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<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{&quot;title&quot;:&quot;NeMo Automodel&quot;,&quot;local&quot;:&quot;nemo-automodel&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;Model yang didukung&quot;,&quot;local&quot;:&quot;supported-models&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Instalasi&quot;,&quot;local&quot;:&quot;installation&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;Kebutuhan perangkat keras&quot;,&quot;local&quot;:&quot;hardware-requirements&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3}],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Persiapan data&quot;,&quot;local&quot;:&quot;data-preparation&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;Format output&quot;,&quot;local&quot;:&quot;output-format&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3}],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Konfigurasi pelatihan&quot;,&quot;local&quot;:&quot;training-configuration&quot;,&quot;sections&quot;:[{&quot;title&quot;:&quot;Referensi bidang konfigurasi&quot;,&quot;local&quot;:&quot;config-field-reference&quot;,&quot;sections&quot;:[],&quot;depth&quot;:3}],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Mulai pelatihan&quot;,&quot;local&quot;:&quot;launch-training&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Generasi&quot;,&quot;local&quot;:&quot;generation&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Integrasi Diffusers&quot;,&quot;local&quot;:&quot;diffusers-integration&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Tim NVIDIA&quot;,&quot;local&quot;:&quot;nvidia-team&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2},{&quot;title&quot;:&quot;Sumber daya&quot;,&quot;local&quot;:&quot;resources&quot;,&quot;sections&quot;:[],&quot;depth&quot;:2}],&quot;depth&quot;:1}"/>
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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="nemo-automodel" 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="#nemo-automodel"><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><!---->NeMo Automodel<!----></span></h1><!--]--><!----> <p><a href="https://github.com/NVIDIA-NeMo/Automodel" rel="nofollow">NeMo Automodel</a> adalah pustaka pelatihan native PyTorch DTensor dari NVIDIA untuk fine-tuning dan pretraining model difusi berskala besar. Pustaka ini native Hugging Face — latih model berformat Diffusers mana pun dari Hub tanpa perlu konversi checkpoint. Resep YAML dan skrip pelatihan yang dapat dimodifikasi yang sama berjalan pada skala apa pun, mulai dari 1 GPU hingga ratusan node, dengan pelatihan terdistribusi <a href="https://pytorch.org/docs/stable/fsdp.html" rel="nofollow">FSDP2</a>, pemuatan data berbucket multiresolusi, dan pelatihan ruang laten pra-terkodekan untuk pemanfaatan GPU maksimal. Pustaka ini menggunakan <a href="https://huggingface.co/papers/2210.02747" rel="nofollow">flow matching</a> untuk pelatihan dan sepenuhnya open source (Apache 2.0), didukung oleh NVIDIA, serta dipelihara secara aktif.</p> <p>NeMo Automodel terintegrasi langsung dengan Diffusers. Ia memuat model pralatih dari Hugging Face Hub menggunakan kelas model Diffusers dan menghasilkan output dengan <a href="/docs/diffusers/main/id/api/pipelines/overview#diffusers.DiffusionPipeline">DiffusionPipeline</a>.</p> <p>Alur kerja yang umum adalah menginstal NeMo Automodel (pip atau Docker), menyiapkan data Anda dengan mengenkodnya ke dalam file <code>.meta</code>, mengonfigurasi resep YAML, meluncurkan pelatihan dengan <code>torchrun</code>, dan menjalankan inferensi dengan checkpoint yang dihasilkan.</p> <!--[1--><h2 class="relative group"><a id="supported-models" 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="#supported-models"><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><!---->Model yang didukung<!----></span></h2><!--]--><!