Buckets:
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| <link href="/docs/diffusers/main/en/_app/immutable/assets/0.tn0RQdqM.css" rel="modulepreload"> <!--[--><!--[0--><!--[--><!--[0--><!--[--><p></p> <div class="items-center shrink-0 min-w-[100px] max-sm:min-w-[50px] justify-end ml-auto flex" style="float: inline-end; margin-inline-start: 10px; display: inline-flex; position: relative; z-index: 10;"><div dir="ltr" class="inline-flex rounded-md max-sm:rounded-sm"><button class="inline-flex items-center gap-1 h-7 max-sm:h-7 px-2 max-sm:px-1.5 text-sm font-medium text-gray-800 border border-r-0 rounded-l-md max-sm:rounded-l-sm border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-live="polite"><span class="inline-flex items-center justify-center rounded-md p-0.5 max-sm:p-0 hover:text-gray-800 dark:hover:text-gray-200"><svg class="sm:size-3.5 size-3" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----></span> <span dir="auto">Copy page</span></button> <button class="inline-flex items-center justify-center w-6 max-sm:w-5 h-7 max-sm:h-7 disabled:pointer-events-none text-sm text-gray-500 hover:text-gray-700 dark:hover:text-white rounded-r-md max-sm:rounded-r-sm border border-l transition border-gray-200 bg-white hover:shadow-inner dark:border-gray-850 dark:bg-gray-950 dark:text-gray-200 dark:hover:bg-gray-800" aria-haspopup="menu" aria-expanded="false" aria-label="Open copy menu"><svg class="transition-transform text-gray-400 overflow-visible sm:size-3.5 size-3 rotate-0" width="1em" height="1em" viewBox="0 0 12 7" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M1 1L6 6L11 1" stroke="currentColor"></path></svg><!----></button></div> <!--[-1--><!--]--></div><!----> <!--[0--><h1 class="relative group"><a id="dreambooth" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#dreambooth"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span><!---->DreamBooth<!----></span></h1><!--]--><!----> <p><a href="https://huggingface.co/papers/2208.12242" rel="nofollow">DreamBooth</a> é uma técnica de treinamento que atualiza o modelo de difusão inteiro treinando em apenas algumas imagens de um sujeito ou estilo. Ele funciona associando uma palavra especial no prompt às imagens de exemplo.</p> <p>Para carregar um checkpoint treinado para inferência, consulte <a href="../using-diffusers/dreambooth">Carregar um modelo DreamBooth para inferência</a>.</p> <p>Se você estiver treinando em uma GPU com vRAM limitado, deve tentar habilitar os parâmetros <code>gradient_checkpointing</code> e <code>mixed_precision</code> no comando de treinamento. Você também pode reduzir o uso de memória usando atenção eficiente em memória com <a href="../optimization/attention_backends">xFormers</a>.</p> <p>Este guia explorará o script <a href="https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py" rel="nofollow">train_dreambooth.py</a> para ajudá-lo a se familiarizar melhor com ele e como você pode adaptá-lo para o seu próprio caso de uso.</p> <p>Antes de executar o script, certifique-se de instalar a biblioteca a partir do código-fonte:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-bash "><!---->git <span class="hljs-built_in">clone</span> https://github.com/huggingface/diffusers | |
| <span class="hljs-built_in">cd</span> diffusers | |
| pip install .<!----></pre></div><!----> <p>Navegue até a pasta de exemplo com o script de treinamento e instale as dependências necessárias para o script que você está usando:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-bash "><!----><span class="hljs-built_in">cd</span> examples/dreambooth | |
| pip install -r requirements.txt<!----></pre></div><!----> <blockquote class="tip"><p>🤗 Accelerate é uma biblioteca para ajudar você a treinar em múltiplos GPUs/TPUs ou com precisão mista. Ela configura automaticamente seu ambiente de treinamento com base no seu hardware e ambiente. Dê uma olhada no <a href="https://huggingface.co/docs/accelerate/quicktour" rel="nofollow">Quick tour</a> do 🤗 Accelerate para saber mais.</p></blockquote> <p>Inicialize um ambiente do 🤗 Accelerate:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-bash "><!---->accelerate config<!----></pre></div><!----> <p>Para configurar um ambiente padrão do 🤗 Accelerate sem escolher nenhuma configuração:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-bash "><!---->accelerate config default<!----></pre></div><!----> <p>Ou, se o seu ambiente não suporta um shell interativo, como um notebook, você pode usar:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-py "><!----><span class="hljs-keyword">from</span> accelerate.utils <span class="hljs-keyword">import</span> write_basic_config | |
