diffusers/main/ko/_app/pages/using-diffusers/write_own_pipeline.mdx-hf-doc-builder.js

import{S as Ip,i as kp,s as Rp,e as i,k as r,t as a,w as M,M as Gp,c as o,d as l,m as c,a as m,h as n,x as h,b as w,N as $p,G as e,g as p,y as U,q as b,o as j,B as J,v as Np,L as Qp}from"../../chunks/vendor-hf-doc-builder.js";import{T as jn}from"../../chunks/Tip-hf-doc-builder.js";import{I as Wp}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as g}from"../../chunks/CodeBlock-hf-doc-builder.js";import{D as Dp}from"../../chunks/DocNotebookDropdown-hf-doc-builder.js";function Xp(A){let f,E,u,Z,C;return{c(){f=i("p"),E=a("\u{1F4A1} VAE, UNet \uBC0F \uD14D\uC2A4\uD2B8 \uC778\uCF54\uB354 \uBAA8\uB378\uC758 \uC791\uB3D9\uBC29\uC2DD\uC5D0 \uB300\uD55C \uC790\uC138\uD55C \uB0B4\uC6A9\uC740 "),u=i("a"),Z=a("How does Stable Diffusion work?"),C=a(" \uBE14\uB85C\uADF8\uB97C \uCC38\uC870\uD558\uC138\uC694."),this.h()},l(y){f=o(y,"P",{});var d=m(f);E=n(d,"\u{1F4A1} VAE, UNet \uBC0F \uD14D\uC2A4\uD2B8 \uC778\uCF54\uB354 \uBAA8\uB378\uC758 \uC791\uB3D9\uBC29\uC2DD\uC5D0 \uB300\uD55C \uC790\uC138\uD55C \uB0B4\uC6A9\uC740 "),u=o(d,"A",{href:!0,rel:!0});var T=m(u);Z=n(T,"How does Stable Diffusion work?"),T.forEach(l),C=n(d," \uBE14\uB85C\uADF8\uB97C \uCC38\uC870\uD558\uC138\uC694."),d.forEach(l),this.h()},h(){w(u,"href","https://huggingface.co/blog/stable_diffusion#how-does-stable-diffusion-work"),w(u,"rel","nofollow")},m(y,d){p(y,f,d),e(f,E),e(f,u),e(u,Z),e(f,C)},d(y){y&&l(f)}}}function Bp(A){let f,E,u,Z,C;return{c(){f=i("p"),E=a("\u{1F4A1} "),u=i("code"),Z=a("guidance_scale"),C=a(" \uB9E4\uAC1C\uBCC0\uC218\uB294 \uC774\uBBF8\uC9C0\uB97C \uC0DD\uC131\uD560 \uB54C \uD504\uB86C\uD504\uD2B8\uC5D0 \uC5BC\uB9C8\uB098 \uB9CE\uC740 \uAC00\uC911\uCE58\uB97C \uBD80\uC5EC\uD560\uC9C0 \uACB0\uC815\uD569\uB2C8\uB2E4.")},l(y){f=o(y,"P",{});var d=m(f);E=n(d,"\u{1F4A1} "),u=o(d,"CODE",{});var T=m(u);Z=n(T,"guidance_scale"),T.forEach(l),C=n(d," \uB9E4\uAC1C\uBCC0\uC218\uB294 \uC774\uBBF8\uC9C0\uB97C \uC0DD\uC131\uD560 \uB54C \uD504\uB86C\uD504\uD2B8\uC5D0 \uC5BC\uB9C8\uB098 \uB9CE\uC740 \uAC00\uC911\uCE58\uB97C \uBD80\uC5EC\uD560\uC9C0 \uACB0\uC815\uD569\uB2C8\uB2E4."),d.forEach(l)},m(y,d){p(y,f,d),e(f,E),e(f,u),e(u,Z),e(f,C)},d(y){y&&l(f)}}}function Sp(A){let f,E,u,Z,C,y,d,T;return d=new g({props:{code:"MiUyMCoqJTIwKGxlbih2YWUuY29uZmlnLmJsb2NrX291dF9jaGFubmVscyklMjAtJTIwMSklMjAlM0QlM0QlMjA4",highlighted:'<span class="hljs-number">2</span> ** (<span class="hljs-built_in">len</span>(vae.config.block_out_channels) - <span class="hljs-number">1</span>) == <span class="hljs-number">8</span>'}}),{c(){f=i("p"),E=a("\u{1F4A1} "),u=i("code"),Z=a("vae"),C=a(" \uBAA8\uB378\uC5D0\uB294 3\uAC1C\uC758 \uB2E4\uC6B4 \uC0D8\uD50C\uB9C1 \uB808\uC774\uC5B4\uAC00 \uC788\uAE30 \uB54C\uBB38\uC5D0 \uB192\uC774\uC640 \uB108\uBE44\uAC00 8\uB85C \uB098\uB269\uB2C8\uB2E4. \uB2E4\uC74C\uC744 \uC2E4\uD589\uD558\uC5EC \uD655\uC778\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4:"),y=r(),M(d.