----> <table><thead><tr><th>Model</th><th>ID Hugging Face</th><th>Tugas</th><th>Parameter</th><th>Kasus penggunaan</th></tr></thead><tbody><tr><td>Wan 2.1 T2V 1.3B</td><td><a href="https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers" rel="nofollow">Wan-AI/Wan2.1-T2V-1.3B-Diffusers</a></td><td>Teks-ke-Video</td><td>1.3B</td><td>generasi video pada perangkat keras terbatas (muat di satu A100 40GB)</td></tr><tr><td>FLUX.1-dev</td><td><a href="https://huggingface.co/black-forest-labs/FLUX.1-dev" rel="nofollow">black-forest-labs/FLUX.1-dev</a></td><td>Teks-ke-Gambar</td><td>12B</td><td>generasi gambar berkualitas tinggi</td></tr><tr><td>HunyuanVideo 1.5</td><td><a href="https://huggingface.co/hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v" rel="nofollow">hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v</a></td><td>Teks-ke-Video</td><td>13B</td><td>generasi video berkualitas tinggi</td></tr></tbody></table> <!--[1--><h2 class="relative group"><a id="installation" 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="#installation"><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><!---->Instalasi<!----></span></h2><!--]--><!----> <!--[2--><h3 class="relative group"><a id="hardware-requirements" 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="#hardware-requirements"><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><!---->Kebutuhan perangkat keras<!----></span></h3><!--]--><!----> <table><thead><tr><th>Komponen</th><th>Minimum</th><th>Direkomendasikan</th></tr></thead><tbody><tr><td>GPU</td><td>A100 40GB</td><td>A100 80GB / H100</td></tr><tr><td>GPU</td><td>4</td><td>8+</td></tr><tr><td>RAM</td><td>128 GB</td><td>256 GB+</td></tr><tr><td>Penyimpanan</td><td>SSD 500 GB</td><td>2 TB NVMe</td></tr></tbody></table> <p>Install NeMo Automodel dengan pip. Untuk seluruh metode instalasi (termasuk dari source), lihat <a href="https://docs.nvidia.com/nemo/automodel/latest/guides/installation.html" rel="nofollow">panduan instalasi NeMo Automodel</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> Disalin</div><!----></button><!----></div> <pre class="language-bash "><!---->pip3 install nemo-automodel<!----></pre></div><!----> <p>Sebagai alternatif, gunakan kontainer Docker yang sudah dibuat sebelumnya yang mencakup semua dependensi.</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> Disalin</div><!----></button><!----></div> <pre class="language-bash "><!---->docker pull nvcr.io/nvidia/nemo-automodel:26.02.00
docker run --gpus all -it --<span class="hljs-built_in">rm</span> --shm-size=8g nvcr.io/nvidia/nemo-automodel:26.02.00<!----></pre></div><!----> <blockquote class="warning"><p>Checkpoint akan hilang saat container keluar kecuali Anda bind-mount direktori checkpoint ke host. Misalnya, tambahkan <code>-v /host/path/checkpoints:/workspace/checkpoints</code> ke perintah <code>docker run</code>.</p></blockquote> <!--[1--><h2 class="relative group"><a id="data-preparation" 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="#data-preparation"><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><!---->Persiapan data<!----></span></h2><!--]--><!----> <p>NeMo Automodel melatih model difusi di ruang laten. Gambar atau video mentah harus dipreproses menjadi file <code>.meta</code> yang berisi laten VAE dan embedding teks sebelum pelatihan. Hal ini menghindari pengkodean ulang pada setiap langkah pelatihan.</p> <p>Gunakan alat pra-pemrosesan bawaan untuk mengkodekan data Anda. Alat ini secara otomatis mendistribusikan pekerjaan di semua GPU yang tersedia.</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">pra-pemrosesan video</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">pra-pemrosesan gambar</div><!--]--></div> <div class="language-select"><div><p>Perintah pra-pemrosesan video sama untuk Wan 2.1 dan HunyuanVideo, tetapi flag-nya berbeda. Wan 2.1 menggunakan <code>--processor wan</code> dengan <code>--resolution_preset</code> dan <code>--caption_format sidecar</code>, sedangkan HunyuanVideo menggunakan <code>--processor hunyuan</code> dengan <code>--target_frames</code> untuk mengatur jumlah frame dan <code>--caption_format meta_json</code>.</p> <p><strong>Wan 2.1:</strong></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> Disalin</div><!----></button><!----></div> <pre class="language-bash "><!---->python -m tools.diffusion.preprocessing_multiprocess video \
