diffusers/pr_12249/ko/_app/immutable/nodes/21.b259fa83.js

import{s as oe,n as pe,o as me}from"../chunks/scheduler.23542ac5.js";import{S as fe,i as de,e as a,s,c as g,h as ce,a as o,d as n,b as i,f as se,g as v,j as U,k as ie,l as ue,m as l,n as w,t as G,o as W,p as J}from"../chunks/index.9b1f405b.js";import{C as re,H as ae,E as be}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.a6a9b8fe.js";import{C as z}from"../chunks/CodeBlock.f3651276.js";function Me(I){let p,j,x,V,m,B,f,L,d,A="많은 diffusion 시스템은 같은 구성 요소들을 공유하므로 한 작업에 대해 사전학습된 모델을 완전히 다른 작업에 적용할 수 있습니다.",k,c,K="이 인페인팅을 위한 가이드는 사전학습된 <code>UNet2DConditionModel</code>의 아키텍처를 초기화하고 수정하여 사전학습된 text-to-image 모델을 어떻게 인페인팅에 적용하는지를 알려줄 것입니다.",E,u,R,r,O='<code>UNet2DConditionModel</code>은 <a href="https://huggingface.co/docs/diffusers/v0.16.0/en/api/models#diffusers.UNet2DConditionModel.in_channels" rel="nofollow">input sample</a>에서 4개의 채널을 기본적으로 허용합니다. 예를 들어,  <a href="https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5" rel="nofollow"><code>stable-diffusion-v1-5/stable-diffusion-v1-5</code></a>와 같은 사전학습된 text-to-image 모델을 불러오고 <code>in_channels</code>의 수를 확인합니다:',N,b,S,M,ee='인페인팅은 입력 샘플에 9개의 채널이 필요합니다. <a href="https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting" rel="nofollow"><code>stable-diffusion-v1-5/stable-diffusion-inpainting</code></a>와 같은 사전학습된 인페인팅 모델에서 이 값을 확인할 수 있습니다:',Q,h,X,$,te="인페인팅에 대한 text-to-image 모델을 적용하기 위해, <code>in_channels</code> 수를 4에서 9로 수정해야 할 것입니다.",H,y,ne="사전학습된 text-to-image 모델의 가중치와 <code>UNet2DConditionModel</code>을 초기화하고 <code>in_channels</code>를 9로 수정해 주세요. <code>in_channels</code>의 수를 수정하면 크기가 달라지기 때문에 크기가 안 맞는 오류를 피하기 위해 <code>ignore_mismatched_sizes=True</code> 및 <code>low_cpu_mem_usage=False</code>를 설정해야 합니다.",P,Z,Y,T,le="Text-to-image 모델로부터 다른 구성 요소의 사전학습된 가중치는 체크포인트로부터 초기화되지만 <code>unet</code>의 입력 채널 가중치 (<code>conv_in.weight</code>)는 랜덤하게 초기화됩니다. 그렇지 않으면 모델이 노이즈를 리턴하기 때문에 인페인팅의 모델을 파인튜닝 할 때 중요합니다.",F,_,D,C,q;return m=new re({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),f=new ae({props:{title:"새로운 작업에 대한 모델을 적용하기",local:"새로운-작업에-대한-모델을-적용하기",headingTag:"h1"}}),u=new ae({props:{title:"UNet2DConditionModel 파라미터 구성",local:"unet2dconditionmodel-파라미터-구성",headingTag:"h2"}}),b=new z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFN0YWJsZURpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBTdGFibGVEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTIyc3RhYmxlLWRpZmZ1c2lvbi12MS01JTJGc3RhYmxlLWRpZmZ1c2lvbi12MS01JTIyKSUwQXBpcGVsaW5lLnVuZXQuY29uZmlnJTVCJTIyaW5fY2hhbm5lbHMlMjIlNUQlMEE0",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline

pipeline = StableDiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;stable-diffusion-v1-5/stable-diffusion-v1-5&quot;</span>)
pipeline.unet.config[<span class="hljs-string">&quot;in_channels&quot;</span>]
<span class="hljs-number">4</span>`,wrap:!1}}),h=new z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFN0YWJsZURpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcGlwZWxpbmUlMjAlM0QlMjBTdGFibGVEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTIyc3RhYmxlLWRpZmZ1c2lvbi12MS01JTJGc3RhYmxlLWRpZmZ1c2lvbi1pbnBhaW50aW5nJTIyKSUwQXBpcGVsaW5lLnVuZXQuY29uZmlnJTVCJTIyaW5fY2hhbm5lbHMlMjIlNUQlMEE5",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline

pipeline = StableDiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;stable-diffusion-v1-5/stable-diffusion-inpainting&quot;</span>)
pipeline.unet.config[<span class="hljs-string">&quot;in_channels&quot;</span>]
<span class="hljs-number">9</span>`,wrap:!1}}),Z=new z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFVOZXQyRENvbmRpdGlvbk1vZGVsJTBBJTBBbW9kZWxfaWQlMjAlM0QlMjAlMjJzdGFibGUtZGlmZnVzaW9uLXYxLTUlMkZzdGFibGUtZGlmZnVzaW9uLXYxLTUlMjIlMEF1bmV0JTIwJTNEJTIwVU5ldDJEQ29uZGl0aW9uTW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMG1vZGVsX2lkJTJDJTIwc3ViZm9sZGVyJTNEJTIydW5ldCUyMiUyQyUyMGluX2NoYW5uZWxzJTNEOSUyQyUyMGxvd19jcHVfbWVtX3VzYWdlJTNERmFsc2UlMkMlMjBpZ25vcmVfbWlzbWF0Y2hlZF9zaXplcyUzRFRydWUlMEEp",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> UNet2DConditionModel

