import{S as lt,i as st,s as et,e as o,k as J,t as n,w as b,M as tt,c as r,d as s,m as i,a as U,h as p,x as V,b as T,G as e,g as a,y as I,q as C,o as R,B as _,v as at}from"../../chunks/vendor-hf-doc-builder.js";import{T as de}from"../../chunks/Tip-hf-doc-builder.js";import{I as Ke}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as Vl}from"../../chunks/CodeBlock-hf-doc-builder.js";import{D as nt}from"../../chunks/DocNotebookDropdown-hf-doc-builder.js";function pt(O){let c,y,f,h,j,M,d,u,w;return{c(){c=o("p"),y=n("\u{1F4A1} Pytorch\uC758 "),f=o("a"),h=n("\uC7AC\uD604\uC131\uC5D0 \uB300\uD55C \uC120\uC5B8"),j=n("\uB97C \uAF2D \uC77D\uC5B4\uBCF4\uAE38 \uCD94\uCC9C\uD569\uB2C8\uB2E4:"),M=J(),d=o("blockquote"),u=o("p"),w=n(`\uC644\uC804\uD558\uAC8C \uC7AC\uD604\uAC00\uB2A5\uD55C \uACB0\uACFC\uB294 Pytorch \uBC30\uD3EC, \uAC1C\uBCC4\uC801\uC778 \uCEE4\uBC0B, \uD639\uC740 \uB2E4\uB978 \uD50C\uB7AB\uD3FC\uB4E4\uC5D0\uC11C \uBCF4\uC7A5\uB418\uC9C0 \uC54A\uC2B5\uB2C8\uB2E4.
\uB610\uD55C, \uACB0\uACFC\uB294 CPU\uC640 GPU \uC2E4\uD589\uAC04\uC5D0 \uC2EC\uC9C0\uC5B4 \uAC19\uC740 seed\uB97C \uC0AC\uC6A9\uD560 \uB54C\uB3C4 \uC7AC\uD604 \uAC00\uB2A5\uD558\uC9C0 \uC54A\uC744 \uC218 \uC788\uC2B5\uB2C8\uB2E4.`),this.h()},l(m){c=r(m,"P",{});var E=U(c);y=p(E,"\u{1F4A1} Pytorch\uC758 "),f=r(E,"A",{href:!0,rel:!0});var g=U(f);h=p(g,"\uC7AC\uD604\uC131\uC5D0 \uB300\uD55C \uC120\uC5B8"),g.forEach(s),j=p(E,"\uB97C \uAF2D \uC77D\uC5B4\uBCF4\uAE38 \uCD94\uCC9C\uD569\uB2C8\uB2E4:"),E.forEach(s),M=i(m),d=r(m,"BLOCKQUOTE",{});var q=U(d);u=r(q,"P",{});var D=U(u);w=p(D,`\uC644\uC804\uD558\uAC8C \uC7AC\uD604\uAC00\uB2A5\uD55C \uACB0\uACFC\uB294 Pytorch \uBC30\uD3EC, \uAC1C\uBCC4\uC801\uC778 \uCEE4\uBC0B, \uD639\uC740 \uB2E4\uB978 \uD50C\uB7AB\uD3FC\uB4E4\uC5D0\uC11C \uBCF4\uC7A5\uB418\uC9C0 \uC54A\uC2B5\uB2C8\uB2E4.
