import{S as io,i as so,s as to,e as t,k as a,w as c,t as p,M as ro,c as r,d as s,m as l,a as o,x as m,h as f,b as n,N as oo,G as i,g as u,y as h,L as no,q as g,o as _,B as v,v as ao}from"../../../chunks/vendor-hf-doc-builder.js";import{D}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as Es}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as A}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function lo(Qt){let P,Ni,E,C,Ye,le,xs,Qe,ks,Si,x,G,We,de,Ms,Xe,js,qi,pe,fe,Is,Ts,Vi,Ue,Ls,Ci,H,Ns,ue,Ss,qs,Gi,k,R,Ze,ce,Vs,ei,Cs,Hi,U,ii,M,si,Gs,Hs,ti,Rs,Us,Oe,Os,zs,ri,j,oi,me,Bs,Ks,ni,ai,Fs,Js,ze,he,Be,Wt,Ri,I,O,li,ge,Ys,di,Qs,Ui,T,z,pi,_e,Ws,fi,Xs,Oi,ve,zi,L,B,ui,be,Zs,ci,et,Bi,$e,Ki,N,K,mi,ye,it,hi,st,Fi,we,Ji,S,F,gi,De,tt,_i,rt,Yi,Ae,Qi,q,J,vi,Pe,ot,bi,nt,Wi,$,Ee,at,xe,lt,Ke,dt,pt,ft,Y,ke,ut,$i,ct,mt,Q,Me,ht,yi,gt,_t,W,je,vt,wi,bt,$t,X,Ie,yt,Di,wt,Dt,Z,Te,At,Ai,Pt,Xi,V,ee,Pi,Le,Et,Ei,xt,Zi,b,Ne,kt,ie,Se,Mt,xi,jt,It,se,qe,Tt,ki,Lt,Nt,te,Ve,St,Mi,qt,Vt,re,Ce,Ct,ji,Gt,Ht,oe,Ge,Rt,Ii,Ut,Ot,ne,He,zt,Ti,Bt,Kt,ae,Re,Ft,Li,Jt,es;return le=new A({}),de=new A({}),ce=new A({}),ge=new A({}),_e=new A({}),ve=new Es({props:{code:`import torch
from IPython.display import Audio
from diffusers import DiffusionPipeline

device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-256").to(device)

output = pipe()
display(output.images[0])
display(Audio(output.audios[0], rate=mel.get_sample_rate()))`,highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> IPython.display <span class="hljs-keyword">import</span> Audio
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

device = <span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&quot;cpu&quot;</span>
pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;teticio/audio-diffusion-256&quot;</span>).to(device)

output = pipe()
display(output.images[<span class="hljs-number">0</span>])
display(Audio(output.audios[<span class="hljs-number">0</span>], rate=mel.get_sample_rate()))`}}),be=new A({}),$e=new Es({props:{code:`import torch
from IPython.display import Audio
from diffusers import DiffusionPipeline

device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("teticio/latent-audio-diffusion-256").to(device)

output = pipe()
display(output.images[0])
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))`,highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> IPython.display <span class="hljs-keyword">import</span> Audio
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

device = <span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&quot;cpu&quot;</span>
pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;teticio/latent-audio-diffusion-256&quot;</span>).to(device)

output = pipe()
display(output.images[<span class="hljs-number">0</span>])
display(Audio(output.audios[<span class="hljs-number">0</span>], rate=pipe.mel.get_sample_rate()))`}}),ye=new A({}),we=new Es({props:{code:`import torch
from IPython.display import Audio
from diffusers import DiffusionPipeline

device = "cuda" if torch.cuda.is_available() else "cpu"
pipe = DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256").to(device)

output = pipe()
display(output.images[0])
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))`,highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> IPython.display <span class="hljs-keyword">import</span> Audio
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

device = <span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&quot;cpu&quot;</span>
pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;teticio/audio-diffusion-ddim-256&quot;</span>).to(device)