$$.fragment)},l(_){f=o(_,"P",{});var v=m(f);E=n(v,"\u{1F4A1} "),u=o(v,"CODE",{});var Xs=m(u);Z=n(Xs,"vae"),Xs.forEach(l),C=n(v," \uBAA8\uB378\uC5D0\uB294 3\uAC1C\uC758 \uB2E4\uC6B4 \uC0D8\uD50C\uB9C1 \uB808\uC774\uC5B4\uAC00 \uC788\uAE30 \uB54C\uBB38\uC5D0 \uB192\uC774\uC640 \uB108\uBE44\uAC00 8\uB85C \uB098\uB269\uB2C8\uB2E4. \uB2E4\uC74C\uC744 \uC2E4\uD589\uD558\uC5EC \uD655\uC778\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4:"),v.forEach(l),y=c(_),h(d.$$.fragment,_)},m(_,v){p(_,f,v),e(f,E),e(f,u),e(u,Z),e(f,C),p(_,y,v),U(d,_,v),T=!0},p:Qp,i(_){T||(b(d.$$.fragment,_),T=!0)},o(_){j(d.$$.fragment,_),T=!1},d(_){_&&l(f),_&&l(y),J(d,_)}}}function Fp(A){let f,E,u,Z,C,y,d,T,_,v,Xs,mt,Ll,Bs,rt,Ol,ts,ct,ql,Ss,ft,Kl,as,se,ns,Fs,Jn,le,zs,ut,ee,k,dt,rl,Mt,ht,cl,Ut,bt,fl,jt,Jt,te,As,yt,ae,V,ps,ul,wt,gt,is,Tt,os,dl,_t,Et,ms,Ct,rs,Ml,Vt,Zt,cs,vt,fs,hl,$t,It,us,kt,F,G,Rt,Ul,Gt,Nt,bl,Qt,Wt,jl,Dt,Xt,Bt,ds,St,Jl,Ft,zt,Ms,yl,At,Yt,hs,ne,$,xt,wl,Ht,Pt,gl,Lt,Ot,Tl,qt,Kt,_l,sa,la,pe,Ys,ea,ie,z,Y,El,Us,ta,Cl,aa,oe,x,na,Vl,pa,ia,me,xs,oa,re,H,ce,N,ma,Zl,ra,ca,bs,vl,fa,ua,fe,js,ue,Q,da,$l,Ma,ha,Il,Ua,ba,de,Js,Me,Hs,ja,he,ys,Ue,ws,Ja,be,Ps,ya,je,P,Je,Ls,wa,ye,gs,we,Os,ga,ge,Ts,Te,I,Ta,kl,_a,Ea,Rl,Ca,Va,Gl,Za,va,Nl,$a,Ia,_e,_s,Ee,qs,ka,Ce,Es,Ve,Cs,Ra,Ze,L,Ga,Ql,Na,Qa,ve,O,$e,Vs,Ie,Zs,Wa,ke,W,Da,Wl,Xa,Ba,Dl,Sa,Fa,Re,vs,Ge,q,za,Xl,Aa,Ya,Ne,D,Bl,xa,Ha,Sl,Pa,La,Fl,Oa,Qe,$s,We,Is,qa,De,X,Ka,zl,sn,ln,Al,en,tn,Xe,ks,Be,K,an,Yl,nn,pn,Se,Rs,Fe,Gs,xl,yn,ze,Ns,on,Ae,Ks,mn,Ye,sl,rn,xe,ll,cn,He,ss,Qs,fn,el,un,dn,Mn,Ws,hn,tl,Un,bn,Pe;return y=new Dp({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/ko/write_own_pipeline.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/ko/pytorch/write_own_pipeline.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/ko/tensorflow/write_own_pipeline.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/ko/write_own_pipeline.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/ko/pytorch/write_own_pipeline.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/ko/tensorflow/write_own_pipeline.ipynb"}]}}),as=new g({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEREUE1QaXBlbGluZSUwQSUwQWRkcG0lMjAlM0QlMjBERFBNUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZSUyRmRkcG0tY2F0LTI1NiUyMikudG8oJTIyY3VkYSUyMiklMEFpbWFnZSUyMCUzRCUyMGRkcG0obnVtX2luZmVyZW5jZV9zdGVwcyUzRDI1KS5pbWFnZXMlNUIwJTVEJTBBaW1hZ2U=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDPMPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>ddpm = DDPMPipeline.from_pretrained(<span class="hljs-string">&quot;google/ddpm-cat-256&quot;</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = ddpm(num_inference_steps=<span