import{s as qt,o as Dt,n as mt}from"../chunks/scheduler.94020406.js";import{S as Pt,i as Ot,g as T,s as o,r as u,E as Kt,h as $,f as s,c as f,j as At,u as M,x as _,k as Qt,y as te,a as n,v as d,d as h,t as J,w}from"../chunks/index.a08c8d92.js";import{T as ee}from"../chunks/Tip.3b0aeee8.js";import{C as B}from"../chunks/CodeBlock.f1fae7de.js";import{F as zt,M as It}from"../chunks/Markdown.23159a98.js";import{H as pt,E as le}from"../chunks/getInferenceSnippets.5a84873a.js";function ae(j){let l,r="text-to-image 파인튜닝 스크립트는 experimental 상태입니다. 과적합하기 쉽고 치명적인 망각과 같은 문제에 부딪히기 쉽습니다. 자체 데이터셋에서 최상의 결과를 얻으려면 다양한 하이퍼파라미터를 탐색하는 것이 좋습니다.";return{c(){l=T("p"),l.textContent=r},l(e){l=$(e,"P",{"data-svelte-h":!0}),_(l)!=="svelte-szes5h"&&(l.textContent=r)},m(e,i){n(e,l,i)},p:mt,d(e){e&&s(l)}}}function se(j){let l,r='다음과 같이 <a href="https://huggingface.co/datasets/lambdalabs/naruto-blip-captions" rel="nofollow">Naruto BLIP 캡션</a> 데이터셋에서 파인튜닝 실행을 위해 <a href="https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py" rel="nofollow">PyTorch 학습 스크립트</a>를 실행합니다:',e,i,c,C,g='자체 데이터셋으로 파인튜닝하려면 🤗 <a href="https://huggingface.co/docs/datasets/index" rel="nofollow">Datasets</a>에서 요구하는 형식에 따라 데이터셋을 준비하세요. <a href="https://huggingface.co/docs/datasets/image_dataset#upload-dataset-to-the-hub" rel="nofollow">데이터셋을 허브에 업로드</a>하거나 [파일들이 있는 로컬 폴더를 준비](https ://huggingface.co/docs/datasets/image_dataset#imagefolder)할 수 있습니다.',Z,b,N="사용자 커스텀 loading logic을 사용하려면 스크립트를 수정하십시오. 도움이 되도록 코드의 적절한 위치에 포인터를 남겼습니다. 🤗 아래 예제 스크립트는 <code>TRAIN_DIR</code>의 로컬 데이터셋으로를 파인튜닝하는 방법과 <code>OUTPUT_DIR</code>에서 모델을 저장할 위치를 보여줍니다:",W,U,I;return i=new B({props:{code:"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",highlighted:`<span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>
<span class="hljs-built_in">export</span> dataset_name=<span class="hljs-string">&quot;lambdalabs/naruto-blip-captions&quot;</span>

accelerate launch train_text_to_image.py \\
  --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \\
  --dataset_name=<span class="hljs-variable">$dataset_name</span> \\
  --use_ema \\
  --resolution=512 --center_crop --random_flip \\
  --train_batch_size=1 \\
  --gradient_accumulation_steps=4 \\
  --gradient_checkpointing \\
  --mixed_precision=<span class="hljs-string">&quot;fp16&quot;</span> \\
  --max_train_steps=15000 \\
  --learning_rate=1e-05 \\
  --max_grad_norm=1 \\