output = pipe()
display(output.images[<span class="hljs-number">0</span>])
display(Audio(output.audios[<span class="hljs-number">0</span>], rate=pipe.mel.get_sample_rate()))`}}),De=new A({}),Ae=new Es({props:{code:`output = pipe(
    raw_audio=output.audios[0, 0],
    start_step=int(pipe.get_default_steps() / 2),
    mask_start_secs=1,
    mask_end_secs=1,
)
display(output.images[0])
display(Audio(output.audios[0], rate=pipe.mel.get_sample_rate()))`,highlighted:`output = pipe(
    raw_audio=output.audios[<span class="hljs-number">0</span>, <span class="hljs-number">0</span>],
    start_step=<span class="hljs-built_in">int</span>(pipe.get_default_steps() / <span class="hljs-number">2</span>),
    mask_start_secs=<span class="hljs-number">1</span>,
    mask_end_secs=<span class="hljs-number">1</span>,
)
display(output.images[<span class="hljs-number">0</span>])
display(Audio(output.audios[<span class="hljs-number">0</span>], rate=pipe.mel.get_sample_rate()))`}}),Pe=new A({}),Ee=new D({props:{name:"class diffusers.AudioDiffusionPipeline",anchor:"diffusers.AudioDiffusionPipeline",parameters:[{name:"vqvae",val:": AutoencoderKL"},{name:"unet",val:": UNet2DConditionModel"},{name:"mel",val:": Mel"},{name:"scheduler",val:": typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_ddpm.DDPMScheduler]"}],parametersDescription:[{anchor:"diffusers.AudioDiffusionPipeline.vqae",description:'<strong>vqae</strong> (<a href="/docs/diffusers/main/en/api/models#diffusers.AutoencoderKL">AutoencoderKL</a>) &#x2014; Variational AutoEncoder for Latent Audio Diffusion or None',name:"vqae"},{anchor:"diffusers.AudioDiffusionPipeline.unet",description:'<strong>unet</strong> (<a href="/docs/diffusers/main/en/api/models#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014; UNET model',name:"unet"},{anchor:"diffusers.AudioDiffusionPipeline.mel",description:'<strong>mel</strong> (<a href="/docs/diffusers/main/en/api/pipelines/audio_diffusion#diffusers.Mel">Mel</a>) &#x2014; transform audio &lt;-&gt; spectrogram',name:"mel"},{anchor:"diffusers.AudioDiffusionPipeline.scheduler",description:"<strong>scheduler</strong> ([<code>DDIMScheduler</code> or <code>DDPMScheduler</code>]) &#x2014; de-noising scheduler",name:"scheduler"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py#L30"}}),ke=new D({props:{name:"__call__",anchor:"diffusers.AudioDiffusionPipeline.__call__",parameters:[{name:"batch_size",val:": int = 1"},{name:"audio_file",val:": str = None"},{name:"raw_audio",val:": ndarray = None"},{name:"slice",val:": int = 0"},{name:"start_step",val:": int = 0"},{name:"steps",val:": int = None"},{name:"generator",val:": Generator = None"},{name:"mask_start_secs",val:": float = 0"},{name:"mask_end_secs",val:": float = 0"},{name:"step_generator",val:": Generator = None"},{name:"eta",val:": float = 0"},{name:"noise",val:": Tensor = None"},{name:"encoding",val:": Tensor = None"},{name:"return_dict",val:" = True"}],parametersDescription:[{anchor:"diffusers.AudioDiffusionPipeline.__call__.batch_size",description:"<strong>batch_size</strong> (<code>int</code>) &#x2014; number of samples to generate",name:"batch_size"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.audio_file",description:"<strong>audio_file</strong> (<code>str</code>) &#x2014; must be a file on disk due to Librosa limitation or",name:"audio_file"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.raw_audio",description:"<strong>raw_audio</strong> (<code>np.ndarray</code>) &#x2014; audio as numpy array",name:"raw_audio"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.slice",description:"<strong>slice</strong> (<code>int</code>) &#x2014; slice number of audio to convert",name:"slice"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.start_step",description:"<strong>start_step</strong> (int) &#x2014; step to start from",name:"start_step"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.steps",description:"<strong>steps</strong> (<code>int</code>) &#x2014; number of de-noising steps (defaults to 50 for DDIM, 1000 for DDPM)",name:"steps"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.generator",description:"<strong>generator</strong> (<code>torch.Generator</code>) &#x2014; random number generator or None",name:"generator"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.mask_start_secs",description:"<strong>mask_start_secs</strong> (<code>float</code>) &#x2014; number of seconds of audio to mask (not generate) at