--video_dir /data/videos \
--output_dir /cache \
--processor wan \
--resolution_preset 512p \
--caption_format sidecar<!----></pre></div><!----> <p><strong>HunyuanVideo:</strong></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> Disalin</div><!----></button><!----></div> <pre class="language-bash "><!---->python -m tools.diffusion.preprocessing_multiprocess video \
--video_dir /data/videos \
--output_dir /cache \
--processor hunyuan \
--target_frames 121 \
--caption_format meta_json<!----></pre></div><!----><!----></div><!----> <div hidden=""><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> Disalin</div><!----></button><!----></div> <pre class="language-bash "><!---->python -m tools.diffusion.preprocessing_multiprocess image \
--image_dir /data/images \
--output_dir /cache \
--processor flux \
--resolution_preset 512p<!----></pre></div><!----></div><!----><!----></div><!----> <!--[2--><h3 class="relative group"><a id="output-format" 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="#output-format"><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><!---->Format output<!----></span></h3><!--]--><!----> <p>Preprocessing menghasilkan direktori cache yang diorganisasi berdasarkan bucket resolusi. NeMo Automodel mendukung pelatihan multi-resolusi melalui bucketed sampling. Sampel dikelompokkan berdasarkan resolusi spasial sehingga setiap batch berisi sampel berukuran sama, menghindari pemborosan padding.</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> Disalin</div><!----></button><!----></div> <pre class=" "><!----><span class="hljs-string">/cache/</span>
├── 512x512/ <span class="hljs-comment"># Resolution bucket</span>
│ ├── &lt;hash1&gt;<span class="hljs-string">.meta</span> <span class="hljs-comment"># VAE latents + text embeddings</span>
│ ├── &lt;hash2&gt;<span class="hljs-string">.meta</span>
│ └── <span class="hljs-string">...</span>
├── 832x480/ <span class="hljs-comment"># Another resolution bucket</span>
│ └── <span class="hljs-string">...</span>
├── metadata.json <span class="hljs-comment"># Global config (processor, model, total items)</span>
└── metadata_shard_0000.json <span class="hljs-comment"># Per-sample metadata (paths, resolutions, captions)</span><!----></pre></div><!----> <blockquote class="tip"><p>Lihat panduan <a href="https://docs.nvidia.com/nemo/automodel/latest/datasets/diffusion-dataset" rel="nofollow">Persiapan Dataset Difusi</a> untuk format caption, persyaratan data input, dan semua argumen pra-pemrosesan yang tersedia.</p></blockquote> <!--[1--><h2 class="relative group"><a id="training-configuration" 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-configuration"><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><!---->Konfigurasi pelatihan<!----></span></h2><!--]--><!----> <p>Fine-tuning didorong oleh dua komponen:</p> <ol><li>Skrip resep (<a href="https://github.com/NVIDIA-NeMo/Automodel/blob/main/examples/diffusion/finetune/finetune.py" rel="nofollow">finetune.py</a>) adalah titik masuk Python yang berisi loop pelatihan: memuat model, membangun dataloader, menjalankan langkah maju/mundur, menghitung kerugian flow matching, membuat checkpoint, dan mencatat log.</li> <li>File konfigurasi YAML menentukan semua pengaturan yang digunakan oleh resep: model mana yang akan dihaluskan, di mana data berada, hiperparameter optimizer, strategi paralelisme, dan lainnya. Anda menyesuaikan pelatihan dengan mengedit file ini, bukan dengan memodifikasi kode, sehingga memungkinkan Anda untuk berskala dari 1 hingga ratusan GPU.</li></ol> <p>Setiap field YAML juga dapat ditimpa dari CLI:</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> Disalin</div><!----></button><!----></div> <pre class="language-bash "><!---->torchrun --nproc-per-node=8 examples/diffusion/finetune/finetune.py \
