,oe,N,me="Output class for audio pipelines.",q,C,F,A,K;return c=new le({props:{title:"Dance Diffusion",local:"dance-diffusion",headingTag:"h1"}}),J=new ve({props:{$$slots:{default:[De]},$$scope:{ctx:V}}}),j=new le({props:{title:"DanceDiffusionPipeline",local:"diffusers.DanceDiffusionPipeline",headingTag:"h2"}}),G=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/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 re({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 ye({props:{anchor:"diffusers.DanceDiffusionPipeline.__call__.example",$$slots:{default:[Pe]},$$scope:{ctx:V}}}),Z=new le({props:{title:"AudioPipelineOutput",local:"diffusers.AudioPipelineOutput",headingTag:"h2"}}),E=new re({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#L120"}}),C=new Me({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/pipelines/dance_diffusion.md"}}),{c(){s=d("meta"),g=o(),l=d("p"),p=o(),b(c.$$.fragment),a=o(),h=d("p"),h.innerHTML=pe,L=o(),B=d("p"),B.innerHTML=de,H=o(),b(J.$$.fragment),O=o(),b(j.$$.fragment),Y=o(),f=d("div"),b(G.$$.fragment),ne=o(),X=d("p"),X.textContent=ue,te=o(),U=d("p"),U.innerHTML=fe,se=o(),$=d("div"),b(I.$$.fragment),ie=o(),W=d("p"),W.textContent=ce,ae=o(),b(x.$$.fragment),R=o(),b(Z.$$.fragment),Q=o(),D=d("div"),b(E.$$.fragment),oe=o(),N=d("p"),N.textContent=me,q=o(),b(C.$$.fragment),F=o(),A=d("p"),this.h()},l(e){const n=we("svelte-u9bgzb",document.head);s=u(n,"META",{name:!0,content:!0}),n.forEach(t),g=r(e),l=u(e,"P",{}),S(l).forEach(t),p=r(e),w(c.$$.fragment,e),a=r(e),h=u(e,"P",{"data-svelte-h":!0}),k(h)!=="svelte-z4ffbo"&&(h.innerHTML=pe),L=r(e),B=u(e,"P",{"data-svelte-h":!0}),k(B)!=="svelte-dzyais"&&(B.innerHTML=de),H=r(e),w(J.$$.fragment,e),O=r(e),w(j.$$.fragment,e),Y=r(e),f=u(e,"DIV",{class:!0});var _=S(f);w(G.$$.fragment,_),ne=r(_),X=u(_,"P",{"data-svelte-h":!0}),k(X)!=="svelte-1jvczvp"&&(X.textContent=ue),te=r(_),U=u(_,"P",{"data-svelte-h":!0}),k(U)!=="svelte-496sm0"&&(U.innerHTML=fe),se=r(_),$=u(_,"DIV",{class:!0});var P=S($);w(I.$$.fragment,P),ie=r(P),W=u(P,"P",{"data-svelte-h":!0}),k(W)!=="svelte-50j04k"&&(W.textContent=ce),ae=r(P),w(x.$$.fragment,P),P.forEach(t),_.forEach(t),R=r(e),w(Z.$$.fragment,e),Q=r(e),D=u(e,"DIV",{class:!0});var ee=S(D);w(E.$$.fragment,ee),oe=r(ee),N=u(ee,"P",{"data-svelte-h":!0}),k(N)!=="svelte-19ryw33"&&(N.textContent=me),ee.forEach(t),q=r(e),w(C.$$.fragment,e),F=r(e),A=u(e,"P",{}),S(A).forEach(t),this.h()},h(){z(s,"name","hf:doc:metadata"),z(s,"content",xe),z($,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),z(f,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),z(D,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,n){m(document.head,s),i(e,g,n),i(e,l,n),i(e,p,n),v(c,e,n),i(e,a,n),i(e,h,n),i(e,L,n),i(e,B,n),i(e,H,n),v(J,e,n),i(e,O,n),v(j,e,n),i(e,Y,n),i(e,f,n),v(G,f,null),m(f,ne),m(f,X),m(f,te),m(f,U),m(f,se),m(f,$),v(I,$,null),m($,ie),m($,W),m($,ae),v(x,$,null),i(e,R,n),v(Z,e,n),i(e,Q,n),i(e,D,n),v(E,D,null),m(D,oe),m(D,N),i(e,q,n),v(C,e,n),i(e,F,n),i(e,A,n),K=!0},p(e,[n]){const _={};n&2&&(_.$$scope={dirty:n,ctx:e}),J.$set(_);const P={};n&2&&(P.$$scope={dirty:n,ctx:e}),x.$set(P)},i(e){K||(T(c.$$.fragment,e),T(J.$$.fragment,e),T(j.$$.fragment,e),T(G.$$.fragment,e),T(I.$$.fragment,e),T(x.$$.fragment,e),T(Z.$$.fragment,e),T(E.$$.fragment,e),T(C.$$.fragment,e),K=!0)},o(e){y(c.$$.fragment,e),y(J.$$.fragment,e),y(j.$$.fragment,e),y(G.$$.fragment,e),y(I.$$.fragment,e),y(x.$$.fragment,e),y(Z.$$.fragment,e),y(E.$$.fragment,e),y(C.$$.fragment,e),K=!1},d(e){e&&(t(g),t(l),t(p),t(a),t(h),t(L),t(B),t(H),t(O),t(Y),t(f),t(R),t(Q),t(D),t(q),t(F),t(A)),t(s),M(c,e),M(J,e),M(j,e),M(G),M(I),M(x),M(Z,e),M(E),M(C,e)}}}const xe='{"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 ke(V){return ge(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Ve extends $e{constructor(s){super(),be(this,s,ke,Je,_e,{})}}export{Ve as component};