start",name:"mask_start_secs"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.mask_end_secs",description:"<strong>mask_end_secs</strong> (<code>float</code>) &#x2014; number of seconds of audio to mask (not generate) at end",name:"mask_end_secs"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.step_generator",description:"<strong>step_generator</strong> (<code>torch.Generator</code>) &#x2014; random number generator used to de-noise or None",name:"step_generator"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.eta",description:"<strong>eta</strong> (<code>float</code>) &#x2014; parameter between 0 and 1 used with DDIM scheduler",name:"eta"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.noise",description:"<strong>noise</strong> (<code>torch.Tensor</code>) &#x2014; noise tensor of shape (batch_size, 1, height, width) or None",name:"noise"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.encoding",description:"<strong>encoding</strong> (<code>torch.Tensor</code>) &#x2014; for UNet2DConditionModel shape (batch_size, seq_length, cross_attention_dim)",name:"encoding"},{anchor:"diffusers.AudioDiffusionPipeline.__call__.return_dict",description:"<strong>return_dict</strong> (<code>bool</code>) &#x2014; if True return AudioPipelineOutput, ImagePipelineOutput else Tuple",name:"return_dict"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py#L77",returnDescription:`
<p>mel spectrograms (<code>float</code>, <code>List[np.ndarray]</code>): sample rate and raw audios</p>
`,returnType:`
<p><code>List[PIL Image]</code></p>
`}}),Me=new D({props:{name:"encode",anchor:"diffusers.AudioDiffusionPipeline.encode",parameters:[{name:"images",val:": typing.List[PIL.Image.Image]"},{name:"steps",val:": int = 50"}],parametersDescription:[{anchor:"diffusers.AudioDiffusionPipeline.encode.images",description:"<strong>images</strong> (<code>List[PIL Image]</code>) &#x2014; list of images to encode",name:"images"},{anchor:"diffusers.AudioDiffusionPipeline.encode.steps",description:"<strong>steps</strong> (<code>int</code>) &#x2014; number of encoding steps to perform (defaults to 50)",name:"steps"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py#L215",returnDescription:`
<p>noise tensor of shape (batch_size, 1, height, width)</p>
`,returnType:`
<p><code>np.ndarray</code></p>
`}}),je=new D({props:{name:"get_default_steps",anchor:"diffusers.AudioDiffusionPipeline.get_default_steps",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py#L69",returnDescription:`
<p>number of steps</p>
`,returnType:`
<p><code>int</code></p>
`}}),Ie=new D({props:{name:"get_input_dims",anchor:"diffusers.AudioDiffusionPipeline.get_input_dims",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py#L54",returnDescription:`
<p>(height, width)</p>
`,returnType:`
<p><code>Tuple</code></p>
`}}),Te=new D({props:{name:"slerp",anchor:"diffusers.AudioDiffusionPipeline.slerp",parameters:[{name:"x0",val:": Tensor"},{name:"x1",val:": Tensor"},{name:"alpha",val:": float"}],parametersDescription:[{anchor:"diffusers.AudioDiffusionPipeline.slerp.x0",description:"<strong>x0</strong> (<code>torch.Tensor</code>) &#x2014; first tensor to interpolate between",name:"x0"},{anchor:"diffusers.AudioDiffusionPipeline.slerp.x1",description:"<strong>x1</strong> (<code>torch.Tensor</code>) &#x2014; seconds tensor to interpolate between",name:"x1"},{anchor:"diffusers.AudioDiffusionPipeline.slerp.alpha",description:"<strong>alpha</strong> (<code>float</code>) &#x2014; interpolation between 0 and 1",name:"alpha"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/audio_diffusion/pipeline_audio_diffusion.py#L252",returnDescription:`
<p>interpolated tensor</p>
`,returnType:`
<p><code>torch.Tensor</code></p>
`}}),Le=new A({}),Ne=new D({props:{name:"class diffusers.Mel",anchor:"diffusers.Mel",parameters:[{name:"x_res",val:": int = 256"},{name:"y_res",val:": int = 256"},{name:"sample_rate",val:": int = 22050"},{name:"n_fft",val:": int = 2048"},{name:"hop_length",val:": int = 512"},{name:"top_db",val:": int = 80"},{name:"n_iter",val:": int = 32"}],parametersDescription:[{anchor:"diffusers.Mel.x_res",description:"<strong>x_res</strong> (<code>int</code>) &#x2014; x resolution of spectrogram (time)",name:"x_res"},{anchor:"diffusers.Mel.y_res",description:"<strong>y_res</strong> (<code>int</code>) &#x2014; y resolution of spectrogram (frequency bins)",name:"y_res"},{anchor:"diffusers.Mel.sample_rate",description:"<strong>sample_rate</strong> (<code>int</code>) &#x2014; sample rate of