class="hljs-number">25</span>).images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image`}}),is=new g({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEREUE1TY2hlZHVsZXIlMkMlMjBVTmV0MkRNb2RlbCUwQSUwQXNjaGVkdWxlciUyMCUzRCUyMEREUE1TY2hlZHVsZXIuZnJvbV9wcmV0cmFpbmVkKCUyMmdvb2dsZSUyRmRkcG0tY2F0LTI1NiUyMiklMEFtb2RlbCUyMCUzRCUyMFVOZXQyRE1vZGVsLmZyb21fcHJldHJhaW5lZCglMjJnb29nbGUlMkZkZHBtLWNhdC0yNTYlMjIpLnRvKCUyMmN1ZGElMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDPMScheduler, UNet2DModel

<span class="hljs-meta">&gt;&gt;&gt; </span>scheduler = DDPMScheduler.from_pretrained(<span class="hljs-string">&quot;google/ddpm-cat-256&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = UNet2DModel.from_pretrained(<span class="hljs-string">&quot;google/ddpm-cat-256&quot;</span>).to(<span class="hljs-string">&quot;cuda&quot;</span>)`}}),ms=new g({props:{code:"c2NoZWR1bGVyLnNldF90aW1lc3RlcHMoNTAp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>scheduler.set_timesteps(<span class="hljs-number">50</span>)'}}),cs=new g({props:{code:"c2NoZWR1bGVyLnRpbWVzdGVwcw==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>scheduler.timesteps
tensor([<span class="hljs-number">980</span>, <span class="hljs-number">960</span>, <span class="hljs-number">940</span>, <span class="hljs-number">920</span>, <span class="hljs-number">900</span>, <span class="hljs-number">880</span>, <span class="hljs-number">860</span>, <span class="hljs-number">840</span>, <span class="hljs-number">820</span>, <span class="hljs-number">800</span>, <span class="hljs-number">780</span>, <span class="hljs-number">760</span>, <span class="hljs-number">740</span>, <span class="hljs-number">720</span>,
    <span class="hljs-number">700</span>, <span class="hljs-number">680</span>, <span class="hljs-number">660</span>, <span class="hljs-number">640</span>, <span class="hljs-number">620</span>, <span class="hljs-number">600</span>, <span class="hljs-number">580</span>, <span class="hljs-number">560</span>, <span class="hljs-number">540</span>, <span class="hljs-number">520</span>, <span class="hljs-number">500</span>, <span class="hljs-number">480</span>, <span class="hljs-number">460</span>, <span class="hljs-number">440</span>,
    <span class="hljs-number">420</span>, <span class="hljs-number">400</span>, <span class="hljs-number">380</span>, <span class="hljs-number">360</span>, <span class="hljs-number">340</span>, <span class="hljs-number">320</span>, <span class="hljs-number">300</span>, <span class="hljs-number">280</span>, <span class="hljs-number">260</span>, <span class="hljs-number">240</span>, <span class="hljs-number">220</span>, <span class="hljs-number">200</span>, <span class="hljs-number">180</span>, <span class="hljs-number">160</span>,