| write_basic_config()<!----></pre></div><!----> <p>Por fim, se você deseja treinar um modelo em seu próprio conjunto de dados, consulte o guia <a href="create_dataset">Criar um conjunto de dados para treinamento</a> para aprender a criar um conjunto de dados que funcione com o script de treinamento.</p> <blockquote class="tip"><p>As seções a seguir destacam partes do script de treinamento que são importantes para entender como modificá-lo, mas não cobrem todos os aspectos do script em detalhes. Se você tiver interesse em aprender mais, sinta-se à vontade para ler o <a href="https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py" rel="nofollow">script</a> e nos informar caso tenha alguma dúvida ou preocupação.</p></blockquote> <!--[1--><h2 class="relative group"><a id="script-parameters" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#script-parameters"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span><!---->Parâmetros do script<!----></span></h2><!--]--><!----> <blockquote class="warning"><p>DreamBooth é muito sensível aos hiperparâmetros de treinamento e é fácil sofrer overfitting. Leia o post no blog <a href="https://huggingface.co/blog/dreambooth" rel="nofollow">Training Stable Diffusion with Dreambooth using 🧨 Diffusers</a> para obter configurações recomendadas para diferentes sujeitos e ajudá-lo a escolher os hiperparâmetros apropriados.</p></blockquote> <p>O script de treinamento oferece muitos parâmetros para personalizar sua execução de treinamento. Todos os parâmetros e suas descrições estão na função <a href="https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L228" rel="nofollow"><code>parse_args()</code></a>. Os parâmetros são definidos com valores padrão que devem funcionar bem sem ajustes, mas você também pode definir seus próprios valores no comando de treinamento, se desejar.</p> <p>Por exemplo, para treinar no formato bf16:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-bash "><!---->accelerate launch train_dreambooth.py \ | |
| --mixed_precision=<span class="hljs-string">"bf16"</span><!----></pre></div><!----> <p>Alguns parâmetros básicos e importantes que você deve conhecer e especificar são:</p> <ul><li><code>--pretrained_model_name_or_path</code>: o nome do modelo no Hub ou um caminho local para o modelo pré-treinado</li> <li><code>--instance_data_dir</code>: caminho para uma pasta contendo o conjunto de dados de treinamento (imagens de exemplo)</li> <li><code>--instance_prompt</code>: o prompt de texto que contém a palavra especial para as imagens de exemplo</li> <li><code>--train_text_encoder</code>: se o text encoder também deve ser treinado</li> <li><code>--output_dir</code>: onde salvar o modelo treinado</li> <li><code>--push_to_hub</code>: se o modelo treinado deve ser enviado para o Hub</li> <li><code>--checkpointing_steps</code>: frequência de salvamento de um checkpoint durante o treinamento do modelo; isso é útil caso, por algum motivo, o treinamento seja interrompido, pois você pode continuar o treinamento a partir desse checkpoint adicionando <code>--resume_from_checkpoint</code> ao seu comando de treinamento</li></ul> <!--[2--><h3 class="relative group"><a id="min-snr-weighting" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#min-snr-weighting"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span><!---->Ponderação Min-SNR<!----></span></h3><!--]--><!----> <p>A estratégia de ponderação <a href="https://huggingface.co/papers/2303.09556" rel="nofollow">Min-SNR</a> pode ajudar no treinamento ao reequilibrar a perda para alcançar uma convergência mais rápida. O script de treinamento suporta a previsão de <code>epsilon</code> (ruído) ou <code>v_prediction</code>, mas o Min-SNR é compatível com ambos os tipos de previsão. Essa estratégia de ponderação é suportada apenas pelo PyTorch.</p> <p>Adicione o parâmetro <code>--snr_gamma</code> e defina-o com o valor recomendado de 5.0:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-bash "><!---->accelerate launch train_dreambooth.py \ | |
| --snr_gamma=5.0<!----></pre></div><!----> <!--[2--><h3 class="relative group"><a id="prior-preservation-loss" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#prior-preservation-loss"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span><!---->Perda de preservação de prioridade<!----></span></h3><!--]--><!----> <p>A perda de preservação de prioridade é um método que utiliza os próprios amostras geradas por um modelo para ajudá-lo a aprender a gerar imagens mais diversas. Como essas imagens de amostra geradas pertencem à mesma classe das imagens fornecidas, elas ajudam o modelo a reter o que ele aprendeu sobre a classe e como pode usar o que já sabe sobre a classe para criar novas composições.</p> <ul><li><code>--with_prior_preservation</code>: se deve usar a perda de preservação de prior</li> <li><code>--prior_loss_weight</code>: controla a influência da perda de preservação do prior no modelo</li> <li><code>--class_data_dir</code>: caminho para uma pasta contendo as imagens de exemplo das classes geradas</li> <li><code>--class_prompt</code>: o prompt de texto descrevendo a classe das imagens de amostra geradas</li></ul> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-bash "><!---->accelerate launch train_dreambooth.py \ | |
| --with_prior_preservation \ | |
| --prior_loss_weight=1.0 \ | |
| --class_data_dir=<span class="hljs-string">"path/to/class/images"</span> \ | |