audio",name:"sample_rate"},{anchor:"diffusers.Mel.n_fft",description:"<strong>n_fft</strong> (<code>int</code>) &#x2014; number of Fast Fourier Transforms",name:"n_fft"},{anchor:"diffusers.Mel.hop_length",description:"<strong>hop_length</strong> (<code>int</code>) &#x2014; hop length (a higher number is recommended for lower than 256 y_res)",name:"hop_length"},{anchor:"diffusers.Mel.top_db",description:"<strong>top_db</strong> (<code>int</code>) &#x2014; loudest in decibels",name:"top_db"},{anchor:"diffusers.Mel.n_iter",description:"<strong>n_iter</strong> (<code>int</code>) &#x2014; number of iterations for Griffin Linn mel inversion",name:"n_iter"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/audio_diffusion/mel.py#L37"}}),Se=new D({props:{name:"audio_slice_to_image",anchor:"diffusers.Mel.audio_slice_to_image",parameters:[{name:"slice",val:": int"}],parametersDescription:[{anchor:"diffusers.Mel.audio_slice_to_image.slice",description:"<strong>slice</strong> (<code>int</code>) &#x2014; slice number of audio to convert (out of get_number_of_slices())",name:"slice"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/audio_diffusion/mel.py#L128",returnDescription:`
<p>grayscale image of x_res x y_res</p>
`,returnType:`
<p><code>PIL Image</code></p>
`}}),qe=new D({props:{name:"get_audio_slice",anchor:"diffusers.Mel.get_audio_slice",parameters:[{name:"slice",val:": int = 0"}],parametersDescription:[{anchor:"diffusers.Mel.get_audio_slice.slice",description:"<strong>slice</strong> (<code>int</code>) &#x2014; slice number of audio (out of get_number_of_slices())",name:"slice"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/audio_diffusion/mel.py#L109",returnDescription:`
<p>audio as numpy array</p>
`,returnType:`
<p><code>np.ndarray</code></p>
`}}),Ve=new D({props:{name:"get_number_of_slices",anchor:"diffusers.Mel.get_number_of_slices",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/audio_diffusion/mel.py#L101",returnDescription:`
<p>number of spectograms audio can be sliced into</p>
`,returnType:`
<p><code>int</code></p>
`}}),Ce=new D({props:{name:"get_sample_rate",anchor:"diffusers.Mel.get_sample_rate",parameters:[],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/audio_diffusion/mel.py#L120",returnDescription:`
<p>sample rate of audio</p>
`,returnType:`
<p><code>int</code></p>
`}}),Ge=new D({props:{name:"image_to_audio",anchor:"diffusers.Mel.image_to_audio",parameters:[{name:"image",val:": Image"}],parametersDescription:[{anchor:"diffusers.Mel.image_to_audio.image",description:"<strong>image</strong> (<code>PIL Image</code>) &#x2014; x_res x y_res grayscale image",name:"image"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/audio_diffusion/mel.py#L145",returnDescription:`
<p>raw audio</p>
`,returnType:`
<p>audio (<code>np.ndarray</code>)</p>
`}}),He=new D({props:{name:"load_audio",anchor:"diffusers.Mel.load_audio",parameters:[{name:"audio_file",val:": str = None"},{name:"raw_audio",val:": ndarray = None"}],parametersDescription:[{anchor:"diffusers.Mel.load_audio.audio_file",description:"<strong>audio_file</strong> (<code>str</code>) &#x2014; must be a file on disk due to Librosa limitation or",name:"audio_file"},{anchor:"diffusers.Mel.load_audio.raw_audio",description:"<strong>raw_audio</strong> (<code>np.ndarray</code>) &#x2014; audio as numpy array",name:"raw_audio"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/audio_diffusion/mel.py#L85"}}),Re=new D({props:{name:"set_resolution",anchor:"diffusers.Mel.set_resolution",parameters:[{name:"x_res",val:": int"},{name:"y_res",val:": int"}],parametersDescription:[{anchor:"diffusers.Mel.set_resolution.x_res",description:"<strong>x_res</strong> (<code>int</code>) &#x2014; x resolution of spectrogram (time)",name:"x_res"},{anchor:"diffusers.Mel.set_resolution.y_res",description:"<strong>y_res</strong> (<code>int</code>) &#x2014; y resolution of spectrogram (frequency bins)",name:"y_res"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/audio_diffusion/mel.py#L73"}}),{c(){P=t("meta"),Ni=a(),E=t("h1"),C=t("a"),Ye=t("span"),c(le.$$.fragment),xs=a(),Qe=t("span"),ks=p("Audio Diffusion"),Si=a(),x=t("h2"),G=t("a"),We=t("span"),c(de.$$.fragment),Ms=a(),Xe=t("span"),js=p("Overview"),qi=a(),pe=t("p"),fe=t("a"),Is=p("Audio Diffusion"),Ts=p(" by Robert Dargavel Smith."),Vi=a(),Ue=t("p"),Ls=p(`Audio Diffusion leverages the recent advances in image generation using diffusion models by converting audio samples to
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