-c examples/diffusion/finetune/wan2_1_t2v_flow.yaml \
--optim.learning_rate 1e-5 \
--step_scheduler.num_epochs 50<!----></pre></div><!----> <p>Di bawah ini adalah konfigurasi anotasi untuk fine-tuning Wan 2.1 T2V 1.3B, dengan setiap bagian dijelaskan.</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> Disalin</div><!----></button><!----></div> <pre class="language-yaml "><!----><span class="hljs-attr">seed:</span> <span class="hljs-number">42</span>
<span class="hljs-comment"># ── Experiment tracking (optional) ──────────────────────────────────────────</span>
<span class="hljs-comment"># Weights &amp; Biases integration for logging metrics, losses, and learning rates.</span>
<span class="hljs-comment"># Set mode: &quot;disabled&quot; to turn off.</span>
<span class="hljs-attr">wandb:</span>
<span class="hljs-attr">project:</span> <span class="hljs-string">wan-t2v-flow-matching</span>
<span class="hljs-attr">mode:</span> <span class="hljs-string">online</span>
<span class="hljs-attr">name:</span> <span class="hljs-string">wan2_1_t2v_fm</span>
<span class="hljs-comment"># ── Model ───────────────────────────────────────────────────────────────────</span>
<span class="hljs-comment"># pretrained_model_name_or_path: any Hugging Face model ID or local path.</span>
<span class="hljs-comment"># mode: &quot;finetune&quot; loads pretrained weights; &quot;pretrain&quot; trains from scratch.</span>
<span class="hljs-attr">model:</span>
<span class="hljs-attr">pretrained_model_name_or_path:</span> <span class="hljs-string">Wan-AI/Wan2.1-T2V-1.3B-Diffusers</span>
<span class="hljs-attr">mode:</span> <span class="hljs-string">finetune</span>
<span class="hljs-comment"># ── Training schedule ───────────────────────────────────────────────────────</span>
<span class="hljs-comment"># global_batch_size: effective batch across all GPUs.</span>
<span class="hljs-comment"># Gradient accumulation is computed automatically: global / (local × num_gpus).</span>
<span class="hljs-attr">step_scheduler:</span>
<span class="hljs-attr">global_batch_size:</span> <span class="hljs-number">8</span>
<span class="hljs-attr">local_batch_size:</span> <span class="hljs-number">1</span>
<span class="hljs-attr">ckpt_every_steps:</span> <span class="hljs-number">1000</span> <span class="hljs-comment"># Save a checkpoint every N steps</span>
<span class="hljs-attr">num_epochs:</span> <span class="hljs-number">100</span>
<span class="hljs-attr">log_every:</span> <span class="hljs-number">2</span> <span class="hljs-comment"># Log metrics every N steps</span>
<span class="hljs-comment"># ── Data ────────────────────────────────────────────────────────────────────</span>
<span class="hljs-comment"># _target_: the dataloader factory function.</span>
<span class="hljs-comment"># Use build_video_multiresolution_dataloader for video models (Wan, HunyuanVideo).</span>
<span class="hljs-comment"># Use build_text_to_image_multiresolution_dataloader for image models (FLUX).</span>
<span class="hljs-comment"># model_type: &quot;wan&quot; or &quot;hunyuan&quot; (selects the correct latent format).</span>
<span class="hljs-comment"># base_resolution: target resolution for multiresolution bucketing.</span>
<span class="hljs-attr">data:</span>
<span class="hljs-attr">dataloader:</span>
<span class="hljs-attr">_target_:</span> <span class="hljs-string">nemo_automodel.components.datasets.diffusion.build_video_multiresolution_dataloader</span>
<span class="hljs-attr">cache_dir:</span> <span class="hljs-string">PATH_TO_YOUR_DATA</span>
<span class="hljs-attr">model_type:</span> <span class="hljs-string">wan</span>
<span class="hljs-attr">base_resolution:</span> [<span class="hljs-number">512</span>, <span class="hljs-number">512</span>]
<span class="hljs-attr">dynamic_batch_size:</span> <span class="hljs-literal">false</span> <span class="hljs-comment"># When true, adjusts batch per bucket to maintain constant memory</span>
