import{S as Oi,i as Ui,s as Gi,e as s,k as p,w as b,t as i,M as Hi,c as r,d as o,m as c,a as n,x as w,h as a,b as l,G as e,g as u,y as D,q as y,o as A,B as M,v as zi,L as Fi}from"../../../chunks/vendor-hf-doc-builder.js";import{D as gt}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as Us}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as ze}from"../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Vi}from"../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function Wi(_t){let g,F,P,_,x;return _=new Us({props:{code:`import torch
from diffusers import AudioLDMPipeline

pipe = AudioLDMPipeline.from_pretrained("cvssp/audioldm", torch_dtype=torch.float16)
pipe = pipe.to("cuda")

prompt = "A hammer hitting a wooden surface"
audio = pipe(prompt).audio[0]`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AudioLDMPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = AudioLDMPipeline.from_pretrained(<span class="hljs-string">&quot;cvssp/audioldm&quot;</span>, torch_dtype=torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;A hammer hitting a wooden surface&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>audio = pipe(prompt).audio[<span class="hljs-number">0</span>]`}}),{c(){g=s("p"),F=i("Examples:"),P=p(),b(_.$$.fragment)},l(f){g=r(f,"P",{});var k=n(g);F=a(k,"Examples:"),k.forEach(o),P=c(f),w(_.$$.fragment,f)},m(f,k){u(f,g,k),e(g,F),u(f,P,k),D(_,f,k),x=!0},p:Fi,i(f){x||(y(_.$$.fragment,f),x=!0)},o(f){A(_.$$.fragment,f),x=!1},d(f){f&&o(g),f&&o(P),M(_,f)}}}function Ri(_t){let g,F,P,_,x,f,k,Fe,Qt,vt,I,V,Ve,oe,Xt,We,Yt,bt,W,Zt,se,eo,to,wt,L,oo,ie,so,io,Re,ro,ao,re,no,lo,Dt,S,po,ae,co,uo,ne,fo,ho,yt,N,R,Be,le,mo,Ke,go,At,T,_o,Me,vo,bo,de,wo,Do,Mt,pe,Pt,O,B,Je,ce,yo,Qe,Ao,$t,Pe,Mo,Et,K,Xe,Po,$o,Ye,Eo,Lt,$e,Lo,xt,J,U,xo,Ze,ko,So,et,To,jo,qo,G,Co,tt,Io,No,ot,Oo,Uo,kt,H,Q,st,ue,Go,it,Ho,St,h,zo,Ee,Fo,Vo,rt,Wo,Ro,Le,Bo,Ko,xe,Jo,Qo,ke,Xo,Yo,Se,Zo,es,Te,ts,os,Tt,$,ss,je,is,rs,at,as,ns,nt,ls,ds,qe,ps,cs,jt,fe,qt,z,X,lt,he,us,dt,fs,Ct,m,me,hs,pt,ms,gs,ge,_s,Ce,vs,bs,ws,j,_e,Ds,ct,ys,As,Y,Ms,Z,ve,Ps,be,$s,ut,Es,Ls,xs,ee,we,ks,De,Ss,ft,Ts,js,qs,q,ye,Cs,ht,Is,Ns,mt,Os,It;return f=new ze({}),oe=new ze({}),le=new ze({}),pe=new Us({props:{code:`from diffusers import AudioLDMPipeline
import torch
import scipy

repo_id = "cvssp/audioldm-s-full-v2"
pipe = AudioLDMPipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")

prompt = "Techno music with a strong, upbeat tempo and high melodic riffs"
audio = pipe(prompt, num_inference_steps=10, audio_length_in_s=5.0).audios[0]

# save the audio sample as a .wav file
scipy.io.wavfile.write("techno.wav", rate=16000, data=audio)`,highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AudioLDMPipeline
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">import</span> scipy

repo_id = <span class="hljs-string">&quot;cvssp/audioldm-s-full-v2&quot;</span>
pipe = AudioLDMPipeline.from_pretrained(repo_id, torch_dtype=torch.float16)
pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;Techno music with a strong, upbeat tempo and high melodic riffs&quot;</span>
audio = pipe(prompt, num_inference_steps=<span class="hljs-number">10</span>, audio_length_in_s=<span class="hljs-number">5.0</span>).audios[<span class="hljs-number">0</span>]