| --class_prompt=<span class="hljs-string">"text prompt describing class"</span><!----></pre></div><!----> <!--[2--><h3 class="relative group"><a id="train-text-encoder" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#train-text-encoder"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span><!---->Treinar o codificador de texto<!----></span></h3><!--]--><!----> <p>Para melhorar a qualidade das saídas geradas, você também pode treinar o text encoder além do UNet. Isso requer memória adicional e você precisará de uma GPU com pelo menos 24GB de vRAM. Se você tiver o hardware necessário, treinar o text encoder produz melhores resultados, especialmente ao gerar imagens de rostos. Ative essa opção por:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-bash "><!---->accelerate launch train_dreambooth.py \ | |
| --train_text_encoder<!----></pre></div><!----> <!--[1--><h2 class="relative group"><a id="training-script" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#training-script"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span><!---->Script de treinamento<!----></span></h2><!--]--><!----> <p>O DreamBooth vem com suas próprias classes de dataset:</p> <ul><li><a href="https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L604" rel="nofollow"><code>DreamBoothDataset</code></a>: pré-processa as imagens e as imagens de classe, e tokeniza os prompts para o treinamento</li> <li><a href="https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L738" rel="nofollow"><code>PromptDataset</code></a>: gera os embeddings de prompt para gerar as imagens de classe</li></ul> <p>Se você ativou a <a href="https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L842" rel="nofollow">prior preservation loss</a>, as imagens de classe são geradas aqui:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-py "><!---->sample_dataset = PromptDataset(args.class_prompt, num_new_images) | |
| sample_dataloader = torch.utils.data.DataLoader(sample_dataset, batch_size=args.sample_batch_size) | |
| sample_dataloader = accelerator.prepare(sample_dataloader) | |
| pipeline.to(accelerator.device) | |
| <span class="hljs-keyword">for</span> example <span class="hljs-keyword">in</span> tqdm( | |
| sample_dataloader, desc=<span class="hljs-string">"Generating class images"</span>, disable=<span class="hljs-keyword">not</span> accelerator.is_local_main_process | |
| ): | |
| images = pipeline(example[<span class="hljs-string">"prompt"</span>]).images<!----></pre></div><!----> <p>A seguir está a função <a href="https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L799" rel="nofollow"><code>main()</code></a>, que trata da configuração do conjunto de dados para o treinamento e do próprio loop de treinamento. O script carrega o <a href="https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L898" rel="nofollow">tokenizer</a>, o <a href="https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L912C1-L912C1" rel="nofollow">agendador e os modelos</a>:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-py "><!----><span class="hljs-comment"># Load the tokenizer</span> | |
| <span class="hljs-keyword">if</span> args.tokenizer_name: | |
| tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, revision=args.revision, use_fast=<span class="hljs-literal">False</span>) | |
| <span class="hljs-keyword">elif</span> args.pretrained_model_name_or_path: | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| args.pretrained_model_name_or_path, | |
| subfolder=<span class="hljs-string">"tokenizer"</span>, | |
| revision=args.revision, | |
| use_fast=<span class="hljs-literal">False</span>, | |
| ) | |
| <span class="hljs-comment"># Load scheduler and models</span> | |
| noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, subfolder=<span class="hljs-string">"scheduler"</span>) | |
| text_encoder = text_encoder_cls.from_pretrained( | |
| args.pretrained_model_name_or_path, subfolder=<span class="hljs-string">"text_encoder"</span>, revision=args.revision | |
| ) | |
| <span class="hljs-keyword">if</span> model_has_vae(args): | |
| vae = AutoencoderKL.from_pretrained( | |
| args.pretrained_model_name_or_path, subfolder=<span class="hljs-string">"vae"</span>, revision=args.revision | |
| ) | |
| <span class="hljs-keyword">else</span>: | |
| vae = <span class="hljs-literal">None</span> | |
| unet = UNet2DConditionModel.from_pretrained( | |
| args.pretrained_model_name_or_path, subfolder=<span class="hljs-string">"unet"</span>, revision=args.revision | |
| )<!----></pre></div><!----> <p>Em seguida, é hora de <a href="https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L1073" rel="nofollow">criar o conjunto de dados de treinamento</a> e o DataLoader a partir de <code>DreamBoothDataset</code>:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-py "><!---->train_dataset = DreamBoothDataset( | |
| instance_data_root=args.instance_data_dir, | |
| instance_prompt=args.instance_prompt, | |
| class_data_root=args.class_data_dir <span class="hljs-keyword">if</span> args.with_prior_preservation <span class="hljs-keyword">else</span> <span class="hljs-literal">None</span>, | |
| class_prompt=args.class_prompt, | |
| class_num=args.num_class_images, | |
| tokenizer=tokenizer, | |
| size=args.resolution, | |
| center_crop=args.center_crop, | |
| encoder_hidden_states=pre_computed_encoder_hidden_states, | |
| class_prompt_encoder_hidden_states=pre_computed_class_prompt_encoder_hidden_states, | |