  --lr_scheduler=<span class="hljs-string">&quot;constant&quot;</span> --lr_warmup_steps=0 \\
  --output_dir=<span class="hljs-string">&quot;sd-naruto-model&quot;</span>`,wrap:!1}}),U=new B({props:{code:"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",highlighted:`<span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>
<span class="hljs-built_in">export</span> TRAIN_DIR=<span class="hljs-string">&quot;path_to_your_dataset&quot;</span>
<span class="hljs-built_in">export</span> OUTPUT_DIR=<span class="hljs-string">&quot;path_to_save_model&quot;</span>

accelerate launch train_text_to_image.py \\
  --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \\
  --train_data_dir=<span class="hljs-variable">$TRAIN_DIR</span> \\
  --use_ema \\
  --resolution=512 --center_crop --random_flip \\
  --train_batch_size=1 \\
  --gradient_accumulation_steps=4 \\
  --gradient_checkpointing \\
  --mixed_precision=<span class="hljs-string">&quot;fp16&quot;</span> \\
  --max_train_steps=15000 \\
  --learning_rate=1e-05 \\
  --max_grad_norm=1 \\
  --lr_scheduler=<span class="hljs-string">&quot;constant&quot;</span> --lr_warmup_steps=0 \\
  --output_dir=<span class="hljs-variable">\${OUTPUT_DIR}</span>`,wrap:!1}}),{c(){l=T("p"),l.innerHTML=r,e=o(),u(i.$$.fragment),c=o(),C=T("p"),C.innerHTML=g,Z=o(),b=T("p"),b.innerHTML=N,W=o(),u(U.$$.fragment)},l(m){l=$(m,"P",{"data-svelte-h":!0}),_(l)!=="svelte-jxsit4"&&(l.innerHTML=r),e=f(m),M(i.$$.fragment,m),c=f(m),C=$(m,"P",{"data-svelte-h":!0}),_(C)!=="svelte-19wquqk"&&(C.innerHTML=g),Z=f(m),b=$(m,"P",{"data-svelte-h":!0}),_(b)!=="svelte-i9hrjr"&&(b.innerHTML=N),W=f(m),M(U.$$.fragment,m)},m(m,y){n(m,l,y),n(m,e,y),d(i,m,y),n(m,c,y),n(m,C,y),n(m,Z,y),n(m,b,y),n(m,W,y),d(U,m,y),I=!0},p:mt,i(m){I||(h(i.$$.fragment,m),h(U.$$.fragment,m),I=!0)},o(m){J(i.$$.fragment,m),J(U.$$.fragment,m),I=!1},d(m){m&&(s(l),s(e),s(c),s(C),s(Z),s(b),s(W)),w(i,m),w(U,m)}}}function ne(j){let l,r;return l=new It({props:{$$slots:{default:[se]},$$scope:{ctx:j}}}),{c(){u(l.$$.fragment)},l(e){M(l.$$.fragment,e)},m(e,i){d(l,e,i),r=!0},p(e,i){const c={};i&2&&(c.$$scope={dirty:i,ctx:e}),l.$set(c)},i(e){r||(h(l.$$.fragment,e),r=!0)},o(e){J(l.$$.fragment,e),r=!1},d(e){w(l,e)}}}function pe(j){let l,r='<a href="https://github.com/duongna21" rel="nofollow">@duongna211</a>의 기여로, Flax를 사용해 TPU 및 GPU에서 Stable Diffusion 모델을 더 빠르게 학습할 수 있습니다. 