import{s as le,n as ee,o as se}from"../chunks/scheduler.94020406.js";import{S as te,i as ne,g as p,s as n,r as u,E as ae,h as o,f as s,c as a,j as Kl,u as i,x as m,k as Ol,y as pe,a as t,v as M,d as J,t as b,w as T}from"../chunks/index.a08c8d92.js";import{C as c}from"../chunks/CodeBlock.b23cf525.js";import{D as oe}from"../chunks/DocNotebookDropdown.d8a25975.js";import{H as K,E as ue}from"../chunks/EditOnGithub.b1bceb47.js";function ie(El){let r,O,L,ll,y,el,h,sl,w,Dl="🤗 Diffusers는 모델, 스케줄러 또는 파이프라인을 Hub에 업로드할 수 있는 <code>PushToHubMixin</code>을 제공합니다. 이는 Hub에 당신의 파일을 저장하는 쉬운 방법이며, 다른 사람들과 작업을 공유할 수도 있습니다. 실제적으로 <code>PushToHubMixin</code>가 동작하는 방식은 다음과 같습니다:",tl,d,Xl="<li>Hub에 리포지토리를 생성합니다.</li> <li>나중에 다시 불러올 수 있도록 모델, 스케줄러 또는 파이프라인 파일을 저장합니다.</li> <li>이러한 파일이 포함된 폴더를 Hub에 업로드합니다.</li>",nl,f,Rl="이 가이드는 <code>PushToHubMixin</code>을 사용하여 Hub에 파일을 업로드하는 방법을 보여줍니다.",al,j,Nl='먼저 액세스 <a href="https://huggingface.co/settings/tokens" rel="nofollow">토큰</a>으로 Hub 계정에 로그인해야 합니다:',pl,I,ol,U,ul,_,Wl="모델을 허브에 푸시하려면 <code>push_to_hub()</code>를 호출하고 Hub에 저장할 모델의 리포지토리 id를 지정합니다:",il,k,Ml,$,Yl='모델의 경우 Hub에 푸시할 가중치의 <a href="loading#checkpoint-variants"><em>변형</em></a>을 지정할 수도 있습니다. 예를 들어, <code>fp16</code> 가중치를 푸시하려면 다음과 같이 하세요:',Jl,B,bl,Z,Hl="<code>push_to_hub()</code> 함수는 모델의 <code>config.json</code> 파일을 저장하고 가중치는 <code>safetensors</code> 형식으로 자동으로 저장됩니다.",Tl,g,xl="이제 Hub의 리포지토리에서 모델을 다시 불러올 수 있습니다:",ml,C,cl,G,rl,V,Ql="스케줄러를 허브에 푸시하려면 <code>push_to_hub()</code>를 호출하고 Hub에 저장할 스케줄러의 리포지토리 id를 지정합니다:",yl,v,hl,E,zl="<code>push_to_hub()</code> 함수는 스케줄러의 <code>scheduler_config.json</code> 파일을 지정된 리포지토리에 저장합니다.",wl,D,Sl="이제 허브의 리포지토리에서 스케줄러를 다시 불러올 수 있습니다:",dl,X,fl,R,jl,N,ql="모든 컴포넌트가 포함된 전체 파이프라인을 Hub로 푸시할 수도 있습니다. 예를 들어, 원하는 파라미터로 <code>StableDiffusionPipeline</code>의 컴포넌트들을 초기화합니다:",Il,W,Ul,Y,Al="모든 컴포넌트들을 <code>StableDiffusionPipeline</code>에 전달하고 <code>push_to_hub()</code>를 호출하여 파이프라인을 Hub로 푸시합니다:",_l,H,kl,x,Fl="<code>push_to_hub()</code> 함수는 각 컴포넌트를 리포지토리의 하위 폴더에 저장합니다. 이제 Hub의 리포지토리에서 파이프라인을 다시 불러올 수 있습니다:",$l,Q,Bl,z,Zl,S,Ll="모델, 스케줄러 또는 파이프라인 파일들을 비공개로 두려면 <code>push_to_hub()</code> 함수에서 <code>private=True</code>를 설정하세요:",gl,q,Cl,A,Pl='비공개 리포지토리는 본인만 볼 수 있으며 다른 사용자는 리포지토리를 복제할 수 없고 리포지토리가 검색 결과에 표시되지 않습니다. 사용자가 비공개 리포지토리의 URL을 가지고 있더라도 <code>404 - Sorry, we can&#39;t find the page you are looking for</code>라는 메시지가 표시됩니다. 비공개 리포지토리에서 모델을 로드하려면 <a href="https://huggingface.co/docs/huggingface_hub/quick-start#login" rel="nofollow">로그인</a> 상태여야 합니다.',Gl,F,Vl,P,vl;return y=new K({props:{title:"파일들을 Hub로 푸시하기",local:"파일들을-hub로-푸시하기",headingTag:"h1"}}),h=new oe({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/push_to_hub.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/ko/pytorch/push_to_hub.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/ko/tensorflow/push_to_hub.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/ko/push_to_hub.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/ko/pytorch/push_to_hub.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/ko/tensorflow/push_to_hub.ipynb"}]}}),I=new c({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMG5vdGVib29rX2xvZ2luJTBBJTBBbm90ZWJvb2tfbG9naW4oKQ==",highlighted:`<span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login