model_id = <span class="hljs-string">&quot;stable-diffusion-v1-5/stable-diffusion-v1-5&quot;</span>
unet = UNet2DConditionModel.from_pretrained(
    model_id, subfolder=<span class="hljs-string">&quot;unet&quot;</span>, in_channels=<span class="hljs-number">9</span>, low_cpu_mem_usage=<span class="hljs-literal">False</span>, ignore_mismatched_sizes=<span class="hljs-literal">True</span>
)`,wrap:!1}}),_=new be({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/ko/training/adapt_a_model.md"}}),{c(){p=a("meta"),j=s(),x=a("p"),V=s(),g(m.$$.fragment),B=s(),g(f.$$.fragment),L=s(),d=a("p"),d.textContent=A,k=s(),c=a("p"),c.innerHTML=K,E=s(),g(u.$$.fragment),R=s(),r=a("p"),r.innerHTML=O,N=s(),g(b.$$.fragment),S=s(),M=a("p"),M.innerHTML=ee,Q=s(),g(h.$$.fragment),X=s(),$=a("p"),$.innerHTML=te,H=s(),y=a("p"),y.innerHTML=ne,P=s(),g(Z.$$.fragment),Y=s(),T=a("p"),T.innerHTML=le,F=s(),g(_.$$.fragment),D=s(),C=a("p"),this.h()},l(e){const t=ce("svelte-u9bgzb",document.head);p=o(t,"META",{name:!0,content:!0}),t.forEach(n),j=i(e),x=o(e,"P",{}),se(x).forEach(n),V=i(e),v(m.$$.fragment,e),B=i(e),v(f.$$.fragment,e),L=i(e),d=o(e,"P",{"data-svelte-h":!0}),U(d)!=="svelte-obt1bb"&&(d.textContent=A),k=i(e),c=o(e,"P",{"data-svelte-h":!0}),U(c)!=="svelte-1bn5mvh"&&(c.innerHTML=K),E=i(e),v(u.$$.fragment,e),R=i(e),r=o(e,"P",{"data-svelte-h":!0}),U(r)!=="svelte-1ie49sx"&&(r.innerHTML=O),N=i(e),v(b.$$.fragment,e),S=i(e),M=o(e,"P",{"data-svelte-h":!0}),U(M)!=="svelte-1k6wu8p"&&(M.innerHTML=ee),Q=i(e),v(h.$$.fragment,e),X=i(e),$=o(e,"P",{"data-svelte-h":!0}),U($)!=="svelte-1nkn0wh"&&($.innerHTML=te),H=i(e),y=o(e,"P",{"data-svelte-h":!0}),U(y)!=="svelte-oj0rfs"&&(y.innerHTML=ne),P=i(e),v(Z.$$.fragment,e),Y=i(e),T=o(e,"P",{"data-svelte-h":!0}),U(T)!=="svelte-rfmg1f"&&(T.innerHTML=le),F=i(e),v(_.$$.fragment,e),D=i(e),C=o(e,"P",{}),se(C).forEach(n),this.h()},h(){ie(p,"name","hf:doc:metadata"),ie(p,"content",he)},m(e,t){ue(document.head,p),l(e,j,t),l(e,x,t),l(e,V,t),w(m,e,t),l(e,B,t),w(f,e,t),l(e,L,t),l(e,d,t),l(e,k,t),l(e,c,t),l(e,E,t),w(u,e,t),l(e,R,t),l(e,r,t),l(e,N,t),w(b,e,t),l(e,S,t),l(e,M,t),l(e,Q,t),w(h,e,t),l(e,X,t),l(e,$,t),l(e,H,t),l(e,y,t),l(e,P,t),w(Z,e,t),l(e,Y,t),l(e,T,t),l(e,F,t),w(_,e,t),l(e,D,t),l(e,C,t),q=!0},p:pe,i(e){q||(G(m.$$.fragment,e),G(f.$$.fragment,e),G(u.$$.fragment,e),G(b.$$.fragment,e),G(h.$$.fragment,e),G(Z.$$.fragment,e),G(_.$$.fragment,e),q=!0)},o(e){W(m.$$.fragment,e),W(f.$$.fragment,e),W(u.$$.fragment,e),W(b.$$.fragment,e),W(h.$$.fragment,e),W(Z.$$.fragment,e),W(_.$$.fragment,e),q=!1},d(e){e&&(n(j),n(x),n(V),n(B),n(L),n(d),n(k),n(c),n(E),n(R),n(r),n(N),n(S),n(M),n(Q),n(X),n($),n(H),n(y),n(P),n(Y),n(T),n(F),n(D),n(C)),n(p),J(m,e),J(f,e),J(u,e),J(b,e),J(h,e),J(Z,e),J(_,e)}}}const he='{"title":"새로운 작업에 대한 모델을 적용하기","local":"새로운-작업에-대한-모델을-적용하기","sections":[{"title":"UNet2DConditionModel 파라미터 구성","local":"unet2dconditionmodel-파라미터-구성","sections":[],"depth":2}],"depth":1}';function $e(I){return me(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ge extends fe{constructor(p){super(),de(this,p,$e,Me,oe,{})}}export{ge as component};