| tokenizer_max_length=args.tokenizer_max_length, | |
| ) | |
| train_dataloader = torch.utils.data.DataLoader( | |
| train_dataset, | |
| batch_size=args.train_batch_size, | |
| shuffle=<span class="hljs-literal">True</span>, | |
| collate_fn=<span class="hljs-keyword">lambda</span> examples: collate_fn(examples, args.with_prior_preservation), | |
| num_workers=args.dataloader_num_workers, | |
| )<!----></pre></div><!----> <p>Por fim, o <a href="https://github.com/huggingface/diffusers/blob/072e00897a7cf4302c347a63ec917b4b8add16d4/examples/dreambooth/train_dreambooth.py#L1151" rel="nofollow">training loop</a> cuida das etapas restantes, como converter as imagens para o espaço latente, adicionar ruído à entrada, prever o resíduo de ruído e calcular a perda.</p> <p>Se você quiser saber mais sobre como funciona o loop de treinamento, confira o tutorial <a href="../using-diffusers/write_own_pipeline">Entendendo pipelines, modelos e agendadores</a>, que detalha o padrão básico do processo de remoção de ruído.</p> <!--[1--><h2 class="relative group"><a id="launch-the-script" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#launch-the-script"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span><!---->Inicie o script<!----></span></h2><!--]--><!----> <p>Agora você está pronto para executar o script de treinamento! 🚀</p> <p>Neste guia, você baixará algumas imagens de um <a href="https://huggingface.co/datasets/diffusers/dog-example" rel="nofollow">cachorro</a> e as armazenará em um diretório. Mas lembre-se: você pode criar e usar seu próprio conjunto de dados, se desejar (veja o guia <a href="create_dataset">Criar um conjunto de dados para treinamento</a>).</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-py "><!----><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> snapshot_download | |
| local_dir = <span class="hljs-string">"./dog"</span> | |
| snapshot_download( | |
| <span class="hljs-string">"diffusers/dog-example"</span>, | |
| local_dir=local_dir, | |
| repo_type=<span class="hljs-string">"dataset"</span>, | |
| ignore_patterns=<span class="hljs-string">".gitattributes"</span>, | |
| )<!----></pre></div><!----> <p>Defina a variável de ambiente <code>MODEL_NAME</code> para um id de modelo no Hub ou um caminho para um modelo local, <code>INSTANCE_DIR</code> para o caminho onde você acabou de baixar as imagens de cachorro e <code>OUTPUT_DIR</code> para onde você deseja salvar o modelo. Você usará <code>sks</code> como a palavra especial para vincular o treinamento.</p> <p>Se você estiver interessado em acompanhar o processo de treinamento, pode salvar periodicamente as imagens geradas à medida que o treinamento avança. Adicione os seguintes parâmetros ao comando de treinamento:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-bash "><!---->--validation_prompt=<span class="hljs-string">"a photo of a sks dog"</span> | |
| --num_validation_images=4 | |
| --validation_steps=100<!----></pre></div><!----> <p>Mais uma coisa antes de você executar o script! Dependendo da GPU que você possui, pode ser necessário habilitar certas otimizações para treinar o DreamBooth.</p> <div class="flex space-x-2 items-center my-1.5 mr-8 h-7 !pl-0 -mx-3 md:mx-0"><!--[--><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd border-gray-800 bg-black dark:bg-gray-700 text-white">16GB</div><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd text-gray-500 cursor-pointer opacity-90 hover:text-gray-700 dark:hover:text-gray-200 hover:shadow-sm">12GB</div><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd text-gray-500 cursor-pointer opacity-90 hover:text-gray-700 dark:hover:text-gray-200 hover:shadow-sm">8GB</div><!--]--></div> <div class="language-select"><div><p>Em uma GPU de 16GB, você pode usar o otimizador de 8 bits do bitsandbytes e o gradient checkpointing para ajudar a treinar um modelo DreamBooth. Instale o bitsandbytes:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-py "><!---->pip install bitsandbytes<!----></pre></div><!----> <p>Em seguida, adicione o seguinte parâmetro ao seu comando de treinamento:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-bash "><!---->accelerate launch train_dreambooth.py \ | |
| --gradient_checkpointing \ | |
| --use_8bit_adam \<!----></pre></div><!----><!----></div><!----> <div hidden=""><p>Em uma GPU de 12GB, você precisará do otimizador de 8 bits do bitsandbytes, do gradient checkpointing, do xFormers e definir os gradientes como <code>None</code> em vez de zero para reduzir o uso de memória.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-bash "><!---->accelerate launch train_dreambooth.py \ | |
| --use_8bit_adam \ | |
| --gradient_checkpointing \ | |
| --enable_xformers_memory_efficient_attention \ | |