<span class="hljs-comment"># save the audio sample as a .wav file</span>
scipy.io.wavfile.write(<span class="hljs-string">&quot;techno.wav&quot;</span>, rate=<span class="hljs-number">16000</span>, data=audio)`}}),ce=new ze({}),ue=new ze({}),fe=new Us({props:{code:`from diffusers import AudioLDMPipeline, DPMSolverMultistepScheduler
import torch

pipeline = AudioLDMPipeline.from_pretrained("cvssp/audioldm-s-full-v2", torch_dtype=torch.float16)
pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)

# or
dpm_scheduler = DPMSolverMultistepScheduler.from_pretrained("cvssp/audioldm-s-full-v2", subfolder="scheduler")
pipeline = AudioLDMPipeline.from_pretrained(
    "cvssp/audioldm-s-full-v2", scheduler=dpm_scheduler, torch_dtype=torch.float16
)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AudioLDMPipeline, DPMSolverMultistepScheduler
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = AudioLDMPipeline.from_pretrained(<span class="hljs-string">&quot;cvssp/audioldm-s-full-v2&quot;</span>, torch_dtype=torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline.scheduler = DPMSolverMultistepScheduler.from_config(pipeline.scheduler.config)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># or</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>dpm_scheduler = DPMSolverMultistepScheduler.from_pretrained(<span class="hljs-string">&quot;cvssp/audioldm-s-full-v2&quot;</span>, subfolder=<span class="hljs-string">&quot;scheduler&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = AudioLDMPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;cvssp/audioldm-s-full-v2&quot;</span>, scheduler=dpm_scheduler, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)`}}),he=new ze({}),me=new gt({props:{name:"class diffusers.AudioLDMPipeline",anchor:"diffusers.AudioLDMPipeline",parameters:[{name:"vae",val:": AutoencoderKL"},{name:"text_encoder",val:": ClapTextModelWithProjection"},{name:"tokenizer",val:": typing.Union[transformers.models.roberta.tokenization_roberta.RobertaTokenizer, transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast]"},{name:"unet",val:": UNet2DConditionModel"},{name:"scheduler",val:": KarrasDiffusionSchedulers"},{name:"vocoder",val:": SpeechT5HifiGan"}],parametersDescription:[{anchor:"diffusers.AudioLDMPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/v0.16.0/en/api/models#diffusers.AutoencoderKL">AutoencoderKL</a>) &#x2014;
Variational Auto-Encoder (VAE) Model to encode and decode audios to and from latent representations.`,name:"vae"},{anchor:"diffusers.AudioLDMPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>ClapTextModelWithProjection</code>) &#x2014;
Frozen text-encoder. AudioLDM uses the text portion of
<a href="https://huggingface.co/docs/transformers/main/model_doc/clap#transformers.ClapTextModelWithProjection" rel="nofollow">CLAP</a>,
specifically the <a href="https://huggingface.co/laion/clap-htsat-unfused" rel="nofollow">RoBERTa HSTAT-unfused</a> variant.`,name:"text_encoder"},{anchor:"diffusers.AudioLDMPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>PreTrainedTokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/model_doc/roberta#transformers.RobertaTokenizer" rel="nofollow">RobertaTokenizer</a>.`,name:"tokenizer"},{anchor:"diffusers.AudioLDMPipeline.unet",description:'<strong>unet</strong> (<a href="/docs/diffusers/v0.16.0/en/api/models#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014; U-Net architecture to denoise the encoded audio latents.',name:"unet"},{anchor:"diffusers.AudioLDMPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/v0.16.0/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/v0.16.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, <a href="/docs/diffusers/v0.16.0/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a>, or <a href="/docs/diffusers/v0.16.0/en/api/schedulers/pndm#diffusers.PNDMScheduler">PNDMScheduler</a>.`,name:"scheduler"},{anchor:"diffusers.AudioLDMPipeline.vocoder",description:`<strong>vocoder</strong> (<code>SpeechT5HifiGan</code>) &#x2014;
Vocoder of class