\uB610\uD55C, \uACB0\uACFC\uB294 CPU\uC640 GPU \uC2E4\uD589\uAC04\uC5D0 \uC2EC\uC9C0\uC5B4 \uAC19\uC740 seed\uB97C \uC0AC\uC6A9\uD560 \uB54C\uB3C4 \uC7AC\uD604 \uAC00\uB2A5\uD558\uC9C0 \uC54A\uC744 \uC218 \uC788\uC2B5\uB2C8\uB2E4.`),D.forEach(s),q.forEach(s),this.h()},h(){T(f,"href","https://pytorch.org/docs/stable/notes/randomness.html"),T(f,"rel","nofollow")},m(m,E){a(m,c,E),e(c,y),e(c,f),e(f,h),e(c,j),a(m,M,E),a(m,d,E),e(d,u),e(u,w)},d(m){m&&s(c),m&&s(M),m&&s(d)}}}function ot(O){let c,y,f,h,j,M,d,u;return{c(){c=o("p"),y=n("\u{1F4A1} \uCC98\uC74C\uC5D0\uB294 \uC2DC\uB4DC\uB97C \uB098\uD0C0\uB0B4\uB294 \uC815\uC218\uAC12 \uB300\uC2E0\uC5D0 "),f=o("code"),h=n("Generator"),j=n(` \uAC1C\uCCB4\uB97C \uD30C\uC774\uD504\uB77C\uC778\uC5D0 \uC804\uB2EC\uD558\uB294 \uAC83\uC774 \uC57D\uAC04 \uBE44\uC9C1\uAD00\uC801\uC77C \uC218 \uC788\uC9C0\uB9CC,
`),M=o("code"),d=n("Generator"),u=n("\uB294 \uC21C\uCC28\uC801\uC73C\uB85C \uC5EC\uB7EC \uD30C\uC774\uD504\uB77C\uC778\uC5D0 \uC804\uB2EC\uB420 \uC218 \uC788\uB294 \\\uB79C\uB364\uC0C1\uD0DC\\\uC774\uAE30 \uB54C\uBB38\uC5D0 PyTorch\uC5D0\uC11C \uD655\uB960\uB860\uC801 \uBAA8\uB378\uC744 \uB2E4\uB8F0 \uB54C \uAD8C\uC7A5\uB418\uB294 \uC124\uACC4\uC785\uB2C8\uB2E4.")},l(w){c=r(w,"P",{});var m=U(c);y=p(m,"\u{1F4A1} \uCC98\uC74C\uC5D0\uB294 \uC2DC\uB4DC\uB97C \uB098\uD0C0\uB0B4\uB294 \uC815\uC218\uAC12 \uB300\uC2E0\uC5D0 "),f=r(m,"CODE",{});var E=U(f);h=p(E,"Generator"),E.forEach(s),j=p(m,` \uAC1C\uCCB4\uB97C \uD30C\uC774\uD504\uB77C\uC778\uC5D0 \uC804\uB2EC\uD558\uB294 \uAC83\uC774 \uC57D\uAC04 \uBE44\uC9C1\uAD00\uC801\uC77C \uC218 \uC788\uC9C0\uB9CC,
`),M=r(m,"CODE",{});var g=U(M);d=p(g,"Generator"),g.forEach(s),u=p(m,"\uB294 \uC21C\uCC28\uC801\uC73C\uB85C \uC5EC\uB7EC \uD30C\uC774\uD504\uB77C\uC778\uC5D0 \uC804\uB2EC\uB420 \uC218 \uC788\uB294 \\\uB79C\uB364\uC0C1\uD0DC\\\uC774\uAE30 \uB54C\uBB38\uC5D0 PyTorch\uC5D0\uC11C \uD655\uB960\uB860\uC801 \uBAA8\uB378\uC744 \uB2E4\uB8F0 \uB54C \uAD8C\uC7A5\uB418\uB294 \uC124\uACC4\uC785\uB2C8\uB2E4."),m.forEach(s)},m(w,m){a(w,c,m),e(c,y),e(c,f),e(f,h),e(c,j),e(c,M),e(M,d),e(c,u)},d(w){w&&s(c)}}}function rt(O){let c,y;return{c(){c=o("p"),y=n(`\u{1F4A1} \uC7AC\uD604\uC131\uC774 \uC911\uC694\uD55C \uACBD\uC6B0\uC5D0\uB294 \uD56D\uC0C1 CPU generator\uB97C \uC804\uB2EC\uD558\uB294 \uAC83\uC774 \uC88B\uC2B5\uB2C8\uB2E4.