| --set_grads_to_none \<!----></pre></div><!----><!----></div><!----> <div hidden=""><p>Em uma GPU de 8 GB, você precisará do <a href="https://www.deepspeed.ai/" rel="nofollow">DeepSpeed</a> para transferir alguns dos tensores da vRAM para a CPU ou o NVME, permitindo o treinamento com menos memória de GPU.</p> <p>Execute o seguinte comando para configurar seu ambiente 🤗 Accelerate:</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-bash "><!---->accelerate config<!----></pre></div><!----> <p>Durante a configuração, confirme que deseja usar o DeepSpeed. Agora, deve ser possível treinar com menos de 8 GB de vRAM ao combinar o DeepSpeed stage 2, precisão mista fp16 e o offloading dos parâmetros do modelo e do estado do otimizador para a CPU. A desvantagem é que isso requer mais RAM de sistema (~25 GB). Consulte a <a href="https://huggingface.co/docs/accelerate/usage_guides/deepspeed" rel="nofollow">documentação do DeepSpeed</a> para mais opções de configuração.</p> <p>Você também deve alterar o otimizador Adam padrão para a versão otimizada do Adam do DeepSpeed <a href="https://deepspeed.readthedocs.io/en/latest/optimizers.html#adam-cpu" rel="nofollow"><code>deepspeed.ops.adam.DeepSpeedCPUAdam</code></a> para uma aceleração substancial. A ativação de <code>DeepSpeedCPUAdam</code> exige que a versão da toolchain CUDA do seu sistema seja a mesma da instalada com o PyTorch.</p> <p>Os otimizadores de 8 bits do bitsandbytes não parecem ser compatíveis com o DeepSpeed no momento.</p> <p>É isso! Você não precisa adicionar nenhum parâmetro adicional ao seu comando de treinamento.</p><!----></div><!----><!----></div><!----> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-bash "><!----><span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">"stable-diffusion-v1-5/stable-diffusion-v1-5"</span> | |
| <span class="hljs-built_in">export</span> INSTANCE_DIR=<span class="hljs-string">"./dog"</span> | |
| <span class="hljs-built_in">export</span> OUTPUT_DIR=<span class="hljs-string">"path_to_saved_model"</span> | |
| accelerate launch train_dreambooth.py \ | |
| --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \ | |
| --instance_data_dir=<span class="hljs-variable">$INSTANCE_DIR</span> \ | |
| --output_dir=<span class="hljs-variable">$OUTPUT_DIR</span> \ | |
| --instance_prompt=<span class="hljs-string">"a photo of sks dog"</span> \ | |
| --resolution=512 \ | |
| --train_batch_size=1 \ | |
| --gradient_accumulation_steps=1 \ | |
| --learning_rate=5e-6 \ | |
| --lr_scheduler=<span class="hljs-string">"constant"</span> \ | |
| --lr_warmup_steps=0 \ | |
| --max_train_steps=400 \ | |
| --push_to_hub<!----></pre></div><!----> <p>Quando o treinamento estiver concluído, você poderá usar o seu modelo recém-treinado para inferência!</p> <blockquote class="tip"><p>Não consegue esperar para testar seu modelo para inferência antes de o treinamento ser concluído? 🤭 Certifique-se de ter a versão mais recente do 🤗 Accelerate instalada.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-py "><!----><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline, UNet2DConditionModel | |
| <span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> CLIPTextModel | |
| <span class="hljs-keyword">import</span> torch | |
| unet = UNet2DConditionModel.from_pretrained(<span class="hljs-string">"path/to/model/checkpoint-100/unet"</span>) | |
| <span class="hljs-comment"># if you have trained with `--args.train_text_encoder` make sure to also load the text encoder</span> | |
| text_encoder = CLIPTextModel.from_pretrained(<span class="hljs-string">"path/to/model/checkpoint-100/checkpoint-100/text_encoder"</span>) | |
| pipeline = DiffusionPipeline.from_pretrained( | |
| <span class="hljs-string">"stable-diffusion-v1-5/stable-diffusion-v1-5"</span>, unet=unet, text_encoder=text_encoder, dtype=torch.float16, | |
| ).to(<span class="hljs-string">"cuda"</span>) | |
| image = pipeline(<span class="hljs-string">"A photo of sks dog in a bucket"</span>, num_inference_steps=<span class="hljs-number">50</span>, guidance_scale=<span class="hljs-number">7.5</span>).images[<span class="hljs-number">0</span>] | |
| image.save(<span class="hljs-string">"dog-bucket.png"</span>)<!----></pre></div><!----></blockquote> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-py "><!----><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline | |
| <span class="hljs-keyword">import</span> torch | |
| pipeline = DiffusionPipeline.from_pretrained(<span class="hljs-string">"path_to_saved_model"</span>, dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>).to(<span class="hljs-string">"cuda"</span>) <span class="hljs-comment"># or "mps", "xpu", "cpu"</span> | |
| image = pipeline(<span class="hljs-string">"A photo of sks dog in a bucket"</span>, num_inference_steps=<span class="hljs-number">50</span>, guidance_scale=<span class="hljs-number">7.5</span>).images[<span class="hljs-number">0</span>] | |