<a href="https://huggingface.co/docs/transformers/main/en/model_doc/speecht5#transformers.SpeechT5HifiGan" rel="nofollow">SpeechT5HifiGan</a>.`,name:"vocoder"}],source:"https://github.com/huggingface/diffusers/blob/v0.16.0/src/diffusers/pipelines/audioldm/pipeline_audioldm.py#L46"}}),_e=new gt({props:{name:"__call__",anchor:"diffusers.AudioLDMPipeline.__call__",parameters:[{name:"prompt",val:": typing.Union[str, typing.List[str]] = None"},{name:"audio_length_in_s",val:": typing.Optional[float] = None"},{name:"num_inference_steps",val:": int = 10"},{name:"guidance_scale",val:": float = 2.5"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"num_waveforms_per_prompt",val:": typing.Optional[int] = 1"},{name:"eta",val:": float = 0.0"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",val:": typing.Optional[torch.FloatTensor] = None"},{name:"prompt_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"negative_prompt_embeds",val:": typing.Optional[torch.FloatTensor] = None"},{name:"return_dict",val:": bool = True"},{name:"callback",val:": typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None"},{name:"callback_steps",val:": typing.Optional[int] = 1"},{name:"cross_attention_kwargs",val:": typing.Union[typing.Dict[str, typing.Any], NoneType] = None"},{name:"output_type",val:": typing.Optional[str] = 'np'"}],parametersDescription:[{anchor:"diffusers.AudioLDMPipeline.__call__.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts to guide the audio generation. If not defined, one has to pass <code>prompt_embeds</code>.
instead.`,name:"prompt"},{anchor:"diffusers.AudioLDMPipeline.__call__.audio_length_in_s",description:`<strong>audio_length_in_s</strong> (<code>int</code>, <em>optional</em>, defaults to 5.12) &#x2014;
The length of the generated audio sample in seconds.`,name:"audio_length_in_s"},{anchor:"diffusers.AudioLDMPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 10) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher quality audio at the
expense of slower inference.`,name:"num_inference_steps"},{anchor:"diffusers.AudioLDMPipeline.__call__.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 2.5) &#x2014;
Guidance scale as defined in <a href="https://arxiv.org/abs/2207.12598" rel="nofollow">Classifier-Free Diffusion Guidance</a>.
<code>guidance_scale</code> is defined as <code>w</code> of equation 2. of <a href="https://arxiv.org/pdf/2205.11487.pdf" rel="nofollow">Imagen
Paper</a>. Guidance scale is enabled by setting <code>guidance_scale &gt; 1</code>. Higher guidance scale encourages to generate audios that are closely linked to the text <code>prompt</code>,
usually at the expense of lower sound quality.`,name:"guidance_scale"},{anchor:"diffusers.AudioLDMPipeline.__call__.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts not to guide the audio generation. If not defined, one has to pass
<code>negative_prompt_embeds</code> instead. Ignored when not using guidance (i.e., ignored if <code>guidance_scale</code> is
less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.AudioLDMPipeline.__call__.num_waveforms_per_prompt",description:`<strong>num_waveforms_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The number of waveforms to generate per prompt.`,name:"num_waveforms_per_prompt"},{anchor:"diffusers.AudioLDMPipeline.__call__.eta",description:`<strong>eta</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Corresponds to parameter eta (&#x3B7;) in the DDIM paper: <a href="https://arxiv.org/abs/2010.02502" rel="nofollow">https://arxiv.org/abs/2010.02502</a>. Only applies to
<a href="/docs/diffusers/v0.16.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">schedulers.DDIMScheduler</a>, will be ignored for others.`,name:"eta"},{anchor:"diffusers.AudioLDMPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code> or <code>List[torch.Generator]</code>, <em>optional</em>) &#x2014;
One or a list of <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">torch generator(s)</a>
to make generation deterministic.`,name:"generator"},{anchor:"diffusers.AudioLDMPipeline.__call__.latents",description:`<strong>latents</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for audio