notebook_login()`,wrap:!1}}),U=new K({props:{title:"모델",local:"모델",headingTag:"h2"}}),k=new c({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> ControlNetModel

controlnet = ControlNetModel(
    block_out_channels=(<span class="hljs-number">32</span>, <span class="hljs-number">64</span>),
    layers_per_block=<span class="hljs-number">2</span>,
    in_channels=<span class="hljs-number">4</span>,
    down_block_types=(<span class="hljs-string">&quot;DownBlock2D&quot;</span>, <span class="hljs-string">&quot;CrossAttnDownBlock2D&quot;</span>),
    cross_attention_dim=<span class="hljs-number">32</span>,
    conditioning_embedding_out_channels=(<span class="hljs-number">16</span>, <span class="hljs-number">32</span>),
)
controlnet.push_to_hub(<span class="hljs-string">&quot;my-controlnet-model&quot;</span>)`,wrap:!1}}),B=new c({props:{code:"Y29udHJvbG5ldC5wdXNoX3RvX2h1YiglMjJteS1jb250cm9sbmV0LW1vZGVsJTIyJTJDJTIwdmFyaWFudCUzRCUyMmZwMTYlMjIp",highlighted:'controlnet.push_to_hub(<span class="hljs-string">&quot;my-controlnet-model&quot;</span>, variant=<span class="hljs-string">&quot;fp16&quot;</span>)',wrap:!1}}),C=new c({props:{code:"bW9kZWwlMjAlM0QlMjBDb250cm9sTmV0TW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUyMnlvdXItbmFtZXNwYWNlJTJGbXktY29udHJvbG5ldC1tb2RlbCUyMik=",highlighted:'model = ControlNetModel.from_pretrained(<span class="hljs-string">&quot;your-namespace/my-controlnet-model&quot;</span>)',wrap:!1}}),G=new K({props:{title:"스케줄러",local:"스케줄러",headingTag:"h2"}}),v=new c({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERESU1TY2hlZHVsZXIlMEElMEFzY2hlZHVsZXIlMjAlM0QlMjBERElNU2NoZWR1bGVyKCUwQSUyMCUyMCUyMCUyMGJldGFfc3RhcnQlM0QwLjAwMDg1JTJDJTBBJTIwJTIwJTIwJTIwYmV0YV9lbmQlM0QwLjAxMiUyQyUwQSUyMCUyMCUyMCUyMGJldGFfc2NoZWR1bGUlM0QlMjJzY2FsZWRfbGluZWFyJTIyJTJDJTBBJTIwJTIwJTIwJTIwY2xpcF9zYW1wbGUlM0RGYWxzZSUyQyUwQSUyMCUyMCUyMCUyMHNldF9hbHBoYV90b19vbmUlM0RGYWxzZSUyQyUwQSklMEFzY2hlZHVsZXIucHVzaF90b19odWIoJTIybXktY29udHJvbG5ldC1zY2hlZHVsZXIlMjIp",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DDIMScheduler

scheduler = DDIMScheduler(
    beta_start=<span class="hljs-number">0.00085</span>,
    beta_end=<span class="hljs-number">0.012</span>,
    beta_schedule=<span class="hljs-string">&quot;scaled_linear&quot;</span>,
    clip_sample=<span class="hljs-literal">False</span>,
    set_alpha_to_one=<span class="hljs-literal">False</span>,
)
scheduler.push_to_hub(<span class="hljs-string">&quot;my-controlnet-scheduler&quot;</span>)`,wrap:!1}}),X=new c({props:{code:"c2NoZWR1bGVyJTIwJTNEJTIwRERJTVNjaGVkdWxlci5mcm9tX3ByZXRyYWluZWQoJTIyeW91ci1uYW1lcHNhY2UlMkZteS1jb250cm9sbmV0LXNjaGVkdWxlciUyMik=",highlighted:'scheduler = DDIMScheduler.from_pretrained(<span class="hljs-string">&quot;your-namepsace/my-controlnet-scheduler&quot;</span>)',wrap:!1}}),R=new K({props:{title:"파이프라인",local:"파이프라인",headingTag:"h2"}}),W=new c({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> (
    UNet2DConditionModel,
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionPipeline,
)
<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> CLIPTextModel, CLIPTextConfig, CLIPTokenizer