\uC131\uB2A5 \uC190\uC2E4\uC740 \uBB34\uC2DC\uD560 \uC218 \uC5C6\uB294 \uACBD\uC6B0\uAC00 \uB9CE\uC73C\uBA70 \uD30C\uC774\uD504\uB77C\uC778\uC774 GPU\uC5D0\uC11C \uC2E4\uD589\uB418\uC5C8\uC744 \uB54C\uBCF4\uB2E4 \uD6E8\uC52C \uB354 \uBE44\uC2B7\uD55C \uAC12\uC744 \uC0DD\uC131\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4.`)},l(f){c=r(f,"P",{});var h=U(c);y=p(h,`\u{1F4A1} \uC7AC\uD604\uC131\uC774 \uC911\uC694\uD55C \uACBD\uC6B0\uC5D0\uB294 \uD56D\uC0C1 CPU generator\uB97C \uC804\uB2EC\uD558\uB294 \uAC83\uC774 \uC88B\uC2B5\uB2C8\uB2E4.
\uC131\uB2A5 \uC190\uC2E4\uC740 \uBB34\uC2DC\uD560 \uC218 \uC5C6\uB294 \uACBD\uC6B0\uAC00 \uB9CE\uC73C\uBA70 \uD30C\uC774\uD504\uB77C\uC778\uC774 GPU\uC5D0\uC11C \uC2E4\uD589\uB418\uC5C8\uC744 \uB54C\uBCF4\uB2E4 \uD6E8\uC52C \uB354 \uBE44\uC2B7\uD55C \uAC12\uC744 \uC0DD\uC131\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4.`),h.forEach(s)},m(f,h){a(f,c,h),e(c,y)},d(f){f&&s(c)}}}function Ut(O){let c,y,f,h,j,M,d,u,w,m,E,g,q,D,Qs,Pl,P,vl,Y,ws,Fl,fl,js,Nl,H,L,bs,Vs,Al,x,Sl,ml,Is,Xl,v,Cs,K,Rs,_s,zl,Ml,ks,ql,W,F,Il,ll,Zs,Cl,Bs,Yl,N,Gs,sl,gs,Ds,Hl,el,Ll,k,Ws,Rl,$s,Os,_l,Ps,vs,xl,ul,Fs,Kl,A,ls,$,S,kl,tl,Ns,Zl,As,ss,yl,Ss,es,al,ts,hl,Xs,as,Q,zs,nl,qs,Ys,Bl,Hs,Ls,Gl,xs,Ks,gl,le,se,ns,dl,ee,ps,pl,os,X,rs,z,te,ol,ae,ne,Us,rl,pe,Js,El,oe,is,Z,re,Ul,Ue,Je,Dl,ie,ce,cs,B,Te,Jl,fe,me,Wl,Me,ue,Ts,il,fs,Ql,ye,ms,cl,Ms;return M=new nt({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/reproducibility.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/ko/pytorch/reproducibility.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/ko/tensorflow/reproducibility.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/ko/reproducibility.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/ko/pytorch/reproducibility.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/ko/tensorflow/reproducibility.ipynb"}]}}),P=new de({props:{$$slots:{default:[pt]},$$scope:{ctx:O}}}),x=new Vl({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDIMPipeline
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