<span class="hljs-attr">shuffle:</span> <span class="hljs-literal">true</span>
<span class="hljs-attr">drop_last:</span> <span class="hljs-literal">false</span>
<span class="hljs-attr">num_workers:</span> <span class="hljs-number">0</span>
<span class="hljs-comment"># ── Optimizer ───────────────────────────────────────────────────────────────</span>
<span class="hljs-comment"># learning_rate: 5e-6 is a good starting point for fine-tuning.</span>
<span class="hljs-comment"># Adjust weight_decay and betas for your dataset.</span>
<span class="hljs-attr">optim:</span>
<span class="hljs-attr">learning_rate:</span> <span class="hljs-number">5e-6</span>
<span class="hljs-attr">optimizer:</span>
<span class="hljs-attr">weight_decay:</span> <span class="hljs-number">0.01</span>
<span class="hljs-attr">betas:</span> [<span class="hljs-number">0.9</span>, <span class="hljs-number">0.999</span>]
<span class="hljs-comment"># ── Learning rate scheduler ─────────────────────────────────────────────────</span>
<span class="hljs-comment"># Supports cosine, linear, and constant schedules.</span>
<span class="hljs-attr">lr_scheduler:</span>
<span class="hljs-attr">lr_decay_style:</span> <span class="hljs-string">cosine</span>
<span class="hljs-attr">lr_warmup_steps:</span> <span class="hljs-number">0</span>
<span class="hljs-attr">min_lr:</span> <span class="hljs-number">1e-6</span>
<span class="hljs-comment"># ── Flow matching ───────────────────────────────────────────────────────────</span>
<span class="hljs-comment"># adapter_type: model-specific adapter — must match the model:</span>
<span class="hljs-comment"># &quot;simple&quot; for Wan 2.1, &quot;flux&quot; for FLUX.1-dev, &quot;hunyuan&quot; for HunyuanVideo.</span>
<span class="hljs-comment"># timestep_sampling: &quot;uniform&quot; for Wan, &quot;logit_normal&quot; for FLUX and HunyuanVideo.</span>
<span class="hljs-comment"># flow_shift: shifts the flow schedule (model-dependent).</span>
<span class="hljs-comment"># i2v_prob: probability of image-to-video conditioning during training (video models).</span>
<span class="hljs-attr">flow_matching:</span>
<span class="hljs-attr">adapter_type:</span> <span class="hljs-string">&quot;simple&quot;</span>
<span class="hljs-attr">adapter_kwargs:</span> {}
<span class="hljs-attr">timestep_sampling:</span> <span class="hljs-string">&quot;uniform&quot;</span>
<span class="hljs-attr">logit_mean:</span> <span class="hljs-number">0.0</span>
<span class="hljs-attr">logit_std:</span> <span class="hljs-number">1.0</span>
<span class="hljs-attr">flow_shift:</span> <span class="hljs-number">3.0</span>
<span class="hljs-attr">num_train_timesteps:</span> <span class="hljs-number">1000</span>
<span class="hljs-attr">i2v_prob:</span> <span class="hljs-number">0.3</span>
<span class="hljs-attr">use_loss_weighting:</span> <span class="hljs-literal">true</span>
<span class="hljs-comment"># ── FSDP2 distributed training ──────────────────────────────────────────────</span>
<span class="hljs-comment"># dp_size: number of GPUs for data parallelism (typically = total GPUs on node).</span>
<span class="hljs-comment"># tp_size, cp_size, pp_size: tensor, context, and pipeline parallelism.</span>
<span class="hljs-comment"># For most fine-tuning, dp_size is all you need; leave others at 1.</span>
<span class="hljs-attr">fsdp:</span>
<span class="hljs-attr">tp_size:</span> <span class="hljs-number">1</span>
<span class="hljs-attr">cp_size:</span> <span class="hljs-number">1</span>
<span class="hljs-attr">pp_size:</span> <span class="hljs-number">1</span>
<span class="hljs-attr">dp_replicate_size:</span> <span class="hljs-number">1</span>
<span class="hljs-attr">dp_size:</span> <span class="hljs-number">8</span>
<span class="hljs-comment"># ── Checkpointing ──────────────────────────────────────────────────────────</span>
<span class="hljs-comment"># checkpoint_dir: where to save checkpoints (use a persistent path with Docker).</span>