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.AudioLDMPipeline.__call__.prompt_embeds",description:`<strong>prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt weighting. If not
provided, text embeddings will be generated from <code>prompt</code> input argument.`,name:"prompt_embeds"},{anchor:"diffusers.AudioLDMPipeline.__call__.negative_prompt_embeds",description:`<strong>negative_prompt_embeds</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, <em>e.g.</em> prompt
weighting. If not provided, negative_prompt_embeds will be generated from <code>negative_prompt</code> input
argument.`,name:"negative_prompt_embeds"},{anchor:"diffusers.AudioLDMPipeline.__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/v0.16.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput">StableDiffusionPipelineOutput</a> instead of a
plain tuple.`,name:"return_dict"},{anchor:"diffusers.AudioLDMPipeline.__call__.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that will be called every <code>callback_steps</code> steps during inference. The function will be
called with the following arguments: <code>callback(step: int, timestep: int, latents: torch.FloatTensor)</code>.`,name:"callback"},{anchor:"diffusers.AudioLDMPipeline.__call__.callback_steps",description:`<strong>callback_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The frequency at which the <code>callback</code> function will be called. If not specified, the callback will be
called at every step.`,name:"callback_steps"},{anchor:"diffusers.AudioLDMPipeline.__call__.cross_attention_kwargs",description:`<strong>cross_attention_kwargs</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
A kwargs dictionary that if specified is passed along to the <code>AttnProcessor</code> as defined under
<code>self.processor</code> in
<a href="https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/cross_attention.py" rel="nofollow">diffusers.cross_attention</a>.`,name:"cross_attention_kwargs"},{anchor:"diffusers.AudioLDMPipeline.__call__.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;np&quot;</code>) &#x2014;
The output format of the generate image. Choose between:<ul>
<li><code>&quot;np&quot;</code>: Return Numpy <code>np.ndarray</code> objects.</li>
<li><code>&quot;pt&quot;</code>: Return PyTorch <code>torch.Tensor</code> objects.</li>
</ul>`,name:"output_type"}],source:"https://github.com/huggingface/diffusers/blob/v0.16.0/src/diffusers/pipelines/audioldm/pipeline_audioldm.py#L398",returnDescription:`
<p><a
  href="/docs/diffusers/v0.16.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</a> if <code>return_dict</code> is True, otherwise a \`tuple.
When returning a tuple, the first element is a list with the generated audios.</p>
`,returnType:`
<p><a
  href="/docs/diffusers/v0.16.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</a> or <code>tuple</code></p>
`}}),Y=new Vi({props:{anchor:"diffusers.AudioLDMPipeline.__call__.example",$$slots:{default:[Wi]},$$scope:{ctx:_t}}}),ve=new gt({props:{name:"disable_vae_slicing",anchor:"diffusers.AudioLDMPipeline.disable_vae_slicing",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.16.0/src/diffusers/pipelines/audioldm/pipeline_audioldm.py#L104"}}),we=new gt({props:{name:"enable_sequential_cpu_offload",anchor:"diffusers.AudioLDMPipeline.enable_sequential_cpu_offload",parameters:[{name:"gpu_id",val:" = 0"}],source:"https://github.com/huggingface/diffusers/blob/v0.16.0/src/diffusers/pipelines/audioldm/pipeline_audioldm.py#L111"}}),ye=new gt({props:{name:"enable_vae_slicing",anchor:"diffusers.AudioLDMPipeline.enable_vae_slicing",parameters:[],source:"https://github.com/huggingface/diffusers/blob/v0.16.0/src/diffusers/pipelines/audioldm/pipeline_audioldm.py#L94"}}),{c(){g=s("meta"),F=p(),P=s("h1"),_=s("a"),x=s("span"),b(f.$$.fragment),k=p(),Fe=s("span"),Qt=i("AudioLDM"),vt=p(),I=s("h2"),V=s("a"),Ve=s("span"),b(oe.$$.fragment),Xt=p(),We=s("span"),Yt=i("Overview"),bt=p(),W=s("p"),Zt=i("AudioLDM was proposed in "),se=s("a"),eo=i("AudioLDM: Text-to-Audio Generation with Latent Diffusion Models"),to=i(" by Haohe Liu et al."),wt=p(),L=s("p"),oo=i("Inspired by "),ie=s("a"),so=i("Stable Diffusion"),io=i(`, AudioLDM