model_id = <span class="hljs-string">&quot;google/ddpm-cifar10-32&quot;</span>

<span class="hljs-comment"># \uBAA8\uB378\uACFC \uC2A4\uCF00\uC904\uB7EC\uB97C \uBD88\uB7EC\uC624\uAE30</span>
ddim = DDIMPipeline.from_pretrained(model_id)

<span class="hljs-comment"># \uB450 \uAC1C\uC758 \uB2E8\uACC4\uC5D0 \uB300\uD574\uC11C \uD30C\uC774\uD504\uB77C\uC778\uC744 \uC2E4\uD589\uD558\uACE0 numpy tensor\uB85C \uAC12\uC744 \uBC18\uD658\uD558\uAE30</span>
image = ddim(num_inference_steps=<span class="hljs-number">2</span>, output_type=<span class="hljs-string">&quot;np&quot;</span>).images
<span class="hljs-built_in">print</span>(np.<span class="hljs-built_in">abs</span>(image).<span class="hljs-built_in">sum</span>())`}}),ll=new Ke({}),el=new Vl({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDIMPipeline
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

model_id = <span class="hljs-string">&quot;google/ddpm-cifar10-32&quot;</span>

<span class="hljs-comment"># \uBAA8\uB378\uACFC \uC2A4\uCF00\uC904\uB7EC \uBD88\uB7EC\uC624\uAE30</span>
ddim = DDIMPipeline.from_pretrained(model_id)

<span class="hljs-comment"># \uC7AC\uD604\uC131\uC744 \uC704\uD574 generator \uB9CC\uB4E4\uAE30</span>
generator = torch.Generator(device=<span class="hljs-string">&quot;cpu&quot;</span>).manual_seed(<span class="hljs-number">0</span>)

<span class="hljs-comment"># \uB450 \uAC1C\uC758 \uB2E8\uACC4\uC5D0 \uB300\uD574\uC11C \uD30C\uC774\uD504\uB77C\uC778\uC744 \uC2E4\uD589\uD558\uACE0 numpy tensor\uB85C \uAC12\uC744 \uBC18\uD658\uD558\uAE30</span>
image = ddim(num_inference_steps=<span class="hljs-number">2</span>, output_type=<span class="hljs-string">&quot;np&quot;</span>, generator=generator).images
<span class="hljs-built_in">print</span>(np.<span class="hljs-built_in">abs</span>(image).<span class="hljs-built_in">sum</span>())`}}),A=new de({props:{$$slots:{default:[ot]},$$scope:{ctx:O}}}),tl=new Ke({}),al=new Vl({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDIMPipeline
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

model_id = <span class="hljs-string">&quot;google/ddpm-cifar10-32&quot;</span>

<span class="hljs-comment"># \uBAA8\uB378\uACFC \uC2A4\uCF00\uC904\uB7EC \uBD88\uB7EC\uC624\uAE30</span>
ddim = DDIMPipeline.from_pretrained(model_id)
ddim.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-comment"># \uC7AC\uD604\uC131\uC744 \uC704\uD55C generator \uB9CC\uB4E4\uAE30</span>
generator = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">0</span>)

<span class="hljs-comment"># \uB450 \uAC1C\uC758 \uB2E8\uACC4\uC5D0 \uB300\uD574\uC11C \uD30C\uC774\uD504\uB77C\uC778\uC744 \uC2E4\uD589\uD558\uACE0 numpy tensor\uB85C \uAC12\uC744 \uBC18\uD658\uD558\uAE30</span>
image = ddim(num_inference_steps=<span class="hljs-number">2</span>, output_type=<span class="hljs-string">&quot;np&quot;</span>, generator=generator).images
<span class="hljs-built_in">print</span>(np.<span class="hljs-built_in">abs</span>(image).<span class="hljs-built_in">sum</span>())`}}),pl=new Vl({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDIMPipeline
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

model_id = <span class="hljs-string">&quot;google/ddpm-cifar10-32&quot;</span>

<span class="hljs-comment"># \uBAA8\uB378\uACFC \uC2A4\uCF00\uC904\uB7EC \uBD88\uB7EC\uC624\uAE30</span>
ddim = DDIMPipeline.from_pretrained(model_id)
ddim.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-comment">#\uC7AC\uD604\uC131\uC744 \uC704\uD55C generator \uB9CC\uB4E4\uAE30 (GPU\uC5D0 \uC62C\uB9AC\uC9C0 \uC54A\uB3C4\uB85D \uC870\uC2EC\uD55C\uB2E4!)</span>
generator = torch.manual_seed(<span class="hljs-number">0</span>)