이는 TPU 하드웨어에서 매우 효율적이지만 GPU에서도 훌륭하게 작동합니다. Flax 학습 스크립트는 gradient checkpointing나 gradient accumulation과 같은 기능을 아직 지원하지 않으므로 메모리가 30GB 이상인 GPU 또는 TPU v3가 필요합니다.',e,i,c="스크립트를 실행하기 전에 요구 사항이 설치되어 있는지 확인하십시오:",C,g,Z,b,N='그러면 다음과 같이 <a href="https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_flax.py" rel="nofollow">Flax 학습 스크립트</a>를 실행할 수 있습니다.',W,U,I,m,y='자체 데이터셋으로 파인튜닝하려면 🤗 <a href="https://huggingface.co/docs/datasets/index" rel="nofollow">Datasets</a>에서 요구하는 형식에 따라 데이터셋을 준비하세요. <a href="https://huggingface.co/docs/datasets/image_dataset#upload-dataset-to-the-hub" rel="nofollow">데이터셋을 허브에 업로드</a>하거나 [파일들이 있는 로컬 폴더를 준비](https ://huggingface.co/docs/datasets/image_dataset#imagefolder)할 수 있습니다.',F,X,it="사용자 커스텀 loading logic을 사용하려면 스크립트를 수정하십시오. 도움이 되도록 코드의 적절한 위치에 포인터를 남겼습니다. 🤗 아래 예제 스크립트는 <code>TRAIN_DIR</code>의 로컬 데이터셋으로를 파인튜닝하는 방법을 보여줍니다:",V,R,G;return g=new B({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1VJTIwLXIlMjByZXF1aXJlbWVudHNfZmxheC50eHQ=",highlighted:"pip install -U -r requirements_flax.txt",wrap:!1}}),U=new B({props:{code:"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",highlighted:`<span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">&quot;stable-diffusion-v1-5/stable-diffusion-v1-5&quot;</span>
<span class="hljs-built_in">export</span> dataset_name=<span class="hljs-string">&quot;lambdalabs/naruto-blip-captions&quot;</span>

python train_text_to_image_flax.py \\
  --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \\
  --dataset_name=<span class="hljs-variable">$dataset_name</span> \\
  --resolution=512 --center_crop --random_flip \\
  --train_batch_size=1 \\
  --max_train_steps=15000 \\
  --learning_rate=1e-05 \\
  --max_grad_norm=1 \\
  --output_dir=<span class="hljs-string">&quot;sd-naruto-model&quot;</span>`,wrap:!1}}),R=new B({props:{code:"ZXhwb3J0JTIwTU9ERUxfTkFNRSUzRCUyMmR1b25nbmElMkZzdGFibGUtZGlmZnVzaW9uLXYxLTQtZmxheCUyMiUwQWV4cG9ydCUyMFRSQUlOX0RJUiUzRCUyMnBhdGhfdG9feW91cl9kYXRhc2V0JTIyJTBBJTBBcHl0aG9uJTIwdHJhaW5fdGV4dF90b19pbWFnZV9mbGF4LnB5JTIwJTVDJTBBJTIwJTIwLS1wcmV0cmFpbmVkX21vZGVsX25hbWVfb3JfcGF0aCUzRCUyNE1PREVMX05BTUUlMjAlNUMlMEElMjAlMjAtLXRyYWluX2RhdGFfZGlyJTNEJTI0VFJBSU5fRElSJTIwJTVDJTBBJTIwJTIwLS1yZXNvbHV0aW9uJTNENTEyJTIwLS1jZW50ZXJfY3JvcCUyMC0tcmFuZG9tX2ZsaXAlMjAlNUMlMEElMjAlMjAtLXRyYWluX2JhdGNoX3NpemUlM0QxJTIwJTVDJTBBJTIwJTIwLS1taXhlZF9wcmVjaXNpb24lM0QlMjJmcDE2JTIyJTIwJTVDJTBBJTIwJTIwLS1tYXhfdHJhaW5fc3RlcHMlM0QxNTAwMCUyMCU1QyUwQSUyMCUyMC0tbGVhcm5pbmdfcmF0ZSUzRDFlLTA1JTIwJTVDJTBBJTIwJTIwLS1tYXhfZ3JhZF9ub3JtJTNEMSUyMCU1QyUwQSUyMCUyMC0tb3V0cHV0X2RpciUzRCUyMnNkLW5hcnV0by1tb2RlbCUyMg==",highlighted:`<span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">&quot;duongna/stable-diffusion-v1-4-flax&quot;</span>