    <span class="hljs-number">140</span>, <span class="hljs-number">120</span>, <span class="hljs-number">100</span>,  <span class="hljs-number">80</span>,  <span class="hljs-number">60</span>,  <span class="hljs-number">40</span>,  <span class="hljs-number">20</span>,   <span class="hljs-number">0</span>])`}}),us=new g({props:{code:"aW1wb3J0JTIwdG9yY2glMEElMEFzYW1wbGVfc2l6ZSUyMCUzRCUyMG1vZGVsLmNvbmZpZy5zYW1wbGVfc2l6ZSUwQW5vaXNlJTIwJTNEJTIwdG9yY2gucmFuZG4oKDElMkMlMjAzJTJDJTIwc2FtcGxlX3NpemUlMkMlMjBzYW1wbGVfc2l6ZSkpLnRvKCUyMmN1ZGElMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>sample_size = model.config.sample_size
<span class="hljs-meta">&gt;&gt;&gt; </span>noise = torch.randn((<span class="hljs-number">1</span>, <span class="hljs-number">3</span>, sample_size, sample_size)).to(<span class="hljs-string">&quot;cuda&quot;</span>)`}}),ds=new g({props:{code:"aW5wdXQlMjAlM0QlMjBub2lzZSUwQSUwQWZvciUyMHQlMjBpbiUyMHNjaGVkdWxlci50aW1lc3RlcHMlM0ElMEElMjAlMjAlMjAlMjB3aXRoJTIwdG9yY2gubm9fZ3JhZCgpJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwbm9pc3lfcmVzaWR1YWwlMjAlM0QlMjBtb2RlbChpbnB1dCUyQyUyMHQpLnNhbXBsZSUwQSUyMCUyMCUyMCUyMHByZXZpb3VzX25vaXN5X3NhbXBsZSUyMCUzRCUyMHNjaGVkdWxlci5zdGVwKG5vaXN5X3Jlc2lkdWFsJTJDJTIwdCUyQyUyMGlucHV0KS5wcmV2X3NhbXBsZSUwQSUyMCUyMCUyMCUyMGlucHV0JTIwJTNEJTIwcHJldmlvdXNfbm9pc3lfc2FtcGxl",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">input</span> = noise

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> t <span class="hljs-keyword">in</span> scheduler.timesteps:
<span class="hljs-meta">... </span>    <span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>        noisy_residual = model(<span class="hljs-built_in">input</span>, t).sample
<span class="hljs-meta">... </span>    previous_noisy_sample = scheduler.step(noisy_residual, t, <span class="hljs-built_in">input</span>).prev_sample
<span class="hljs-meta">... </span>    <span class="hljs-built_in">input</span> = previous_noisy_sample`}}),hs=new g({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBaW1wb3J0JTIwbnVtcHklMjBhcyUyMG5wJTBBJTBBaW1hZ2UlMjAlM0QlMjAoaW5wdXQlMjAlMkYlMjAyJTIwJTJCJTIwMC41KS5jbGFtcCgwJTJDJTIwMSklMEFpbWFnZSUyMCUzRCUyMGltYWdlLmNwdSgpLnBlcm11dGUoMCUyQyUyMDIlMkMlMjAzJTJDJTIwMSkubnVtcHkoKSU1QjAlNUQlMEFpbWFnZSUyMCUzRCUyMEltYWdlLmZyb21hcnJheSgoaW1hZ2UlMjAqJTIwMjU1KS5yb3VuZCgpLmFzdHlwZSglMjJ1aW50OCUyMikpJTBBaW1hZ2U=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

<span class="hljs-meta">&gt;&gt;&gt; </span>image = (<span class="hljs-built_in">input</span> / <span class="hljs-number">2</span> + <span class="hljs-number">0.5</span>).clamp(<span class="hljs-number">0</span>, <span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = image.cpu().permute(<span class="hljs-number">0</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">1</span>).numpy()[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.fromarray((image * <span class="hljs-number">255</span>).<span class="hljs-built_in">round</span>().astype(<span class="hljs-string">&quot;uint8&quot;</span>))