<span class="hljs-comment"># restore_from: path to resume training from a previous checkpoint.</span>
<span class="hljs-attr">checkpoint:</span>
<span class="hljs-attr">enabled:</span> <span class="hljs-literal">true</span>
<span class="hljs-attr">checkpoint_dir:</span> <span class="hljs-string">PATH_TO_YOUR_CKPT_DIR</span>
<span class="hljs-attr">model_save_format:</span> <span class="hljs-string">torch_save</span>
<span class="hljs-attr">save_consolidated:</span> <span class="hljs-literal">false</span>
<span class="hljs-attr">restore_from:</span> <span class="hljs-literal">null</span><!----></pre></div><!----> <!--[2--><h3 class="relative group"><a id="config-field-reference" 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="#config-field-reference"><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><!---->Referensi bidang konfigurasi<!----></span></h3><!--]--><!----> <p>Tabel di bawah ini mencantumkan konfigurasi minimal yang diperlukan. Lihat <a href="https://github.com/NVIDIA-NeMo/Automodel/tree/main/examples/diffusion/finetune" rel="nofollow">contoh NeMo Automodel</a> untuk contoh konfigurasi lengkap untuk semua model.</p> <table><thead><tr><th>Bagian</th><th>Wajib?</th><th>Yang Perlu Diubah</th></tr></thead><tbody><tr><td><code>model</code></td><td>Ya</td><td>Set <code>pretrained_model_name_or_path</code> ke model ID Hugging Face. Set <code>mode: finetune</code> atau <code>mode: pretrain</code>.</td></tr><tr><td><code>step_scheduler</code></td><td>Ya</td><td><code>global_batch_size</code> adalah ukuran batch efektif di semua GPU. <code>ckpt_every_steps</code> mengontrol frekuensi checkpoint. Akumulasi gradien dihitung secara otomatis.</td></tr><tr><td><code>data</code></td><td>Ya</td><td>Set <code>cache_dir</code> ke path yang berisi file <code>.meta</code> yang sudah diproses sebelumnya. Ubah <code>_target_</code> dan <code>model_type</code> untuk model yang berbeda.</td></tr><tr><td><code>optim</code></td><td>Ya</td><td><code>learning_rate: 5e-6</code> adalah nilai default yang baik untuk fine-tuning. Sesuaikan dengan dataset dan model Anda.</td></tr><tr><td><code>lr_scheduler</code></td><td>Ya</td><td>Pilih <code>cosine</code>, <code>linear</code>, atau <code>constant</code> untuk <code>lr_decay_style</code>. Atur <code>lr_warmup_steps</code> untuk warmup bertahap.</td></tr><tr><td><code>flow_matching</code></td><td>Ya</td><td><code>adapter_type</code> harus sesuai dengan model (<code>simple</code> untuk Wan, <code>flux</code> untuk FLUX, <code>hunyuan</code> untuk HunyuanVideo). Lihat konfigurasi spesifik model untuk <code>adapter_kwargs</code>.</td></tr><tr><td><code>fsdp</code></td><td>Ya</td><td>Set <code>dp_size</code> ke jumlah GPU. Untuk multi-node, set ke total GPU di semua node.</td></tr><tr><td><code>checkpoint</code></td><td>Direkomendasikan</td><td>Setel <code>checkpoint_dir</code> ke path yang persisten, terutama di Docker. Gunakan <code>restore_from</code> untuk melanjutkan dari checkpoint sebelumnya.</td></tr><tr><td><code>wandb</code></td><td>Opsional</td><td>Konfigurasi untuk mengaktifkan pelacakan eksperimen Weights &amp; Biases. Atur <code>mode: disabled</code> untuk menonaktifkan.</td></tr></tbody></table> <!--[1--><h2 class="relative group"><a id="launch-training" 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-training"><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><!---->Mulai pelatihan<!----></span></h2><!--]--><!----> <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">satu-node</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">multi-node</div><!--]--></div> <div class="language-select"><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> Disalin</div><!----></button><!----></div> <pre class="language-bash "><!---->torchrun --nproc-per-node=8 \
examples/diffusion/finetune/finetune.py \
-c examples/diffusion/finetune/wan2_1_t2v_flow.yaml<!----></pre></div><!----></div><!----> <div hidden=""><p>Jalankan berikut di setiap node, dengan mengatur <code>NODE_RANK</code> sesuai:</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> Disalin</div><!----></button><!----></div> <pre class="language-bash "><!----><span class="hljs-built_in">export</span> MASTER_ADDR=node0.hostname