<span class="hljs-built_in">export</span> TRAIN_DIR=<span class="hljs-string">&quot;path_to_your_dataset&quot;</span>

python train_text_to_image_flax.py \\
  --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \\
  --train_data_dir=<span class="hljs-variable">$TRAIN_DIR</span> \\
  --resolution=512 --center_crop --random_flip \\
  --train_batch_size=1 \\
  --mixed_precision=<span class="hljs-string">&quot;fp16&quot;</span> \\
  --max_train_steps=15000 \\
  --learning_rate=1e-05 \\
  --max_grad_norm=1 \\
  --output_dir=<span class="hljs-string">&quot;sd-naruto-model&quot;</span>`,wrap:!1}}),{c(){l=T("p"),l.innerHTML=r,e=o(),i=T("p"),i.textContent=c,C=o(),u(g.$$.fragment),Z=o(),b=T("p"),b.innerHTML=N,W=o(),u(U.$$.fragment),I=o(),m=T("p"),m.innerHTML=y,F=o(),X=T("p"),X.innerHTML=it,V=o(),u(R.$$.fragment)},l(p){l=$(p,"P",{"data-svelte-h":!0}),_(l)!=="svelte-c7loxj"&&(l.innerHTML=r),e=f(p),i=$(p,"P",{"data-svelte-h":!0}),_(i)!=="svelte-1w9ydbl"&&(i.textContent=c),C=f(p),M(g.$$.fragment,p),Z=f(p),b=$(p,"P",{"data-svelte-h":!0}),_(b)!=="svelte-16f5q4a"&&(b.innerHTML=N),W=f(p),M(U.$$.fragment,p),I=f(p),m=$(p,"P",{"data-svelte-h":!0}),_(m)!=="svelte-19wquqk"&&(m.innerHTML=y),F=f(p),X=$(p,"P",{"data-svelte-h":!0}),_(X)!=="svelte-3igbkc"&&(X.innerHTML=it),V=f(p),M(R.$$.fragment,p)},m(p,x){n(p,l,x),n(p,e,x),n(p,i,x),n(p,C,x),d(g,p,x),n(p,Z,x),n(p,b,x),n(p,W,x),d(U,p,x),n(p,I,x),n(p,m,x),n(p,F,x),n(p,X,x),n(p,V,x),d(R,p,x),G=!0},p:mt,i(p){G||(h(g.$$.fragment,p),h(U.$$.fragment,p),h(R.$$.fragment,p),G=!0)},o(p){J(g.$$.fragment,p),J(U.$$.fragment,p),J(R.$$.fragment,p),G=!1},d(p){p&&(s(l),s(e),s(i),s(C),s(Z),s(b),s(W),s(I),s(m),s(F),s(X),s(V)),w(g,p),w(U,p),w(R,p)}}}function ie(j){let l,r;return l=new It({props:{$$slots:{default:[pe]},$$scope:{ctx:j}}}),{c(){u(l.$$.fragment)},l(e){M(l.$$.fragment,e)},m(e,i){d(l,e,i),r=!0},p(e,i){const c={};i&2&&(c.$$scope={dirty:i,ctx:e}),l.$set(c)},i(e){r||(h(l.$$.fragment,e),r=!0)},o(e){J(l.