<span class="hljs-meta">&gt;&gt;&gt; </span>image`}}),Us=new Wp({}),H=new jn({props:{$$slots:{default:[Xp]},$$scope:{ctx:A}}}),js=new g({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<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> transformers <span class="hljs-keyword">import</span> CLIPTextModel, CLIPTokenizer
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoencoderKL, UNet2DConditionModel, PNDMScheduler

<span class="hljs-meta">&gt;&gt;&gt; </span>vae = AutoencoderKL.from_pretrained(<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>, subfolder=<span class="hljs-string">&quot;vae&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = CLIPTokenizer.from_pretrained(<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>, subfolder=<span class="hljs-string">&quot;tokenizer&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>text_encoder = CLIPTextModel.from_pretrained(<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>, subfolder=<span class="hljs-string">&quot;text_encoder&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>unet = UNet2DConditionModel.from_pretrained(<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>, subfolder=<span class="hljs-string">&quot;unet&quot;</span>)`}}),Js=new g({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFVuaVBDTXVsdGlzdGVwU2NoZWR1bGVyJTBBJTBBc2NoZWR1bGVyJTIwJTNEJTIwVW5pUENNdWx0aXN0ZXBTY2hlZHVsZXIuZnJvbV9wcmV0cmFpbmVkKCUyMkNvbXBWaXMlMkZzdGFibGUtZGlmZnVzaW9uLXYxLTQlMjIlMkMlMjBzdWJmb2xkZXIlM0QlMjJzY2hlZHVsZXIlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> UniPCMultistepScheduler

<span class="hljs-meta">&gt;&gt;&gt; </span>scheduler = UniPCMultistepScheduler.from_pretrained(<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>, subfolder=<span class="hljs-string">&quot;scheduler&quot;</span>)`}}),ys=new g({props:{code:"dG9yY2hfZGV2aWNlJTIwJTNEJTIwJTIyY3VkYSUyMiUwQXZhZS50byh0b3JjaF9kZXZpY2UpJTBBdGV4dF9lbmNvZGVyLnRvKHRvcmNoX2RldmljZSklMEF1bmV0LnRvKHRvcmNoX2RldmljZSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>torch_device = <span class="hljs-string">&quot;cuda&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>vae.to(torch_device)
<span class="hljs-meta">&gt;&gt;&gt; </span>text_encoder.to(torch_device)