<span class="hljs-built_in">export</span> MASTER_PORT=29500
<span class="hljs-built_in">export</span> NODE_RANK=0 <span class="hljs-comment"># 0 on master, 1 on second node, etc.</span>
torchrun \
--nnodes=2 \
--nproc-per-node=8 \
--node_rank=<span class="hljs-variable">${NODE_RANK}</span> \
--rdzv_backend=c10d \
--rdzv_endpoint=<span class="hljs-variable">${MASTER_ADDR}</span>:<span class="hljs-variable">${MASTER_PORT}</span> \
examples/diffusion/finetune/finetune.py \
-c examples/diffusion/finetune/wan2_1_t2v_flow_multinode.yaml<!----></pre></div><!----> <blockquote class="note"><p>Untuk pelatihan multi-node, atur <code>fsdp.dp_size</code> di YAML ke jumlah <strong>total</strong> GPU di semua node (misalnya, 16 untuk 2 node dengan 8 GPU masing-masing).</p></blockquote><!----></div><!----><!----></div><!----> <!--[1--><h2 class="relative group"><a id="generation" 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="#generation"><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><!---->Generasi<!----></span></h2><!--]--><!----> <p>Setelah pelatihan, generate video atau gambar dari prompt teks menggunakan checkpoint yang telah dihaluskan.</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">Wan 2.1</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">FLUX</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">HunyuanVideo</div><!--]--></div> <div class="language-select"><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> Disalin</div><!----></button><!----></div> <pre class="language-bash "><!---->python examples/diffusion/generate/generate.py \
-c examples/diffusion/generate/configs/generate_wan.yaml<!----></pre></div><!----> <p>Dengan checkpoint yang telah dihaluskan:</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> Disalin</div><!----></button><!----></div> <pre class="language-bash "><!---->python examples/diffusion/generate/generate.py \
-c examples/diffusion/generate/configs/generate_wan.yaml \
--model.checkpoint ./checkpoints/step_1000 \
--inference.prompts <span class="hljs-string">&#x27;[&quot;A dog running on a beach&quot;]&#x27;</span><!----></pre></div><!----><!----></div><!----> <div hidden=""><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> Disalin</div><!----></button><!----></div> <pre class="language-bash "><!---->python examples/diffusion/generate/generate.py \
-c examples/diffusion/generate/configs/generate_flux.yaml<!----></pre></div><!----> <p>Dengan checkpoint yang telah dihaluskan:</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> Disalin</div><!----></button><!----></div> <pre class="language-bash "><!---->python examples/diffusion/generate/generate.py \
-c examples/diffusion/generate/configs/generate_flux.yaml \
--model.checkpoint ./checkpoints/step_1000 \
--inference.prompts <span class="hljs-string">&#x27;[&quot;A dog running on a beach&quot;]&#x27;</span><!----></pre></div><!----><!----></div><!----> <div hidden=""><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> Disalin</div><!----></button><!----></div> <pre class="language-bash "><!---->python examples/diffusion/generate/generate.py \
-c examples/diffusion/generate/configs/generate_hunyuan.yaml<!----></pre></div><!----> <p>Dengan checkpoint yang telah dihaluskan:</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> Disalin</div><!----></button><!----></div> <pre class="language-bash "><!---->python examples/diffusion/generate/generate.py \
-c examples/diffusion/generate/configs/generate_hunyuan.yaml \
--model.checkpoint ./checkpoints/step_1000 \
