diffusers/pr_13921/en/_app/immutable/nodes/37.21f0d0d7.js

import{s as ie,n as de,o as ce}from"../chunks/scheduler.53228c21.js";import{S as ue,i as me,e as i,s as a,c as u,h as pe,a as d,d as o,b as s,f as C,g as m,j as Q,k as G,l as p,m as n,n as f,t as g,o as h,p as $}from"../chunks/index.cac5d66a.js";import{C as fe}from"../chunks/CopyLLMTxtMenu.0ef49226.js";import{D as z}from"../chunks/Docstring.9de32ff4.js";import{C as ge}from"../chunks/CodeBlock.606cbaf4.js";import{H as le,E as he}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.48d5cb47.js";function $e(ee){let c,Z,D,F,_,J,v,V,b,te='The 3D variational autoencoder (VAE) model with KL loss used in <a href="https://huggingface.co/Lightricks/LTX-2" rel="nofollow">LTX-2</a> was introduced by Lightricks. This is for encoding and decoding audio latent representations.',H,L,oe="The model can be loaded with the following code snippet.",N,T,P,A,R,r,x,S,X,re="LTX2 audio VAE for encoding and decoding audio latent representations.",B,j,w,Y,K,y,O,E,M,W,k,U,I,q;return _=new fe({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),v=new le({props:{title:"AutoencoderKLLTX2Audio",local:"autoencoderklltx2audio",headingTag:"h1"}}),T=new ge({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEF1dG9lbmNvZGVyS0xMVFgyQXVkaW8lMEElMEF2YWUlMjAlM0QlMjBBdXRvZW5jb2RlcktMTFRYMkF1ZGlvLmZyb21fcHJldHJhaW5lZCglMjJMaWdodHJpY2tzJTJGTFRYLTIlMjIlMkMlMjBzdWJmb2xkZXIlM0QlMjJ2YWUlMjIlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MzIpLnRvKCUyMmN1ZGElMjIp",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoencoderKLLTX2Audio

vae = AutoencoderKLLTX2Audio.from_pretrained(<span class="hljs-string">&quot;Lightricks/LTX-2&quot;</span>, subfolder=<span class="hljs-string">&quot;vae&quot;</span>, torch_dtype=torch.float32).to(<span class="hljs-string">&quot;cuda&quot;</span>)`,lang:"python",wrap:!1}}),A=new le({props:{title:"AutoencoderKLLTX2Audio",local:"diffusers.AutoencoderKLLTX2Audio",headingTag:"h2"}}),x=new z({props:{name:"class diffusers.AutoencoderKLLTX2Audio",anchor:"diffusers.AutoencoderKLLTX2Audio",parameters:[{name:"base_channels",val:": int = 128"},{name:"output_channels",val:": int = 2"},{name:"ch_mult",val:": tuple = (1, 2, 4)"},{name:"num_res_blocks",val:": int = 2"},{name:"attn_resolutions",val:": tuple[int, ...] | None = None"},{name:"in_channels",val:": int = 2"},{name:"resolution",val:": int = 256"},{name:"latent_channels",val:": int = 8"},{name:"norm_type",val:": str = 'pixel'"},{name:"causality_axis",val:": str | None = 'height'"},{name:"dropout",val:": float = 0.0"},{name:"mid_block_add_attention",val:": bool = False"},{name:"sample_rate",val:": int = 16000"},{name:"mel_hop_length",val:": int = 160"},{name:"is_causal",val:": bool = True"},{name:"mel_bins",val:": int | None = 64"},{name:"double_z",val:": bool = True"}],source:"https://github.com/huggingface/diffusers/blob/vr_13921/src/diffusers/models/autoencoders/autoencoder_kl_ltx2_audio.py#L668"}}),w=new z({props:{name:"wrapper",anchor:"diffusers.AutoencoderKLLTX2Audio.encode",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_13921/src/diffusers/utils/accelerate_utils.py#L43"}}),y=new z({props:{name:"wrapper",anchor:"diffusers.AutoencoderKLLTX2Audio.decode",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/diffusers/blob/vr_13921/src/diffusers/utils/accelerate_utils.py#L43"}}),M=new z({props:{name:"forward",anchor:"diffusers.AutoencoderKLLTX2Audio.forward",parameters:[{name:"sample",val:": Tensor"},{name:"sample_posterior",val:": bool = False"},{name:"return_dict",val:": bool = True"},{name:"generator",val:": torch._C.Generator | None = None"}],parametersDescription:[{anchor:"diffusers.AutoencoderKLLTX2Audio.forward.sample",description:"<strong>sample</strong> (<code>torch.Tensor</code>) &#x2014; Input sample.",name:"sample"},{anchor:"diffusers.AutoencoderKLLTX2Audio.forward.sample_posterior",description:`<strong>sample_posterior</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to sample from the posterior.`,name:"sample_posterior"},{anchor:"diffusers.AutoencoderKLLTX2Audio.forward.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 <code>DecoderOutput</code> instead of a plain tuple.`,name:"return_dict"},{anchor:"diffusers.AutoencoderKLLTX2Audio.forward.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 sampling
deterministic.`,name:"generator"}],source:"https://github.com/huggingface/diffusers/blob/vr_13921/src/diffusers/models/autoencoders/autoencoder_kl_ltx2_audio.py#L788",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>If <code>return_dict</code> is True, a <code>DecoderOutput</code> is returned, otherwise a plain <code>tuple</code> is returned.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>DecoderOutput</code> or <code>tuple</code></p>
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