$$.fragment,e),r=!1},d(e){w(l,e)}}}function re(j){let l,r;return l=new B({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFN0YWJsZURpZmZ1c2lvblBpcGVsaW5lJTBBJTBBbW9kZWxfcGF0aCUyMCUzRCUyMCUyMnBhdGhfdG9fc2F2ZWRfbW9kZWwlMjIlMEFwaXBlJTIwJTNEJTIwU3RhYmxlRGlmZnVzaW9uUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKG1vZGVsX3BhdGglMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYpJTBBcGlwZS50byglMjJjdWRhJTIyKSUwQSUwQWltYWdlJTIwJTNEJTIwcGlwZShwcm9tcHQlM0QlMjJ5b2RhJTIyKS5pbWFnZXMlNUIwJTVEJTBBaW1hZ2Uuc2F2ZSglMjJ5b2RhLW5hcnV0by5wbmclMjIp",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline

model_path = <span class="hljs-string">&quot;path_to_saved_model&quot;</span>
pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

image = pipe(prompt=<span class="hljs-string">&quot;yoda&quot;</span>).images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">&quot;yoda-naruto.png&quot;</span>)`,wrap:!1}}),{c(){u(l.$$.fragment)},l(e){M(l.$$.fragment,e)},m(e,i){d(l,e,i),r=!0},p:mt,i(e){r||(h(l.$$.fragment,e),r=!0)},o(e){J(l.$$.fragment,e),r=!1},d(e){w(l,e)}}}function me(j){let l,r;return l=new It({props:{$$slots:{default:[re]},$$scope:{ctx:j}}}),{c(){u(l.$$.fragment)},l(e){M(l.$$.fragment,e)},m(e,i){d(l,e,i),r=!0},p(e,i){const c={};i&2&&(c.$$scope={dirty:i,ctx:e}),l.$set(c)},i(e){r||(h(l.$$.fragment,e),r=!0)},o(e){J(l.$$.fragment,e),r=!1},d(e){w(l,e)}}}function oe(j){let l,r;return l=new B({props:{code:"aW1wb3J0JTIwamF4JTBBaW1wb3J0JTIwbnVtcHklMjBhcyUyMG5wJTBBZnJvbSUyMGZsYXguamF4X3V0aWxzJTIwaW1wb3J0JTIwcmVwbGljYXRlJTBBZnJvbSUyMGZsYXgudHJhaW5pbmcuY29tbW9uX3V0aWxzJTIwaW1wb3J0JTIwc2hhcmQlMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwRmxheFN0YWJsZURpZmZ1c2lvblBpcGVsaW5lJTBBJTBBbW9kZWxfcGF0aCUyMCUzRCUyMCUyMnBhdGhfdG9fc2F2ZWRfbW9kZWwlMjIlMEFwaXBlJTJDJTIwcGFyYW1zJTIwJTNEJTIwRmxheFN0YWJsZURpZmZ1c2lvblBpcGVsaW5lLmZyb21fcHJldHJhaW5lZChtb2RlbF9wYXRoJTJDJTIwZHR5cGUlM0RqYXgubnVtcHkuYmZsb2F0MTYpJTBBJTBBcHJvbXB0JTIwJTNEJTIwJTIyeW9kYSUyMG5hcnV0byUyMiUwQXBybmdfc2VlZCUyMCUzRCUyMGpheC5yYW5kb20uUFJOR0tleSgwKSUwQW51bV9pbmZlcmVuY2Vfc3RlcHMlMjAlM0QlMjA1MCUwQSUwQW51bV9zYW1wbGVzJTIwJTNEJTIwamF4LmRldmljZV9jb3VudCgpJTBBcHJvbXB0JTIwJTNEJTIwbnVtX3NhbXBsZXMlMjAqJTIwJTVCcHJvbXB0JTVEJTBBcHJvbXB0X2lkcyUyMCUzRCUyMHBpcGVsaW5lLnByZXBhcmVfaW5wdXRzKHByb21wdCklMEElMEElMjMlMjBzaGFyZCUyMGlucHV0cyUyMGFuZCUyMHJuZyUwQXBhcmFtcyUyMCUzRCUyMHJlcGxpY2F0ZShwYXJhbXMpJTBBcHJuZ19zZWVkJTIwJTNEJTIwamF4LnJhbmRvbS5zcGxpdChwcm5nX3NlZWQlMkMlMjBqYXguZGV2aWNlX2NvdW50KCkpJTBBcHJvbXB0X2lkcyUyMCUzRCUyMHNoYXJkKHByb21wdF9pZHMpJTBBJTBBaW1hZ2VzJTIwJTNEJTIwcGlwZWxpbmUocHJvbXB0X2lkcyUyQyUyMHBhcmFtcyUyQyUyMHBybmdfc2VlZCUyQyUyMG51bV9pbmZlcmVuY2Vfc3RlcHMlMkMlMjBqaXQlM0RUcnVlKS5pbWFnZXMlMEFpbWFnZXMlMjAlM0QlMjBwaXBlbGluZS5udW1weV90b19waWwobnAuYXNhcnJheShpbWFnZXMucmVzaGFwZSgobnVtX3NhbXBsZXMlMkMpJTIwJTJCJTIwaW1hZ2VzLnNoYXBlJTVCLTMlM0ElNUQpKSklMEFpbWFnZS5zYXZlKCUyMnlvZGEtbmFydXRvLnBuZyUyMik=",highlighted:`<span class="hljs-keyword">import</span> jax
<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">from</span> flax.jax_utils <span class="hljs-keyword">import</span> replicate
<span class="hljs-keyword">from</span> flax.training.common_utils <span class="hljs-keyword">import</span> shard
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> FlaxStableDiffusionPipeline

model_path = <span class="hljs-string">&quot;path_to_saved_model&quot;</span>
pipe, params = FlaxStableDiffusionPipeline.from_pretrained(model_path, dtype=jax.numpy.bfloat16)

prompt = <span class="hljs-string">&quot;yoda naruto&quot;</span>
prng_seed = jax.random.PRNGKey(<span class="hljs-number">0</span>)
num_inference_steps = <span class="hljs-number">50</span>

num_samples = jax.device_count()
prompt = num_samples * [prompt]
prompt_ids = pipeline.prepare_inputs(prompt)

<span class="hljs-comment"># shard inputs and rng</span>
params = replicate(params)
prng_seed = jax.random.split(prng_seed, jax.device_count())
prompt_ids = shard(prompt_ids)

images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=<span class="hljs-literal">True</span>).images
images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-<span class="hljs-number">3</span>:])))
image.save(<span class="hljs-string">&quot;yoda-naruto.png&quot;</span>)`,wrap:!1}}),{c(){u(l.$$.fragment)},l(e){M(l.$$.fragment,e)},m(e,i){d(l,e,i),r=!0},p:mt,i(e){r||(h(l.$$.fragment,e),r=!0)},o(e){J(l.$$.fragment,e),r=!1},d(e){w(l,e)}}}function fe(j){let l,r;return l=new It({props:{$$slots:{default:[oe]},$$scope:{ctx:j}}}),{c(){u(l.$$.fragment)},l(e){M(l.$$.fragment,e)},m(e,i){d(l,e,i),r=!0},p(e,i){const c={};i&2&&(c.$$scope={dirty:i,ctx:e}),l.$set(c)},i(e){r||(h(l.$$.fragment,e),r=!0)},o(e){J(l.$$.fragment,e),r=!1},d(e){w(l,e)}}}function ce(j){let l,r,e,i,c,C,g,Z,b,N='Stable Diffusion과 같은 text-to-image 모델은 텍스트 프롬프트에서 이미지를 생성합니다. 이 가이드는 PyTorch 및 Flax를 사용하여 자체 데이터셋에서 <a href="https://huggingface.co/CompVis/stable-diffusion-v1-4" rel="nofollow"><code>CompVis/stable-diffusion-v1-4</code></a> 모델로 파인튜닝하는 방법을 보여줍니다. 