<span class="hljs-comment"># \uB450 \uAC1C\uC758 \uB2E8\uACC4\uC5D0 \uB300\uD574\uC11C \uD30C\uC774\uD504\uB77C\uC778\uC744 \uC2E4\uD589\uD558\uACE0 numpy tensor\uB85C \uAC12\uC744 \uBC18\uD658\uD558\uAE30</span>
image = ddim(num_inference_steps=<span class="hljs-number">2</span>, output_type=<span class="hljs-string">&quot;np&quot;</span>, generator=generator).images
<span class="hljs-built_in">print</span>(np.<span class="hljs-built_in">abs</span>(image).<span class="hljs-built_in">sum</span>())`}}),X=new de({props:{$$slots:{default:[rt]},$$scope:{ctx:O}}}),il=new Vl({props:{code:"aW1wb3J0JTIwb3MlMEElMEFvcy5lbnZpcm9uJTVCJTIyQ1VCTEFTX1dPUktTUEFDRV9DT05GSUclMjIlNUQlMjAlM0QlMjAlMjIlM0ExNiUzQTglMjIlMEElMEF0b3JjaC5iYWNrZW5kcy5jdWRubi5iZW5jaG1hcmslMjAlM0QlMjBGYWxzZSUwQXRvcmNoLnVzZV9kZXRlcm1pbmlzdGljX2FsZ29yaXRobXMoVHJ1ZSk=",highlighted:`<span class="hljs-keyword">import</span> os

os.environ[<span class="hljs-string">&quot;CUBLAS_WORKSPACE_CONFIG&quot;</span>] = <span class="hljs-string">&quot;:16:8&quot;</span>

torch.backends.cudnn.benchmark = <span class="hljs-literal">False</span>
torch.use_deterministic_algorithms(<span class="hljs-literal">True</span>)`}}),cl=new Vl({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDIMScheduler, StableDiffusionPipeline
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np

model_id = <span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>
pipe = StableDiffusionPipeline.from_pretrained(model_id).to(<span class="hljs-string">&quot;cuda&quot;</span>)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
g = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;A bear is playing a guitar on Times Square&quot;</span>

g.manual_seed(<span class="hljs-number">0</span>)
result1 = pipe(prompt=prompt, num_inference_steps=<span class="hljs-number">50</span>, generator=g, output_type=<span class="hljs-string">&quot;latent&quot;</span>).images

g.manual_seed(<span class="hljs-number">0</span>)
result2 = pipe(prompt=prompt, num_inference_steps=<span class="hljs-number">50</span>, generator=g, output_type=<span class="hljs-string">&quot;latent&quot;</span>).images