| image.save(<span class="hljs-string">"dog-bucket.png"</span>)<!----></pre></div><!----> <!--[1--><h2 class="relative group"><a id="lora" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#lora"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span><!---->LoRA<!----></span></h2><!--]--><!----> <p>LoRA é uma técnica de treinamento para reduzir significativamente o número de parâmetros treináveis. Como resultado, o treinamento é mais rápido e fica mais fácil armazenar os pesos resultantes, pois eles são muito menores (~100MBs). Use o script <a href="https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora.py" rel="nofollow">train_dreambooth_lora.py</a> para treinar com LoRA.</p> <p>O script de treinamento LoRA é discutido em mais detalhes no guia <a href="lora">treinamento LoRA</a>.</p> <!--[1--><h2 class="relative group"><a id="stable-diffusion-xl" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#stable-diffusion-xl"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span><!---->Stable Diffusion XL<!----></span></h2><!--]--><!----> <p>Stable Diffusion XL (SDXL) é um modelo poderoso de texto para imagem que gera imagens em alta resolução e adiciona um segundo text-encoder à sua arquitetura. Use o script <a href="https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora_sdxl.py" rel="nofollow">train_dreambooth_lora_sdxl.py</a> para treinar um modelo SDXL com LoRA.</p> <p>O script de treinamento do SDXL é discutido em mais detalhes no guia de <a href="sdxl">treinamento do SDXL</a>.</p> <!--[1--><h2 class="relative group"><a id="deepfloyd-if" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#deepfloyd-if"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span><!---->DeepFloyd IF<!----></span></h2><!--]--><!----> <p>DeepFloyd IF é um modelo de difusão de pixels em cascata com três estágios. O primeiro estágio gera uma imagem base e os segundo e terceiro estágios aumentam progressivamente a resolução da imagem base até uma imagem de alta resolução de 1024x1024. Use os scripts <a href="https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora.py" rel="nofollow">train_dreambooth_lora.py</a> ou <a href="https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth.py" rel="nofollow">train_dreambooth.py</a> para treinar um modelo DeepFloyd IF com LoRA ou o modelo completo.</p> <p>O DeepFloyd IF usa variância prevista, mas os scripts de treinamento do Diffusers usam erro previsto, de modo que os modelos treinados do DeepFloyd IF são alterados para usar um agendamento de variância fixo. Os scripts de treinamento atualizam a configuração do agendador do modelo totalmente treinado para você. No entanto, ao carregar os pesos LoRA salvos, você também deve atualizar a configuração do agendador do pipeline.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-py "><!----><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline | |
| pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">"DeepFloyd/IF-I-XL-v1.0"</span>, use_safetensors=<span class="hljs-literal">True</span>) | |
| pipe.load_lora_weights(<span class="hljs-string">"<lora weights path>"</span>) | |
| <span class="hljs-comment"># Update scheduler config to fixed variance schedule</span> | |
| pipe.scheduler = pipe.scheduler.__class__.from_config(pipe.scheduler.config, variance_type=<span class="hljs-string">"fixed_small"</span>)<!----></pre></div><!----> <p>O modelo da etapa 2 requer imagens de validação adicionais para aumentar a resolução. Você pode baixar e usar uma versão reduzida das imagens de treinamento para isso.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-py "><!----><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> snapshot_download | |
| local_dir = <span class="hljs-string">"./dog_downsized"</span> | |
| snapshot_download( | |
| <span class="hljs-string">"diffusers/dog-example-downsized"</span>, | |
| local_dir=local_dir, | |
| repo_type=<span class="hljs-string">"dataset"</span>, | |
| ignore_patterns=<span class="hljs-string">".gitattributes"</span>, | |
| )<!----></pre></div><!----> <p>Os exemplos de código abaixo fornecem uma visão geral de como treinar um modelo DeepFloyd IF com uma combinação de DreamBooth e LoRA. Alguns parâmetros importantes a observar são:</p> <ul><li><code>--resolution=64</code>, uma resolução muito menor é necessária porque o DeepFloyd IF é um modelo de difusão de pixels e, para trabalhar com pixels não comprimidos, as imagens de entrada devem ser menores</li> <li><code>--pre_compute_text_embeddings</code>, calcula os embeddings de texto antecipadamente para economizar memória, pois o <a href="https://huggingface.co/docs/transformers/main/en/model_doc/t5#transformers.T5Model" rel="nofollow">T5Model</a> pode consumir bastante memória</li> <li><code>--tokenizer_max_length=77</code>, você pode usar um comprimento de texto padrão maior com o T5 como codificador de texto, mas o procedimento de codificação do modelo padrão usa um comprimento de texto menor</li> <li><code>--text_encoder_use_attention_mask</code>, para passar a máscara de atenção para o codificador de texto</li></ul> <div class="flex space-x-2 items-center my-1.5 mr-8 h-7 !pl-0 -mx-3 md:mx-0"><!--[--><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd border-gray-800 bg-black dark:bg-gray-700 text-white">Etapa 1 LoRA DreamBooth</div><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd text-gray-500 cursor-pointer opacity-90 hover:text-gray-700 dark:hover:text-gray-200 hover:shadow-sm">Etapa 2 LoRA DreamBooth</div><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd text-gray-500 cursor-pointer opacity-90 hover:text-gray-700 dark:hover:text-gray-200 hover:shadow-sm">Etapa 1 DreamBooth</div><div class="flex items-center border rounded-lg px-1.5 py-1 leading-none select-none text-smd text-gray-500 cursor-pointer opacity-90 hover:text-gray-700 dark:hover:text-gray-200 hover:shadow-sm">Etapa 2 DreamBooth</div><!--]--></div> <div class="language-select"><div><p>O estágio 1 de treinamento do DeepFloyd IF com LoRA e DreamBooth requer ~28GB de memória.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-bash "><!----><span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">"DeepFloyd/IF-I-XL-v1.0"</span> | |