unet = UNet2DConditionModel(
    block_out_channels=(<span class="hljs-number">32</span>, <span class="hljs-number">64</span>),
    layers_per_block=<span class="hljs-number">2</span>,
    sample_size=<span class="hljs-number">32</span>,
    in_channels=<span class="hljs-number">4</span>,
    out_channels=<span class="hljs-number">4</span>,
    down_block_types=(<span class="hljs-string">&quot;DownBlock2D&quot;</span>, <span class="hljs-string">&quot;CrossAttnDownBlock2D&quot;</span>),
    up_block_types=(<span class="hljs-string">&quot;CrossAttnUpBlock2D&quot;</span>, <span class="hljs-string">&quot;UpBlock2D&quot;</span>),
    cross_attention_dim=<span class="hljs-number">32</span>,
)

scheduler = DDIMScheduler(
    beta_start=<span class="hljs-number">0.00085</span>,
    beta_end=<span class="hljs-number">0.012</span>,
    beta_schedule=<span class="hljs-string">&quot;scaled_linear&quot;</span>,
    clip_sample=<span class="hljs-literal">False</span>,
    set_alpha_to_one=<span class="hljs-literal">False</span>,
)

vae = AutoencoderKL(
    block_out_channels=[<span class="hljs-number">32</span>, <span class="hljs-number">64</span>],
    in_channels=<span class="hljs-number">3</span>,
    out_channels=<span class="hljs-number">3</span>,
    down_block_types=[<span class="hljs-string">&quot;DownEncoderBlock2D&quot;</span>, <span class="hljs-string">&quot;DownEncoderBlock2D&quot;</span>],
    up_block_types=[<span class="hljs-string">&quot;UpDecoderBlock2D&quot;</span>, <span class="hljs-string">&quot;UpDecoderBlock2D&quot;</span>],
    latent_channels=<span class="hljs-number">4</span>,
)

text_encoder_config = CLIPTextConfig(
    bos_token_id=<span class="hljs-number">0</span>,
    eos_token_id=<span class="hljs-number">2</span>,
    hidden_size=<span class="hljs-number">32</span>,
    intermediate_size=<span class="hljs-number">37</span>,
    layer_norm_eps=<span class="hljs-number">1e-05</span>,
    num_attention_heads=<span class="hljs-number">4</span>,
    num_hidden_layers=<span class="hljs-number">5</span>,
    pad_token_id=<span class="hljs-number">1</span>,
    vocab_size=<span class="hljs-number">1000</span>,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained(<span class="hljs-string">&quot;hf-internal-testing/tiny-random-clip&quot;</span>)`,wrap:!1}}),H=new c({props:{code:"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",highlighted:`components = {
    <span class="hljs-string">&quot;unet&quot;</span>: unet,
    <span class="hljs-string">&quot;scheduler&quot;</span>: scheduler,
    <span class="hljs-string">&quot;vae&quot;</span>: vae,
    <span class="hljs-string">&quot;text_encoder&quot;</span>: text_encoder,
    <span class="hljs-string">&quot;tokenizer&quot;</span>: tokenizer,
    <span class="hljs-string">&quot;safety_checker&quot;</span>: <span class="hljs-literal">None</span>,
    <span class="hljs-string">&quot;feature_extractor&quot;</span>: <span class="hljs-literal">None</span>,
}

pipeline = StableDiffusionPipeline(**components)
pipeline.push_to_hub(<span class="hljs-string">&quot;my-pipeline&quot;</span>)`,wrap:!1}}),Q=new c({props:{code:"cGlwZWxpbmUlMjAlM0QlMjBTdGFibGVEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQoJTIyeW91ci1uYW1lc3BhY2UlMkZteS1waXBlbGluZSUyMik=",highlighted:'pipeline = StableDiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;your-namespace/my-pipeline&quot;</span>)',wrap:!1}}),z=new K({props:{title:"비공개",local:"비공개",headingTag:"h2"}}),q=new c({props:{code:"Y29udHJvbG5ldC5wdXNoX3RvX2h1YiglMjJteS1jb250cm9sbmV0LW1vZGVsLXByaXZhdGUlMjIlMkMlMjBwcml2YXRlJTNEVHJ1ZSk=",highlighted:'controlnet.push_to_hub(<span class="hljs-string">&quot;my-controlnet-model-private&quot;</span>, private=<span class="hljs-literal">True</span>)',wrap:!1}}),F=new 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Me='{"title":"파일들을 Hub로 푸시하기","local":"파일들을-hub로-푸시하기","sections":[{"title":"모델","local":"모델","sections":[],"depth":2},{"title":"스케줄러","local":"스케줄러","sections":[],"depth":2},{"title":"파이프라인","local":"파이프라인","sections":[],"depth":2},{"title":"비공개","local":"비공개","sections":[],"depth":2}],"depth":1}';function Je(El){return se(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ye extends te{constructor(r){super(),ne(this,r,Je,ie,le,{})}}export{ye as component};