<span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;L_inf dist = &quot;</span>, <span class="hljs-built_in">abs</span>(result1 - result2).<span class="hljs-built_in">max</span>())
<span class="hljs-string">&quot;L_inf dist =  tensor(0., device=&#x27;cuda:0&#x27;)&quot;</span>`}}),{c(){c=o("meta"),y=J(),f=o("h1"),h=n("\uC7AC\uD604 \uAC00\uB2A5\uD55C \uD30C\uC774\uD504\uB77C\uC778 \uC0DD\uC131\uD558\uAE30"),j=J(),b(M.$$.fragment),d=J(),u=o("p"),w=n("\uC7AC\uD604\uC131\uC740 \uD14C\uC2A4\uD2B8, \uACB0\uACFC \uC7AC\uD604, \uADF8\uB9AC\uACE0 "),m=o("a"),E=n("\uC774\uBBF8\uC9C0 \uD004\uB9AC\uD2F0 \uB192\uC774\uAE30"),g=n(`\uC5D0\uC11C \uC911\uC694\uD569\uB2C8\uB2E4.
\uADF8\uB7EC\uB098 diffusion \uBAA8\uB378\uC758 \uBB34\uC791\uC704\uC131\uC740 \uB9E4\uBC88 \uBAA8\uB378\uC774 \uB3CC\uC544\uAC08 \uB54C\uB9C8\uB2E4 \uD30C\uC774\uD504\uB77C\uC778\uC774 \uB2E4\uB978 \uC774\uBBF8\uC9C0\uB97C \uC0DD\uC131\uD560 \uC218 \uC788\uB3C4\uB85D \uD558\uB294 \uC774\uC720\uB85C \uD544\uC694\uD569\uB2C8\uB2E4.
\uD50C\uB7AB\uD3FC \uAC04\uC5D0 \uC815\uD655\uD558\uAC8C \uB3D9\uC77C\uD55C \uACB0\uACFC\uB97C \uC5BB\uC744 \uC218\uB294 \uC5C6\uC9C0\uB9CC, \uD2B9\uC815 \uD5C8\uC6A9 \uBC94\uC704 \uB0B4\uC5D0\uC11C \uB9B4\uB9AC\uC2A4 \uBC0F \uD50C\uB7AB\uD3FC \uAC04\uC5D0 \uACB0\uACFC\uB97C \uC7AC\uD604\uD560 \uC218\uB294 \uC788\uC2B5\uB2C8\uB2E4.
\uADF8\uB7FC\uC5D0\uB3C4 diffusion \uD30C\uC774\uD504\uB77C\uC778\uACFC \uCCB4\uD06C\uD3EC\uC778\uD2B8\uC5D0 \uB530\uB77C \uD5C8\uC6A9 \uC624\uCC28\uAC00 \uB2EC\uB77C\uC9D1\uB2C8\uB2E4.`),q=J(),D=o("p"),Qs=n("diffusion \uBAA8\uB378\uC5D0\uC11C \uBB34\uC791\uC704\uC131\uC758 \uC6D0\uCC9C\uC744 \uC81C\uC5B4\uD558\uAC70\uB098 \uACB0\uC815\uB860\uC801 \uC54C\uACE0\uB9AC\uC998\uC744 \uC0AC\uC6A9\uD558\uB294 \uBC29\uBC95\uC744 \uC774\uD574\uD558\uB294 \uAC83\uC774 \uC911\uC694\uD55C \uC774\uC720\uC785\uB2C8\uB2E4."),Pl=J(),b(P.$$.fragment),vl=J(),Y=o("h2"),ws=n("\uBB34\uC791\uC704\uC131 \uC81C\uC5B4\uD558\uAE30"),Fl=J(),fl=o("p"),js=n("\uCD94\uB860\uC5D0\uC11C, \uD30C\uC774\uD504\uB77C\uC778\uC740 \uB178\uC774\uC988\uB97C \uC904\uC774\uAE30 \uC704\uD574 \uAC00\uC6B0\uC2DC\uC548 \uB178\uC774\uC988\uB97C \uC0DD\uC131\uD558\uAC70\uB098 \uC2A4\uCF00\uC904\uB9C1 \uB2E8\uACC4\uC5D0 \uB178\uC774\uC988\uB97C \uB354\uD558\uB294 \uB4F1\uC758 \uB79C\uB364 \uC0D8\uD50C\uB9C1 \uC2E4\uD589\uC5D0 \uD06C\uAC8C \uC758\uC874\uD569\uB2C8\uB2E4,"),Nl=J(),H=o("p"),L=o("a"),bs=n("DDIMPipeline"),Vs=n("\uC5D0\uC11C \uB450 \uCD94\uB860 \uB2E8\uACC4 \uC774\uD6C4\uC758 \uD150\uC11C \uAC12\uC744 \uC0B4\uD3B4\uBCF4\uC138\uC694:"),Al=J(),b(x.