<span class="hljs-meta">&gt;&gt;&gt; </span>unet.to(torch_device)`}}),P=new jn({props:{$$slots:{default:[Bp]},$$scope:{ctx:A}}}),gs=new g({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = [<span class="hljs-string">&quot;a photograph of an astronaut riding a horse&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>height = <span class="hljs-number">512</span>  <span class="hljs-comment"># Stable Diffusion\uC758 \uAE30\uBCF8 \uB192\uC774</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>width = <span class="hljs-number">512</span>  <span class="hljs-comment"># Stable Diffusion\uC758 \uAE30\uBCF8 \uB108\uBE44</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>num_inference_steps = <span class="hljs-number">25</span>  <span class="hljs-comment"># \uB178\uC774\uC988 \uC81C\uAC70 \uC2A4\uD15D \uC218</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>guidance_scale = <span class="hljs-number">7.5</span>  <span class="hljs-comment"># classifier-free guidance\uB97C \uC704\uD55C scale</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>generator = torch.manual_seed(<span class="hljs-number">0</span>)  <span class="hljs-comment"># \uCD08\uAE30 \uC7A0\uC7AC \uB178\uC774\uC988\uB97C \uC0DD\uC131\uD558\uB294 seed generator</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>batch_size = <span class="hljs-built_in">len</span>(prompt)`}}),Ts=new g({props:{code:"dGV4dF9pbnB1dCUyMCUzRCUyMHRva2VuaXplciglMEElMjAlMjAlMjAlMjBwcm9tcHQlMkMlMjBwYWRkaW5nJTNEJTIybWF4X2xlbmd0aCUyMiUyQyUyMG1heF9sZW5ndGglM0R0b2tlbml6ZXIubW9kZWxfbWF4X2xlbmd0aCUyQyUyMHRydW5jYXRpb24lM0RUcnVlJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMiUwQSklMEElMEF3aXRoJTIwdG9yY2gubm9fZ3JhZCgpJTNBJTBBJTIwJTIwJTIwJTIwdGV4dF9lbWJlZGRpbmdzJTIwJTNEJTIwdGV4dF9lbmNvZGVyKHRleHRfaW5wdXQuaW5wdXRfaWRzLnRvKHRvcmNoX2RldmljZSkpJTVCMCU1RA==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>text_input = tokenizer(
<span class="hljs-meta">... </span>    prompt, padding=<span class="hljs-string">&quot;max_length&quot;</span>, max_length=tokenizer.model_max_length, truncation=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>    text_embeddings = text_encoder(text_input.input_ids.to(torch_device))[<span class="hljs-number">0</span>]`}}),_s=new g({props:{code:"bWF4X2xlbmd0aCUyMCUzRCUyMHRleHRfaW5wdXQuaW5wdXRfaWRzLnNoYXBlJTVCLTElNUQlMEF1bmNvbmRfaW5wdXQlMjAlM0QlMjB0b2tlbml6ZXIoJTVCJTIyJTIyJTVEJTIwKiUyMGJhdGNoX3NpemUlMkMlMjBwYWRkaW5nJTNEJTIybWF4X2xlbmd0aCUyMiUyQyUyMG1heF9sZW5ndGglM0RtYXhfbGVuZ3RoJTJDJTIwcmV0dXJuX3RlbnNvcnMlM0QlMjJwdCUyMiklMEF1bmNvbmRfZW1iZWRkaW5ncyUyMCUzRCUyMHRleHRfZW5jb2Rlcih1bmNvbmRfaW5wdXQuaW5wdXRfaWRzLnRvKHRvcmNoX2RldmljZSkpJTVCMCU1RA==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>max_length = text_input.input_ids.shape[-<span class="hljs-number">1</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>uncond_input = tokenizer([<span class="hljs-string">&quot;&quot;</span>] * batch_size, padding=<span class="hljs-string">&quot;max_length&quot;</span>, max_length=max_length, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>uncond_embeddings = text_encoder(uncond_input.input_ids.to(torch_device))[<span class="hljs-number">0</span>]`}}),Es=new