is a text-to-audio `),Re=s("em"),ro=i("latent diffusion model (LDM)"),ao=i(" that learns continuous audio representations from "),re=s("a"),no=i("CLAP"),lo=i(`
latents. AudioLDM takes a text prompt as input and predicts the corresponding audio. It can generate text-conditional
sound effects, human speech and music.`),Dt=p(),S=s("p"),po=i("This pipeline was contributed by "),ae=s("a"),co=i("sanchit-gandhi"),uo=i(". The original codebase can be found "),ne=s("a"),fo=i("here"),ho=i("."),yt=p(),N=s("h2"),R=s("a"),Be=s("span"),b(le.$$.fragment),mo=p(),Ke=s("span"),go=i("Text-to-Audio"),At=p(),T=s("p"),_o=i("The "),Me=s("a"),vo=i("AudioLDMPipeline"),bo=i(" can be used to load pre-trained weights from "),de=s("a"),wo=i("cvssp/audioldm-s-full-v2"),Do=i(" and generate text-conditional audio outputs:"),Mt=p(),b(pe.$$.fragment),Pt=p(),O=s("h3"),B=s("a"),Je=s("span"),b(ce.$$.fragment),yo=p(),Qe=s("span"),Ao=i("Tips"),$t=p(),Pe=s("p"),Mo=i("Prompts:"),Et=p(),K=s("ul"),Xe=s("li"),Po=i("Descriptive prompt inputs work best: you can use adjectives to describe the sound (e.g. \u201Chigh quality\u201D or \u201Cclear\u201D) and make the prompt context specific (e.g., \u201Cwater stream in a forest\u201D instead of \u201Cstream\u201D)."),$o=p(),Ye=s("li"),Eo=i("It\u2019s best to use general terms like \u2018cat\u2019 or \u2018dog\u2019 instead of specific names or abstract objects that the model may not be familiar with."),Lt=p(),$e=s("p"),Lo=i("Inference:"),xt=p(),J=s("ul"),U=s("li"),xo=i("The "),Ze=s("em"),ko=i("quality"),So=i(" of the predicted audio sample can be controlled by the "),et=s("code"),To=i("num_inference_steps"),jo=i(" argument: higher steps give higher quality audio at the expense of slower inference."),qo=p(),G=s("li"),Co=i("The "),tt=s("em"),Io=i("length"),No=i(" of the predicted audio sample can be controlled by varying the "),ot=s("code"),Oo=i("audio_length_in_s"),Uo=i(" argument."),kt=p(),H=s("h3"),Q=s("a"),st=s("span"),b(ue.$$.fragment),Go=p(),it=s("span"),Ho=i("How to load and use different schedulers"),St=p(),h=s("p"),zo=i("The AudioLDM pipeline uses "),Ee=s("a"),Fo=i("DDIMScheduler"),Vo=i(" scheduler by default. But "),rt=s("code"),Wo=i("diffusers"),Ro=i(` provides many other schedulers
that can be used with the AudioLDM pipeline such as `),Le=s("a"),Bo=i("PNDMScheduler"),Ko=i(", "),xe=s("a"),Jo=i("LMSDiscreteScheduler"),Qo=i(", "),ke=s("a"),Xo=i("EulerDiscreteScheduler"),Yo=i(`,
`),Se=s("a"),Zo=i("EulerAncestralDiscreteScheduler"),es=i(" etc. We recommend using the "),Te=s("a"),ts=i("DPMSolverMultistepScheduler"),os=i(` as it\u2019s currently the fastest
scheduler there is.`),Tt=p(),$=s("p"),ss=i("To use a different scheduler, you can either change it via the "),je=s("a"),is=i("ConfigMixin.from_config()"),rs=i(`
method, or pass the `),at=s("code"),as=i("scheduler"),ns=i(" argument to the "),nt=s("code"),ls=i("from_pretrained"),ds=i(` method of the pipeline. For example, to use the
`),qe=s("a"),ps=i("DPMSolverMultistepScheduler"),cs=i(", you can do the following:"),jt=p(),b(fe.$$.fragment),qt=p(),z=s("h2"),X=s("a"),lt=s("span"),b(he.$$.fragment),us=p(),dt=s("span"),fs=i("AudioLDMPipeline"),Ct=p(),m=s("div"),b(me.$$.fragment),hs=p(),pt=s("p"),ms=i("Pipeline for text-to-audio generation using AudioLDM."),gs=p(),ge=s("p"),_s=i("This model inherits from "),Ce=s("a"),vs=i("DiffusionPipeline"),bs=i(`. Check the superclass documentation for the generic methods the
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)`),ws=p(),j=s("div"),b(_e.$$.fragment),Ds=p(),ct=s("p"),ys=i("Function invoked when calling the pipeline for generation."),As=p(),b(Y.$$.fragment),Ms=p(),Z=s("div"),b(ve.$$.fragment),Ps=p(),be=s("p"),$s=i("Disable sliced VAE decoding. If "),ut=s("code"),Es=i("enable_vae_slicing"),Ls=i(` was previously invoked, this method will go back to
computing decoding in one step.`),xs=p(),ee=s("div"),b(we.$$.fragment),ks=p(),De=s("p"),Ss=i(`Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
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