$$.fragment),Sl=J(),ml=o("p"),Is=n("\uC704\uC758 \uCF54\uB4DC\uB97C \uC2E4\uD589\uD558\uBA74 \uD558\uB098\uC758 \uAC12\uC774 \uB098\uC624\uC9C0\uB9CC, \uB2E4\uC2DC \uC2E4\uD589\uD558\uBA74 \uB2E4\uB978 \uAC12\uC774 \uB098\uC635\uB2C8\uB2E4. \uBB34\uC2A8 \uC77C\uC774 \uC77C\uC5B4\uB098\uACE0 \uC788\uB294 \uAC78\uAE4C\uC694?"),Xl=J(),v=o("p"),Cs=n("\uD30C\uC774\uD504\uB77C\uC778\uC774 \uC2E4\uD589\uB420 \uB54C\uB9C8\uB2E4, "),K=o("a"),Rs=n("torch.randn"),_s=n(`\uC740
\uB2E8\uACC4\uC801\uC73C\uB85C \uB178\uC774\uC988 \uC81C\uAC70\uB418\uB294 \uAC00\uC6B0\uC2DC\uC548 \uB178\uC774\uC988\uAC00 \uC0DD\uC131\uD558\uAE30 \uC704\uD55C \uB2E4\uB978 \uB79C\uB364 seed\uB97C \uC0AC\uC6A9\uD569\uB2C8\uB2E4.`),zl=J(),Ml=o("p"),ks=n("\uADF8\uB7EC\uB098 \uB3D9\uC77C\uD55C \uC774\uBBF8\uC9C0\uB97C \uC548\uC815\uC801\uC73C\uB85C \uC0DD\uC131\uD574\uC57C \uD558\uB294 \uACBD\uC6B0\uC5D0\uB294 CPU\uC5D0\uC11C \uD30C\uC774\uD504\uB77C\uC778\uC744 \uC2E4\uD589\uD558\uB294\uC9C0 GPU\uC5D0\uC11C \uC2E4\uD589\uD558\uB294\uC9C0\uC5D0 \uB530\uB77C \uB2EC\uB77C\uC9D1\uB2C8\uB2E4."),ql=J(),W=o("h3"),F=o("a"),Il=o("span"),b(ll.$$.fragment),Zs=J(),Cl=o("span"),Bs=n("CPU"),Yl=J(),N=o("p"),Gs=n("CPU\uC5D0\uC11C \uC7AC\uD604 \uAC00\uB2A5\uD55C \uACB0\uACFC\uB97C \uC0DD\uC131\uD558\uB824\uBA74, PyTorch "),sl=o("a"),gs=n("Generator"),Ds=n("\uB85C seed\uB97C \uACE0\uC815\uD569\uB2C8\uB2E4:"),Hl=J(),b(el.$$.fragment),Ll=J(),k=o("p"),Ws=n("\uC774\uC81C \uC704\uC758 \uCF54\uB4DC\uB97C \uC2E4\uD589\uD558\uBA74 seed\uB97C \uAC00\uC9C4 "),Rl=o("code"),$s=n("Generator"),Os=n(" \uAC1D\uCCB4\uAC00 \uD30C\uC774\uD504\uB77C\uC778\uC758 \uBAA8\uB4E0 \uB79C\uB364 \uD568\uC218\uC5D0 \uC804\uB2EC\uB418\uBBC0\uB85C \uD56D\uC0C1 "),_l=o("code"),Ps=n("1491.1711"),vs=n(" \uAC12\uC774 \uCD9C\uB825\uB429\uB2C8\uB2E4."),xl=J(),ul=o("p"),Fs=n("\uD2B9\uC815 \uD558\uB4DC\uC6E8\uC5B4 \uBC0F PyTorch \uBC84\uC804\uC5D0\uC11C \uC774 \uCF54\uB4DC \uC608\uC81C\uB97C \uC2E4\uD589\uD558\uBA74 \uB3D9\uC77C\uD558\uC9C0\uB294 \uC54A\uB354\uB77C\uB3C4 \uC720\uC0AC\uD55C \uACB0\uACFC\uB97C \uC5BB\uC744 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),Kl=J(),b(A.