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kt({props:{$$slots:{default:[Al]},$$scope:{ctx:f}}}),{c(){L(e.$$.fragment)},l(t){I(e.$$.fragment,t)},m(t,l){J(e,t,l),n=!0},p(t,l){const a={};l&2&&(a.$$scope={dirty:l,ctx:t}),e.$set(a)},i(t){n||(_(e.$$.fragment,t),n=!0)},o(t){M(e.$$.fragment,t),n=!1},d(t){H(e,t)}}}function Fl(f){let e,n,t,l,a,u,d,m,C,T="お使いのディープラーニングライブラリに合わせてDiffusersをインストールできます。",E,z,j="🤗 DiffusersはPython 3.8+、PyTorch 1.7.0+、Flaxでテストされています。使用するディープラーニングライブラリの以下のインストール手順に従ってください：",G,R,Q='<li><a href="https://pytorch.org/get-started/locally/" rel="nofollow">PyTorch</a>のインストール手順。</li> <li><a href="https://flax.readthedocs.io/en/latest/" rel="nofollow">Flax</a>のインストール手順。</li>',b,P,D,A,y=`Diffusersは<a href="https://docs.python.org/3/library/venv.html" rel="nofollow">仮想環境</a>の中でインストールすることが推奨されています。
Python の仮想環境についてよく知らない場合は、こちらの <a href="https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/" rel="nofollow">ガイド</a> を参照してください。
仮想環境は異なるプロジェクトの管理を容易にし、依存関係間の互換性の問題を回避します。`,S,V,N="ではさっそく、プロジェクトディレクトリに仮想環境を作ってみます：",W,X,fe,ee,_e="仮想環境をアクティブにします：",oe,te,se,B,we="🤗 Diffusers もまた 🤗 Transformers ライブラリに依存しており、以下のコマンドで両方をインストールできます：",ce,O,ue,F,he,le,ve="ソースから🤗 Diffusersをインストールする前に、<code>torch</code>と🤗 Accelerateがインストールされていることを確認してください。",ne,U,Ke='<code>torch</code>のインストールについては、<code>torch</code> <a href="https://pytorch.org/get-started/locally/#start-locally" rel="nofollow">インストール</a>ガイドを参照してください。',Y,re,Be="🤗 Accelerateをインストールするには：",Ce,q,pe,ae,zt="以下のコマンドでソースから🤗 Diffusersをインストールできます：",tt,xe,lt,ye,Tt=`このコマンドは最新の <code>stable</code> バージョンではなく、最先端の <code>main</code> バージョンをインストールします。
<code>main</code>バージョンは最新の開発に対応するのに便利です。
例えば、前回の公式リリース以降にバグが修正されたが、新しいリリースがまだリリースされていない場合などには都合がいいです。
しかし、これは <code>main</code> バージョンが常に安定しているとは限らないです。
私たちは <code>main</code> バージョンを運用し続けるよう努力しており、ほとんどの問題は通常数時間から1日以内に解決されます。
もし問題が発生した場合は、<a href="https://github.com/huggingface/diffusers/issues/new/choose" rel="nofollow">Issue</a> を開いてください！`,st,ze,nt,Te,Lt="以下の場合、編集可能なインストールが必要です：",rt,Le,Jt="<li>ソースコードの <code>main</code> バージョンを使用する。</li> <li>🤗 Diffusers に貢献し、コードの変更をテストする必要がある場合。</li>",at,Je,Ht="リポジトリをクローンし、次のコマンドで 🤗 Diffusers をインストールしてください：",it,He,ft,Ee,ot,Ie,It=`これらのコマンドは、リポジトリをクローンしたフォルダと Python のライブラリパスをリンクします。
Python は通常のライブラリパスに加えて、クローンしたフォルダの中を探すようになります。
例えば、Python パッケージが通常 <code>~/anaconda3/envs/main/lib/python3.10/site-packages/</code> にインストールされている場合、Python はクローンした <code>~/diffusers/</code> フォルダも同様に参照します。`,ct,Me,jt="<p>ライブラリを使い続けたい場合は、<code>diffusers</code>フォルダを残しておく必要があります。</p>",ut,je,Pt="これで、以下のコマンドで簡単にクローンを最新版の🤗 Diffusersにアップデートできます：",ht,Pe,pt,Se,St="Python環境は次の実行時に <code>main</code> バージョンの🤗 Diffusersを見つけます。",mt,Ne,dt,Ue,Nt=`このライブラリは <code>from_pretrained()</code> リクエスト中にデータを収集します。
このデータには Diffusers と PyTorch/Flax のバージョン、要求されたモデルやパイプラインクラスが含まれます。
また、Hubでホストされている場合は、事前に学習されたチェックポイントへのパスが含まれます。
この使用データは問題のデバッグや新機能の優先順位付けに役立ちます。
テレメトリーはHuggingFace Hubからモデルやパイプラインをロードするときのみ送信されます。ローカルでの使用中は収集されません。`,$t,Ze,Ut=`我々は、すべての人が追加情報を共有したくないことを理解し、あなたのプライバシーを尊重します。
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