import{S as It,i as qt,s as At,e as o,k as d,w as L,t as r,M as zt,c as n,d as i,m as u,a,x as M,h as s,b as f,G as t,g as p,y as S,q as N,o as G,B as F,v as Ot}from"../../../chunks/vendor-hf-doc-builder.js";import{T as Ct}from"../../../chunks/Tip-hf-doc-builder.js";import{D as ke}from"../../../chunks/Docstring-hf-doc-builder.js";import{I as ft}from"../../../chunks/IconCopyLink-hf-doc-builder.js";function Lt(pe){let c,E,m,v,P,g,W,x;return{c(){c=o("p"),E=r("Make sure to check out the Schedulers "),m=o("a"),v=r("guide"),P=r(" to learn how to explore the tradeoff between scheduler speed and quality, and see the "),g=o("a"),W=r("reuse components across pipelines"),x=r(" section to learn how to efficiently load the same components into multiple pipelines."),this.h()},l($){c=n($,"P",{});var _=a(c);E=s(_,"Make sure to check out the Schedulers "),m=n(_,"A",{href:!0});var y=a(m);v=s(y,"guide"),y.forEach(i),P=s(_," to learn how to explore the tradeoff between scheduler speed and quality, and see the "),g=n(_,"A",{href:!0});var w=a(g);W=s(w,"reuse components across pipelines"),w.forEach(i),x=s(_," section to learn how to efficiently load the same components into multiple pipelines."),_.forEach(i),this.h()},h(){f(m,"href","/using-diffusers/schedulers"),f(g,"href","/using-diffusers/loading#reuse-components-across-pipelines")},m($,_){p($,c,_),t(c,E),t(c,m),t(m,v),t(c,P),t(c,g),t(g,W),t(c,x)},d($){$&&i(c)}}}function Mt(pe){let c,E,m,v,P,g,W,x,$,_,y,w,Ie,qe,me,X,Ae,he,ee,ie,ze,ge,k,Oe,B,Ce,Le,_e,I,ve,V,q,oe,R,Me,ne,Se,be,h,U,Ne,ae,Ge,Fe,H,We,te,Be,Re,Ue,A,J,He,re,Je,Ye,z,Y,Ze,b,je,se,Ke,Xe,le,et,tt,fe,it,ot,ye,Q,O,ce,Z,nt,de,at,we,T,j,rt,ue,st,De;return g=new ft({}),I=new Ct({props:{$$slots:{default:[Lt]},$$scope:{ctx:pe}}}),R=new ft({}),U=new ke({props:{name:"class diffusers.VQDiffusionPipeline",anchor:"diffusers.VQDiffusionPipeline",parameters:[{name:"vqvae",val:": VQModel"},{name:"text_encoder",val:": CLIPTextModel"},{name:"tokenizer",val:": CLIPTokenizer"},{name:"transformer",val:": Transformer2DModel"},{name:"scheduler",val:": VQDiffusionScheduler"},{name:"learned_classifier_free_sampling_embeddings",val:": LearnedClassifierFreeSamplingEmbeddings"}],parametersDescription:[{anchor:"diffusers.VQDiffusionPipeline.vqvae",description:`<strong>vqvae</strong> (<a href="/docs/diffusers/v0.21.0/en/api/models/vq#diffusers.VQModel">VQModel</a>) &#x2014;
Vector Quantized Variational Auto-Encoder (VAE) model to encode and decode images to and from latent
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Frozen text-encoder (<a href="https://huggingface.co/openai/clip-vit-base-patch32" rel="nofollow">clip-vit-base-patch32</a>).`,name:"text_encoder"},{anchor:"diffusers.VQDiffusionPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
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<code>prompt</code> at the expense of lower image quality. Guidance scale is enabled when <code>guidance_scale &gt; 1</code>.`,name:"guidance_scale"},{anchor:"diffusers.VQDiffusionPipeline.__call__.truncation_rate",description:`<strong>truncation_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0 (equivalent to no truncation)) &#x2014;
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