$$.fragment),ls=J(),$=o("h3"),S=o("a"),kl=o("span"),b(tl.$$.fragment),Ns=J(),Zl=o("span"),As=n("GPU"),ss=J(),yl=o("p"),Ss=n("\uC608\uB97C \uB4E4\uBA74, GPU \uC0C1\uC5D0\uC11C \uAC19\uC740 \uCF54\uB4DC \uC608\uC2DC\uB97C \uC2E4\uD589\uD558\uBA74:"),es=J(),b(al.$$.fragment),ts=J(),hl=o("p"),Xs=n("GPU\uAC00 CPU\uC640 \uB2E4\uB978 \uB09C\uC218 \uC0DD\uC131\uAE30\uB97C \uC0AC\uC6A9\uD558\uAE30 \uB54C\uBB38\uC5D0 \uB3D9\uC77C\uD55C \uC2DC\uB4DC\uB97C \uC0AC\uC6A9\uD558\uB354\uB77C\uB3C4 \uACB0\uACFC\uAC00 \uAC19\uC9C0 \uC54A\uC2B5\uB2C8\uB2E4."),as=J(),Q=o("p"),zs=n(`\uC774 \uBB38\uC81C\uB97C \uD53C\uD558\uAE30 \uC704\uD574 \u{1F9E8} Diffusers\uB294 CPU\uC5D0 \uC784\uC758\uC758 \uB178\uC774\uC988\uB97C \uC0DD\uC131\uD55C \uB2E4\uC74C \uD544\uC694\uC5D0 \uB530\uB77C \uD150\uC11C\uB97C GPU\uB85C \uC774\uB3D9\uC2DC\uD0A4\uB294
`),nl=o("a"),qs=n("randn_tensor()"),Ys=n(`\uAE30\uB2A5\uC744 \uAC00\uC9C0\uACE0 \uC788\uC2B5\uB2C8\uB2E4.
`),Bl=o("code"),Hs=n("randn_tensor"),Ls=n(" \uAE30\uB2A5\uC740 \uD30C\uC774\uD504\uB77C\uC778 \uB0B4\uBD80 \uC5B4\uB514\uC5D0\uC11C\uB098 \uC0AC\uC6A9\uB418\uBBC0\uB85C \uD30C\uC774\uD504\uB77C\uC778\uC774 GPU\uC5D0\uC11C \uC2E4\uD589\uB418\uB354\uB77C\uB3C4 "),Gl=o("strong"),xs=n("\uD56D\uC0C1"),Ks=n(" CPU "),gl=o("code"),le=n("Generator"),se=n("\uB97C \uD1B5\uACFC\uD560 \uC218 \uC788\uC2B5\uB2C8\uB2E4."),ns=J(),dl=o("p"),ee=n("\uC774\uC81C \uACB0\uACFC\uC5D0 \uD6E8\uC52C \uB354 \uB2E4\uAC00\uC654\uC2B5\uB2C8\uB2E4!"),ps=J(),b(pl.$$.fragment),os=J(),b(X.$$.fragment),rs=J(),z=o("p"),te=n("\uB9C8\uC9C0\uB9C9\uC73C\uB85C "),ol=o("a"),ae=n("UnCLIPPipeline"),ne=n(`\uACFC \uAC19\uC740
\uB354 \uBCF5\uC7A1\uD55C \uD30C\uC774\uD504\uB77C\uC778\uC758 \uACBD\uC6B0, \uC774\uB4E4\uC740 \uC885\uC885 \uC815\uBC00 \uC624\uCC28 \uC804\uD30C\uC5D0 \uADF9\uB3C4\uB85C \uCDE8\uC57D\uD569\uB2C8\uB2E4.
\uB2E4\uB978 GPU \uD558\uB4DC\uC6E8\uC5B4 \uB610\uB294 PyTorch \uBC84\uC804\uC5D0\uC11C \uC720\uC0AC\uD55C \uACB0\uACFC\uB97C \uAE30\uB300\uD558\uC9C0 \uB9C8\uC138\uC694.
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