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Ht({props:{$$slots:{default:[Ql]},$$scope:{ctx:i}}}),{c(){y(e.$$.fragment)},l(t){I(e.$$.fragment,t)},m(t,l){L(e,t,l),s=!0},p(t,l){const r={};l&2&&(r.$$scope={dirty:l,ctx:t}),e.$set(r)},i(t){s||(v(e.$$.fragment,t),s=!0)},o(t){b(e.$$.fragment,t),s=!1},d(t){H(e,t)}}}function Xl(i){let e,s,t,l,r,u,d,h,C,J="🤗 Diffusers é testado no Python 3.8+, PyTorch 1.7.0+, e Flax. Siga as instruções de instalação abaixo para a biblioteca de deep learning que você está utilizando:",E,z,j='<li><a href="https://pytorch.org/get-started/locally/" rel="nofollow">PyTorch</a> instruções de instalação</li> <li><a href="https://flax.readthedocs.io/en/latest/" rel="nofollow">Flax</a> instruções de instalação</li>',A,R,K,x,N=`Recomenda-se instalar 🤗 Diffusers em um <a href="https://docs.python.org/3/library/venv.html" rel="nofollow">ambiente virtual</a>.
Se você não está familiarizado com ambiente virtuals, veja o <a href="https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/" rel="nofollow">guia</a>.
Um ambiente virtual facilita gerenciar diferentes projetos e evitar problemas de compatibilidade entre dependências.`,q,F,T="Comece criando um ambiente virtual no diretório do projeto:",P,V,S,B,ge="Ative o ambiente virtual:",ie,Y,fe,Q,ve="Recomenda-se a instalação do 🤗 Transformers porque 🤗 Diffusers depende de seus modelos:",se,D,ce,W,ue,X,ne="Antes da instalação do 🤗 Diffusers a partir do código fonte, certifique-se de ter o PyTorch e o 🤗 Accelerate instalados.",pe,ee,_e="Para instalar o 🤗 Accelerate:",re,Z,Me,G,ke="então instale o 🤗 Diffusers do código fonte:",we,oe,te,le,Te=`Esse comando instala a última versão em desenvolvimento <code>main</code> em vez da última versão estável <code>stable</code>.
A versão <code>main</code> é útil para se manter atualizado com os últimos desenvolvimentos.
Por exemplo, se um bug foi corrigido desde o último lançamento estável, mas um novo lançamento ainda não foi lançado.
No entanto, isso significa que a versão <code>main</code> pode não ser sempre estável.
Nós nos esforçamos para manter a versão <code>main</code> operacional, e a maioria dos problemas geralmente são resolvidos em algumas horas ou um dia.
Se você encontrar um problema, por favor abra uma <a href="https://github.com/huggingface/diffusers/issues/new/choose" rel="nofollow">Issue</a>, assim conseguimos arrumar o quanto antes!`,st,ze,nt,ye,jt="Você precisará de uma instalação editável se você:",rt,Le,Pt="<li>Usar a versão <code>main</code> do código fonte.</li> <li>Contribuir para o 🤗 Diffusers e precisa testar mudanças no código.</li>",ot,He,St="Clone o repositório e instale o 🤗 Diffusers com os seguintes comandos:",it,Ie,ft,be,ct,Je,Nt=`Esses comandos irão vincular a pasta que você clonou o repositório e os caminhos das suas bibliotecas Python.
Python então irá procurar dentro da pasta que você clonou além dos caminhos normais das bibliotecas.
Por exemplo, se o pacote python for tipicamente instalado no <code>~/anaconda3/envs/main/lib/python3.10/site-packages/</code>, o Python também irá procurar na pasta <code>~/diffusers/</code> que você clonou.`,ut,Ce,Ut="<p>Você deve manter a pasta <code>diffusers</code> se quiser continuar usando a biblioteca.</p>",pt,je,Zt="Agora você pode facilmente atualizar seu clone para a última versão do 🤗 Diffusers com o seguinte comando:",mt,Pe,ht,Se,Rt="Seu ambiente Python vai encontrar a versão <code>main</code> do 🤗 Diffusers na próxima execução.",dt,Ne,$t,Ue,Vt="Os pesos e os arquivos dos modelos são baixados do Hub para o cache que geralmente é o seu diretório home. Você pode mudar a localização do cache especificando as variáveis de ambiente <code>HF_HOME</code> ou <code>HUGGINFACE_HUB_CACHE</code> ou configurando o parâmetro <code>cache_dir</code> em métodos como <code>from_pretrained()</code>.",gt,Ze,qt="Aquivos em cache permitem que você rode 🤗 Diffusers offline. Para prevenir que o 🤗 Diffusers se conecte à internet, defina a variável de ambiente <code>HF_HUB_OFFLINE</code> para <code>True</code> e o 🤗 Diffusers irá apenas carregar arquivos previamente baixados em cache.",vt,Re,_t,Ve,At='Para mais detalhes de como gerenciar e limpar o cache, olhe o guia de <a href="https://huggingface.co/docs/huggingface_hub/guides/manage-cache" rel="nofollow">caching</a>.',wt,qe,bt,Ae,Ft=`Nossa biblioteca coleta informações de telemetria durante as requisições <code>from_pretrained()</code>.
O dado coletado inclui a versão do 🤗 Diffusers e PyTorch/Flax, o modelo ou classe de pipeline requisitado,
e o caminho para um checkpoint pré-treinado se ele estiver hospedado no Hugging Face Hub.
Esse dado de uso nos ajuda a debugar problemas e priorizar novas funcionalidades.
Telemetria é enviada apenas quando é carregado modelos e pipelines do Hub,
e não é coletado se você estiver carregando arquivos locais.`,Ct,Fe,Dt=`Nos entendemos que nem todo mundo quer compartilhar informações adicionais, e nós respeitamos sua privacidade.
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