이 가이드에 사용된 text-to-image 파인튜닝을 위한 모든 학습 스크립트에 관심이 있는 경우 이 <a href="https://github.com/huggingface/diffusers/tree/main/examples/text_to_image" rel="nofollow">리포지토리</a>에서 자세히 찾을 수 있습니다.',W,U,I="스크립트를 실행하기 전에, 라이브러리의 학습 dependency들을 설치해야 합니다:",m,y,F,X,it='그리고 <a href="https://github.com/huggingface/accelerate/" rel="nofollow">🤗Accelerate</a> 환경을 초기화합니다:',V,R,G,p,x="리포지토리를 이미 복제한 경우, 이 단계를 수행할 필요가 없습니다. 대신, 로컬 체크아웃 경로를 학습 스크립트에 명시할 수 있으며 거기에서 로드됩니다.",ot,k,ft,L,Bt='<code>gradient_checkpointing</code> 및 <code>mixed_precision</code>을 사용하면 단일 24GB GPU에서 모델을 파인튜닝할 수 있습니다. 더 높은 <code>batch_size</code>와 더 빠른 훈련을 위해서는 GPU 메모리가 30GB 이상인 GPU를 사용하는 것이 좋습니다. TPU 또는 GPU에서 파인튜닝을 위해 JAX나 Flax를 사용할 수도 있습니다. 자세한 내용은 <a href="#flax-jax-finetuning">아래</a>를 참조하세요.',ct,S,Ft='xFormers로 memory efficient attention을 활성화하여 메모리 사용량 훨씬 더 줄일 수 있습니다. <a href="./optimization/xformers">xFormers가 설치</a>되어 있는지 확인하고 <code>--enable_xformers_memory_efficient_attention</code>를 학습 스크립트에 명시합니다.',ut,Y,Vt="xFormers는 Flax에 사용할 수 없습니다.",Mt,H,dt,A,Gt="학습 스크립트에 다음 인수를 추가하여 모델을 허브에 저장합니다:",ht,Q,Jt,z,wt,q,Nt="학습 중 발생할 수 있는 일에 대비하여 정기적으로 체크포인트를 저장해 두는 것이 좋습니다. 체크포인트를 저장하려면 학습 스크립트에 다음 인수를 명시합니다.",Tt,D,$t,P,vt="500스텝마다 전체 학습 state가 ‘output_dir’의 하위 폴더에 저장됩니다. 체크포인트는 ‘checkpoint-‘에 지금까지 학습된 step 수입니다. 예를 들어 ‘checkpoint-1500’은 1500 학습 step 후에 저장된 체크포인트입니다.",bt,O,Et="학습을 재개하기 위해 체크포인트를 불러오려면 ‘—resume_from_checkpoint’ 인수를 학습 스크립트에 명시하고 재개할 체크포인트를 지정하십시오. 예를 들어 다음 인수는 1500개의 학습 step 후에 저장된 체크포인트에서부터 훈련을 재개합니다.",Ut,K,_t,tt,yt,v,gt,et,jt,lt,kt='Text-to-image 모델 파인튜닝을 위해, 대규모 모델 학습을 가속화하기 위한 파인튜닝 기술인 LoRA(Low-Rank Adaptation of Large Language Models)를 사용할 수 있습니다. 자세한 내용은 <a href="lora#text-to-image">LoRA 학습</a> 가이드를 참조하세요.',xt,at,Ct,st,Lt="허브의 모델 경로 또는 모델 이름을 <code>StableDiffusionPipeline</code>에 전달하여 추론을 위해 파인 튜닝된 모델을 불러올 수 있습니다:",Xt,E,Rt,nt,Zt,rt,Wt;return c=new pt({props:{title:"Text-to-image",local:"text-to-image",headingTag:"h1"}}),g=new ee({props:{warning:!0,$$slots:{default:[ae]},$$scope:{ctx:j}}}),y=new B({props:{code:"cGlwJTIwaW5zdGFsbCUyMGdpdCUyQmh0dHBzJTNBJTJGJTJGZ2l0aHViLmNvbSUyRmh1Z2dpbmdmYWNlJTJGZGlmZnVzZXJzLmdpdCUwQXBpcCUyMGluc3RhbGwlMjAtVSUyMC1yJTIwcmVxdWlyZW1lbnRzLnR4dA==",highlighted:`pip install git+https://github.com/huggingface/diffusers.git
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