| <span class="hljs-built_in">export</span> INSTANCE_DIR=<span class="hljs-string">"dog"</span> | |
| <span class="hljs-built_in">export</span> OUTPUT_DIR=<span class="hljs-string">"dreambooth_dog_lora"</span> | |
| accelerate launch train_dreambooth_lora.py \ | |
| --report_to wandb \ | |
| --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \ | |
| --instance_data_dir=<span class="hljs-variable">$INSTANCE_DIR</span> \ | |
| --output_dir=<span class="hljs-variable">$OUTPUT_DIR</span> \ | |
| --instance_prompt=<span class="hljs-string">"a sks dog"</span> \ | |
| --resolution=64 \ | |
| --train_batch_size=4 \ | |
| --gradient_accumulation_steps=1 \ | |
| --learning_rate=5e-6 \ | |
| --scale_lr \ | |
| --max_train_steps=1200 \ | |
| --validation_prompt=<span class="hljs-string">"a sks dog"</span> \ | |
| --validation_epochs=25 \ | |
| --checkpointing_steps=100 \ | |
| --pre_compute_text_embeddings \ | |
| --tokenizer_max_length=77 \ | |
| --text_encoder_use_attention_mask<!----></pre></div><!----><!----></div><!----> <div hidden=""><p>Para a etapa 2 do DeepFloyd IF com LoRA e DreamBooth, preste atenção nestes parâmetros:</p> <ul><li><code>--validation_images</code>, as imagens para aumentar durante a validação</li> <li><code>--class_labels_conditioning=timesteps</code>, para condicionar adicionalmente o UNet conforme necessário na etapa 2</li> <li><code>--learning_rate=1e-6</code>, uma taxa de aprendizado menor é usada em comparação com a etapa 1</li> <li><code>--resolution=256</code>, a resolução esperada para o upscaler</li></ul> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-bash "><!----><span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">"DeepFloyd/IF-II-L-v1.0"</span> | |
| <span class="hljs-built_in">export</span> INSTANCE_DIR=<span class="hljs-string">"dog"</span> | |
| <span class="hljs-built_in">export</span> OUTPUT_DIR=<span class="hljs-string">"dreambooth_dog_upscale"</span> | |
| <span class="hljs-built_in">export</span> VALIDATION_IMAGES=<span class="hljs-string">"dog_downsized/image_1.png dog_downsized/image_2.png dog_downsized/image_3.png dog_downsized/image_4.png"</span> | |
| python train_dreambooth_lora.py \ | |
| --report_to wandb \ | |
| --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \ | |
| --instance_data_dir=<span class="hljs-variable">$INSTANCE_DIR</span> \ | |
| --output_dir=<span class="hljs-variable">$OUTPUT_DIR</span> \ | |
| --instance_prompt=<span class="hljs-string">"a sks dog"</span> \ | |
| --resolution=256 \ | |
| --train_batch_size=4 \ | |
| --gradient_accumulation_steps=1 \ | |
| --learning_rate=1e-6 \ | |
| --max_train_steps=2000 \ | |
| --validation_prompt=<span class="hljs-string">"a sks dog"</span> \ | |
| --validation_epochs=100 \ | |
| --checkpointing_steps=500 \ | |
| --pre_compute_text_embeddings \ | |
| --tokenizer_max_length=77 \ | |
| --text_encoder_use_attention_mask \ | |
| --validation_images <span class="hljs-variable">$VALIDATION_IMAGES</span> \ | |
| --class_labels_conditioning=timesteps<!----></pre></div><!----><!----></div><!----> <div hidden=""><p>Para a etapa 1 do DeepFloyd IF com DreamBooth, preste atenção nestes parâmetros:</p> <ul><li><code>--skip_save_text_encoder</code>, para pular o salvamento do codificador de texto T5 completo junto com o modelo ajustado</li> <li><code>--use_8bit_adam</code>, para usar o otimizador Adam de 8 bits e economizar memória devido ao tamanho do estado do otimizador ao treinar o modelo completo</li> <li><code>--learning_rate=1e-7</code>, uma taxa de aprendizado muito baixa deve ser usada para o treinamento completo do modelo, caso contrário a qualidade do modelo será degradada (você pode usar uma taxa de aprendizado maior com um tamanho de lote maior)</li></ul> <p>Com o treinamento usando 8-bit Adam e um tamanho de lote de 4, o modelo completo pode ser treinado com ~48GB de memória.</p> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-bash "><!----><span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">"DeepFloyd/IF-I-XL-v1.0"</span> | |
| <span class="hljs-built_in">export</span> INSTANCE_DIR=<span class="hljs-string">"dog"</span> | |
| <span class="hljs-built_in">export</span> OUTPUT_DIR=<span class="hljs-string">"dreambooth_if"</span> | |
| accelerate launch train_dreambooth.py \ | |
| --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \ | |
| --instance_data_dir=<span class="hljs-variable">$INSTANCE_DIR</span> \ | |
| --output_dir=<span class="hljs-variable">$OUTPUT_DIR</span> \ | |
| --instance_prompt=<span class="hljs-string">"a photo of sks dog"</span> \ | |
| --resolution=64 \ | |
| --train_batch_size=4 \ | |
| --gradient_accumulation_steps=1 \ | |
| --learning_rate=1e-7 \ | |
| --max_train_steps=150 \ | |
| --validation_prompt <span class="hljs-string">"a photo of sks dog"</span> \ | |
| --validation_steps 25 \ | |
| --text_encoder_use_attention_mask \ | |