--inference.prompts <span class="hljs-string">&#x27;[&quot;A dog running on a beach&quot;]&#x27;</span><!----></pre></div><!----><!----></div><!----><!----></div><!----> <!--[1--><h2 class="relative group"><a id="diffusers-integration" 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="#diffusers-integration"><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><!---->Integrasi Diffusers<!----></span></h2><!--]--><!----> <p>NeMo Automodel dibangun di atas Diffusers dan menggunakannya sebagai tulang punggung untuk pemuatan model dan inferensi. Model dimuat langsung dari Hugging Face Hub menggunakan kelas model Diffusers seperti <a href="/docs/diffusers/main/id/api/models/wan_transformer_3d#diffusers.WanTransformer3DModel">WanTransformer3DModel</a>, <a href="/docs/diffusers/main/id/api/models/flux_transformer#diffusers.FluxTransformer2DModel">FluxTransformer2DModel</a>, dan <a href="/docs/diffusers/main/id/api/models/hunyuan_video_transformer_3d#diffusers.HunyuanVideoTransformer3DModel">HunyuanVideoTransformer3DModel</a>, serta menghasilkan output melalui pipeline Diffusers seperti <a href="/docs/diffusers/main/id/api/pipelines/wan#diffusers.WanPipeline">WanPipeline</a> dan <a href="/docs/diffusers/main/id/api/pipelines/flux#diffusers.FluxPipeline">FluxPipeline</a>.</p> <p>Integrasi ini memberikan beberapa manfaat bagi pengguna Diffusers:</p> <ul><li><strong>Tidak ada konversi checkpoint</strong>: bobot pretrained dari Hub dapat langsung digunakan. Arahkan <code>pretrained_model_name_or_path</code> ke ID model berformat Diffusers mana pun dan mulai pelatihan segera.</li> <li><strong>Dukungan model sejak hari pertama</strong>: ketika model difusi baru ditambahkan ke Diffusers dan diunggah ke Hub, model tersebut dapat dihaluskan (fine-tuned) dengan NeMo Automodel tanpa perlu menunggu skrip pelatihan khusus.</li> <li><strong>Output yang kompatibel dengan Pipeline</strong>: checkpoint hasil fine-tuning disimpan dalam format yang dapat dimuat kembali secara langsung ke dalam pipeline Diffusers untuk inferensi, berbagi di Hub, atau optimasi lebih lanjut dengan alat seperti kuantisasi dan kompilasi.</li> <li><strong>Pelatihan yang dapat diskalakan untuk model Diffusers</strong>: NeMo Automodel menambahkan kemampuan pelatihan terdistribusi (FSDP2, multi-node, bucketing multiresolution) yang melampaui apa yang disediakan oleh skrip pelatihan bawaan Diffusers, sambil tetap mempertahankan antarmuka model dan pipeline yang sama.</li> <li><strong>Ekosistem bersama</strong>: model, adapter LoRA, atau komponen pipeline mana pun dari ekosistem Diffusers tetap kompatibel di seluruh alur kerja pelatihan dan inferensi.</li></ul> <!--[1--><h2 class="relative group"><a id="nvidia-team" 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="#nvidia-team"><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><!---->Tim NVIDIA<!----></span></h2><!--]--><!----> <ul><li>Pranav Prashant Thombre, <a href="mailto:pthombre@nvidia.com">pthombre@nvidia.com</a></li> <li>Linnan Wang, <a href="mailto:linnanw@nvidia.com">linnanw@nvidia.com</a></li> <li>Alexandros Koumparoulis, <a href="mailto:akoumparouli@nvidia.com">akoumparouli@nvidia.com</a></li></ul> <!--[1--><h2 class="relative group"><a id="resources" 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="#resources"><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><!---->Sumber daya<!----></span></h2><!--]--><!----> <ul><li><a href="https://github.com/NVIDIA-NeMo/Automodel" rel="nofollow">NeMo Automodel GitHub</a></li> <li><a href="https://docs.nvidia.com/nemo/automodel/latest/recipes-e2e-examples/diffusion-fine-tuning" rel="nofollow">Panduan Fine-Tuning Diffusion</a></li> <li><a href="https://docs.nvidia.com/nemo/automodel/latest/datasets/diffusion-dataset" rel="nofollow">Persiapan Dataset Difusi</a></li> <li><a href="https://docs.nvidia.com/nemo/automodel/latest/model-coverage/diffusion.html" rel="nofollow">Cakupan Model Difusi</a></li> <li><a href="https://huggingface.co/docs/transformers/en/community_integrations/nemo_automodel_finetuning" rel="nofollow">NeMo Automodel untuk Transformers (fine-tuning LLM/VLM)</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/nemo_automodel.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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