g({props:{code:"dGV4dF9lbWJlZGRpbmdzJTIwJTNEJTIwdG9yY2guY2F0KCU1QnVuY29uZF9lbWJlZGRpbmdzJTJDJTIwdGV4dF9lbWJlZGRpbmdzJTVEKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>text_embeddings = torch.cat([uncond_embeddings, text_embeddings])'}}),O=new jn({props:{$$slots:{default:[Sp]},$$scope:{ctx:A}}}),Vs=new g({props:{code:"bGF0ZW50cyUyMCUzRCUyMHRvcmNoLnJhbmRuKCUwQSUyMCUyMCUyMCUyMChiYXRjaF9zaXplJTJDJTIwdW5ldC5pbl9jaGFubmVscyUyQyUyMGhlaWdodCUyMCUyRiUyRiUyMDglMkMlMjB3aWR0aCUyMCUyRiUyRiUyMDgpJTJDJTBBJTIwJTIwJTIwJTIwZ2VuZXJhdG9yJTNEZ2VuZXJhdG9yJTJDJTBBKSUwQWxhdGVudHMlMjAlM0QlMjBsYXRlbnRzLnRvKHRvcmNoX2RldmljZSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>latents = torch.randn(
<span class="hljs-meta">... </span>    (batch_size, unet.in_channels, height // <span class="hljs-number">8</span>, width // <span class="hljs-number">8</span>),
<span class="hljs-meta">... </span>    generator=generator,
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>latents = latents.to(torch_device)`}}),vs=new g({props:{code:"bGF0ZW50cyUyMCUzRCUyMGxhdGVudHMlMjAqJTIwc2NoZWR1bGVyLmluaXRfbm9pc2Vfc2lnbWE=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>latents = latents * scheduler.init_noise_sigma'}}),$s=new g({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> tqdm.auto <span class="hljs-keyword">import</span> tqdm

<span class="hljs-meta">&gt;&gt;&gt; </span>scheduler.set_timesteps(num_inference_steps)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> t <span class="hljs-keyword">in</span> tqdm(scheduler.timesteps):
<span class="hljs-meta">... </span>    <span class="hljs-comment"># classifier-free guidance\uB97C \uC218\uD589\uD558\uB294 \uACBD\uC6B0 \uB450\uBC88\uC758 forward pass\uB97C \uC218\uD589\uD558\uC9C0 \uC54A\uB3C4\uB85D latent\uB97C \uD655\uC7A5.</span>
<span class="hljs-meta">... </span>    latent_model_input = torch.cat([latents] * <span class="hljs-number">2</span>)

<span class="hljs-meta">... </span>    latent_model_input = scheduler.scale_model_input(latent_model_input, timestep=t)

<span class="hljs-meta">... </span>    <span class="hljs-comment"># noise residual \uC608\uCE21</span>
<span class="hljs-meta">... </span>    <span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>        noise_pred = unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample

<span class="hljs-meta">... </span>    <span class="hljs-comment"># guidance \uC218\uD589</span>
<span class="hljs-meta">... </span>    noise_pred_uncond, noise_pred_text = noise_pred.chunk(<span class="hljs-number">2</span>)
<span class="hljs-meta">... </span>    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

<span class="hljs-meta">... </span>    <span class="hljs-comment"># \uC774\uC804 \uB178\uC774\uC988 \uC0D8\uD50C\uC744 \uACC4\uC0B0 x_t -&gt; x_t-1</span>