| --tokenizer_max_length 77 \ | |
| --pre_compute_text_embeddings \ | |
| --use_8bit_adam \ | |
| --set_grads_to_none \ | |
| --skip_save_text_encoder \ | |
| --push_to_hub<!----></pre></div><!----><!----></div><!----> <div hidden=""><p>Para a etapa 2 do DeepFloyd IF com DreamBooth, preste atenção nestes parâmetros:</p> <ul><li><code>--learning_rate=5e-6</code>, use uma taxa de aprendizado menor com um tamanho de lote efetivo menor</li> <li><code>--resolution=256</code>, a resolução esperada para o upscaler</li> <li><code>--train_batch_size=2</code> e <code>--gradient_accumulation_steps=6</code>, para treinar efetivamente em imagens com rostos, é necessário usar tamanhos de lote maiores</li></ul> <div class="code-block relative "><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg><!----> <div class=" absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0 "><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent;"></div> Copiado</div><!----></button><!----></div> <pre class="language-bash "><!----><span class="hljs-built_in">export</span> MODEL_NAME=<span class="hljs-string">"DeepFloyd/IF-II-L-v1.0"</span> | |
| <span class="hljs-built_in">export</span> INSTANCE_DIR=<span class="hljs-string">"dog"</span> | |
| <span class="hljs-built_in">export</span> OUTPUT_DIR=<span class="hljs-string">"dreambooth_dog_upscale"</span> | |
| <span class="hljs-built_in">export</span> VALIDATION_IMAGES=<span class="hljs-string">"dog_downsized/image_1.png dog_downsized/image_2.png dog_downsized/image_3.png dog_downsized/image_4.png"</span> | |
| accelerate launch train_dreambooth.py \ | |
| --report_to wandb \ | |
| --pretrained_model_name_or_path=<span class="hljs-variable">$MODEL_NAME</span> \ | |
| --instance_data_dir=<span class="hljs-variable">$INSTANCE_DIR</span> \ | |
| --output_dir=<span class="hljs-variable">$OUTPUT_DIR</span> \ | |
| --instance_prompt=<span class="hljs-string">"a sks dog"</span> \ | |
| --resolution=256 \ | |
| --train_batch_size=2 \ | |
| --gradient_accumulation_steps=6 \ | |
| --learning_rate=5e-6 \ | |
| --max_train_steps=2000 \ | |
| --validation_prompt=<span class="hljs-string">"a sks dog"</span> \ | |
| --validation_steps=150 \ | |
| --checkpointing_steps=500 \ | |
| --pre_compute_text_embeddings \ | |
| --tokenizer_max_length=77 \ | |
| --text_encoder_use_attention_mask \ | |
| --validation_images <span class="hljs-variable">$VALIDATION_IMAGES</span> \ | |
| --class_labels_conditioning timesteps \ | |
| --push_to_hub<!----></pre></div><!----><!----></div><!----><!----></div><!----> <!--[2--><h3 class="relative group"><a id="training-tips" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#training-tips"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span><!---->Dicas de treinamento<!----></span></h3><!--]--><!----> <p>Treinar o modelo DeepFloyd IF pode ser desafiador, mas aqui estão algumas dicas que nos foram úteis:</p> <ul><li>LoRA é suficiente para treinar o modelo da etapa 1, pois a baixa resolução do modelo dificulta a representação de detalhes mais finos, independentemente do método utilizado.</li> <li>Para objetos comuns ou simples, você não necessariamente precisa fazer o ajuste fino do upscaler. Certifique-se de que o prompt passado para o upscaler seja ajustado para remover o novo token do prompt da instância. Por exemplo, se o seu prompt da etapa 1 for “a sks dog”, então o seu prompt da etapa 2 deve ser “a dog”.</li> <li>Para detalhes mais finos, como rostos, treinar completamente o upscaler da etapa 2 é melhor do que treinar o modelo da etapa 2 com LoRA. Também ajuda usar taxas de aprendizado mais baixas com tamanhos de lote maiores.</li> <li>Taxas de aprendizado mais baixas devem ser usadas para treinar o modelo da etapa 2.</li> <li>O <a href="/docs/diffusers/main/pt/api/schedulers/ddpm#diffusers.DDPMScheduler">DDPMScheduler</a> funciona melhor do que o DPMSolver usado nos scripts de treinamento.</li></ul> <!--[1--><h2 class="relative group"><a id="next-steps" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#next-steps"><span><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg><!----></span></a> <span><!---->Próximas etapas<!----></span></h2><!--]--><!----> <p>Parabéns por treinar seu modelo DreamBooth! Para saber mais sobre como usar seu novo modelo, o guia a seguir pode ser útil:</p> <ul><li>Aprenda como <a href="../using-diffusers/dreambooth">carregar um checkpoint do DreamBooth</a> para inferência.</li> <li>Se você treinou com LoRA, consulte <a href="../tutorials/using_peft_for_inference">LoRA para inferência</a>.</li></ul> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/diffusers/blob/main/docs/source/en/training/dreambooth.md" target="_blank"><svg class="mr-1" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M31,16l-7,7l-1.41-1.41L28.17,16l-5.58-5.59L24,9l7,7z"></path><path d="M1,16l7-7l1.41,1.41L3.83,16l5.58,5.59L8,23l-7-7z"></path><path d="M12.419,25.484L17.639,6.552l1.932,0.518L14.351,26.002z"></path></svg><!----> <span><span class="underline">Update</span> on GitHub</span></a><!----> <p></p><!--]--><!----><!--]--><!--]--><!--]--> <!--[-1--><!--]--><!--]--> | |
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