<span class="hljs-meta">... </span>    latents = scheduler.step(noise_pred, t, latents).prev_sample`}}),ks=new g({props:{code:"JTIzJTIwbGF0ZW50JUVCJUE1JUJDJTIwJUVDJThBJUE0JUVDJUJDJTgwJUVDJTlEJUJDJUVCJUE3JTgxJUVEJTk1JTk4JUVBJUIzJUEwJTIwdmFlJUVCJUExJTlDJTIwJUVDJTlEJUI0JUVCJUFGJUI4JUVDJUE3JTgwJTIwJUVCJTk0JTk0JUVDJUJEJTk0JUVCJTk0JUE5JTBBbGF0ZW50cyUyMCUzRCUyMDElMjAlMkYlMjAwLjE4MjE1JTIwKiUyMGxhdGVudHMlMEF3aXRoJTIwdG9yY2gubm9fZ3JhZCgpJTNBJTBBJTIwJTIwJTIwJTIwaW1hZ2UlMjAlM0QlMjB2YWUuZGVjb2RlKGxhdGVudHMpLnNhbXBsZQ==",highlighted:`<span class="hljs-comment"># latent\uB97C \uC2A4\uCF00\uC77C\uB9C1\uD558\uACE0 vae\uB85C \uC774\uBBF8\uC9C0 \uB514\uCF54\uB529</span>
latents = <span class="hljs-number">1</span> / <span class="hljs-number">0.18215</span> * latents
<span class="hljs-keyword">with</span> torch.no_grad():
    image = vae.decode(latents).sample`}}),Rs=new g({props:{code:"aW1hZ2UlMjAlM0QlMjAoaW1hZ2UlMjAlMkYlMjAyJTIwJTJCJTIwMC41KS5jbGFtcCgwJTJDJTIwMSklMEFpbWFnZSUyMCUzRCUyMGltYWdlLmRldGFjaCgpLmNwdSgpLnBlcm11dGUoMCUyQyUyMDIlMkMlMjAzJTJDJTIwMSkubnVtcHkoKSUwQWltYWdlcyUyMCUzRCUyMChpbWFnZSUyMColMjAyNTUpLnJvdW5kKCkuYXN0eXBlKCUyMnVpbnQ4JTIyKSUwQXBpbF9pbWFnZXMlMjAlM0QlMjAlNUJJbWFnZS5mcm9tYXJyYXkoaW1hZ2UpJTIwZm9yJTIwaW1hZ2UlMjBpbiUyMGltYWdlcyU1RCUwQXBpbF9pbWFnZXMlNUIwJTVE",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>image = (image / <span class="hljs-number">2</span> + <span class="hljs-number">0.5</span>).clamp(<span class="hljs-number">0</span>, <span class="hljs-number">1</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = image.detach().cpu().permute(<span class="hljs-number">0</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">1</span>).numpy()
<span class="hljs-meta">&gt;&gt;&gt; </span>images = (image * <span class="hljs-number">255</span>).<span class="hljs-built_in">round</span>().astype(<span class="hljs-string">&quot;uint8&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pil_images = [Image.fromarray(image) <span class="hljs-keyword">for</span> image <span class="hljs-keyword">in</span> images]
<span class="hljs-meta">&gt;&gt;&gt; </span>pil_images[<span class="hljs-number">0</span>]`}}),{c(){f=i("meta"),E=r(),u=i("h1"),Z=a("\uD30C\uC774\uD504\uB77C\uC778, \uBAA8\uB378 \uBC0F \uC2A4\uCF00\uC904\uB7EC \uC774\uD574\uD558\uAE30"),C=r(),M(y.$$.fragment),d=r(),T=i("p"),_=a("\u{1F9E8} Diffusers\uB294 \uC0AC\uC6A9\uC790 \uCE5C\uD654\uC801\uC774\uBA70 \uC720\uC5F0\uD55C \uB3C4\uAD6C \uC0C1\uC790\uB85C, \uC0AC\uC6A9\uC0AC\uB840\uC5D0 \uB9DE\uAC8C diffusion \uC2DC\uC2A4\uD15C\uC744 \uAD6C\uCD95 \uD560 \uC218 \uC788\uB3C4\uB85D \uC124\uACC4\uB418\uC5C8\uC2B5\uB2C8\uB2E4. \uC774 \uB3C4\uAD6C \uC0C1\uC790\uC758 \uD575\uC2EC\uC740 \uBAA8\uB378\uACFC \uC2A4\uCF00\uC904\uB7EC\uC785\uB2C8\uB2E4. 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