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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="nvidia-modelopt" 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-modelopt"><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><!---->NVIDIA ModelOpt<!----></span></h1><!--]--><!----> <p><a href="https://github.com/NVIDIA/Model-Optimizer" rel="nofollow">NVIDIA-ModelOpt</a>は、量子化、プルーニング、蒸留、投機的デコーディングなどの最先端のモデル最適化技術を含む統合ライブラリです。TensorRT-LLMやTensorRTなどの下流デプロイメントフレームワーク向けに深層学習モデルを圧縮し、推論速度の最適化を実現します。</p> <p>開始する前に、nvidia_modeloptがインストールされていることを確認してください。</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> コピーしました</div><!----></button><!----></div> <pre class="language-bash "><!---->pip install -U <span class="hljs-string">&quot;nvidia_modelopt[hf]&quot;</span><!----></pre></div><!----> <p><code>NVIDIAModelOptConfig</code> を <a href="/docs/diffusers/v0.43.0/ja/api/models/overview#diffusers.ModelMixin.from_pretrained">from_pretrained()</a> に渡すことでモデルを量子化できます（事前量子化済みモデルの読み込みも可能です）。<a href="https://hf.co/docs/accelerate/index" rel="nofollow">Accelerate</a> による読み込みをサポートし、<code>torch.nn.Linear</code> レイヤーを含むモデルであれば、モダリティを問わず動作します。</p> <p>以下の例では、重みをFP8に量子化します。</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> コピーしました</div><!----></button><!----></div> <pre class="language-python "><!----><span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoModel, SanaPipeline, NVIDIAModelOptConfig

model_id = <span class="hljs-string">&quot;Efficient-Large-Model/Sana_600M_1024px_diffusers&quot;</span>
dtype = torch.bfloat16

quantization_config = NVIDIAModelOptConfig(quant_type=<span class="hljs-string">&quot;FP8&quot;</span>, quant_method=<span class="hljs-string">&quot;modelopt&quot;</span>)
transformer = AutoModel.from_pretrained(
    model_id,
    subfolder=<span class="hljs-string">&quot;transformer&quot;</span>,
    quantization_config=quantization_config,
    dtype=dtype,
)
pipe = SanaPipeline.from_pretrained(
    model_id,
    transformer=transformer,
    dtype=dtype,
)
pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)  <span class="hljs-comment"># or &quot;mps&quot;, &quot;xpu&quot;, &quot;cpu&quot;</span>

<span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Pipeline memory usage: <span class="hljs-subst">{torch.cuda.max_memory_reserved() / <span class="hljs-number">1024</span>**<span class="hljs-number">3</span>:<span class="hljs-number">.3</span>f}</span> GB&quot;</span>)

prompt = <span class="hljs-string">&quot;A cat holding a sign that says hello world&quot;</span>
image = pipe(
    prompt, num_inference_steps=<span class="hljs-number">50</span>, guidance_scale=<span class="hljs-number">4.5</span>, max_sequence_length=<span class="hljs-number">512</span>
).images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">&quot;output.png&quot;</span>)<!----></pre></div><!----> <blockquote><p><strong>注:</strong></p> <p>NVIDIA-ModelOpt の量子化手法は、モデルの性能を維持しつつ、さまざまな QAT（Quantization-Aware Training）および PTQ（Post-Training Quantization）技術を用いてモデル重みのメモリフットプリントを削減するように設計されています。ただし、推論時の実際の性能向上は、デプロイメントフレームワーク（例：TRT-LLM、TensorRT）や具体的なハードウェア構成に依存します。</p> <p>詳細については、<a href="https://github.com/NVIDIA/Model-Optimizer/tree/main/examples" rel="nofollow">こちら</a>を参照してください。</p></blockquote> <!--[1--><h2 class="relative group"><a id="nvidiamodeloptconfig" 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="#nvidiamodeloptconfig"><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><!---->NVIDIAModelOptConfig<!----></span></h2><!--]--><!----> <p><code>NVIDIAModelOptConfig</code> クラスは、3つのパラメータを受け取ります。</p> <ul><li><code>quant_type</code>: 以下の量子化タイプのいずれかを指定する文字列値。</li> <li><code>modules_to_not_convert</code>: 量子化を実行しないモジュールの完全名または部分名のリストです。例えば、<a href="/docs/diffusers/v0.43.0/ja/api/models/sd3_transformer2d#diffusers.SD3Transformer2DModel">SD3Transformer2DModel</a>のpos_embedプロジェクションブロックに対して量子化を一切行わない場合は、<code>modules_to_not_convert=["pos_embed.proj.weight"]</code>と指定します。</li> <li><code>disable_conv_quantization</code>: 真偽値。これを<code>True</code>に設定すると、モデル内のすべての畳み込み層の量子化が無効になります。これは、チャネル量子化やブロック量子化が一般的に畳み込み層（INT4、NF4、NVFP4で使用）と相性が良くないため、有用です。特定の畳み込み層の量子化のみを無効にしたい場合は、代わりに<code>modules_to_not_convert</code>を使用してください。</li> <li><code>algorithm</code>: スケールを決定するために使用するアルゴリズムです。デフォルト値は<code>"max"</code>です。その他のアルゴリズムや詳細については、modeloptのドキュメントを参照してください。</li> <li><code>forward_loop</code>: 量子化時のアクティベーションのキャリブレーションに使用するフォワードループ関数。指定しない場合、重みのみを使用して計算された静的なスケール値に依存します。</li> <li><code>kwargs</code>: <code>quant_type</code>に基づいて呼び出される、基盤となる量子化メソッドに渡すキーワード引数の辞書。</li></ul> <!--[1--><h2 class="relative group"><a id="supported-quantization-types" 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-quantization-types"><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><!---->サポートされている量子化タイプ<!----></span></h2><!--]--><!----> <p>ModelOptは、weight-only、channel、blockの量子化に対応しており、int8、fp8、int4、nf4、nvfp4をサポートしています。これらの量子化手法は、推論時のモデルの性能を維持しながら、モデルの重みのメモリフットプリントを削減することを目的に設計されています。</p> <p>Weight-only quantization は、モデルの重みを特定の低ビットのデータ型で保存し、<code>bfloat16</code> などの高精度なデータ型で計算を実行します。これにより、モデルの重みによるメモリ要件は低減されますが、アクティベーションの計算に伴うメモリピークは維持されます。</p> <p>サポートされている量子化手法は以下の通りです:</p> <table><thead><tr><th><strong>量子化タイプ</strong></th><th><strong>サポートされているスキーム</strong></th><th><strong>必須のKwargs</strong></th><th><strong>補足</strong></th></tr></thead><tbody><tr><td><strong>INT8</strong></td><td><code>int8 weight only</code>, <code>int8 channel quantization</code>, <code>int8 block quantization</code></td><td><code>quant_type</code>, <code>quant_type + channel_quantize</code>, <code>quant_type + channel_quantize + block_quantize</code></td><td></td></tr><tr><td><strong>FP8</strong></td><td><code>fp8 weight only</code>, <code>fp8 channel quantization</code>, <code>fp8 block quantization</code></td><td><code>quant_type</code>, <code>quant_type + channel_quantize</code>, <code>quant_type + channel_quantize + block_quantize</code></td><td></td></tr><tr><td><strong>INT4</strong></td><td><code>int4 weight only</code>, <code>int4 block quantization</code></td><td><code>quant_type</code>, <code>quant_type + channel_quantize + block_quantize</code></td><td><code>channel_quantize = -1 is only supported for now</code></td></tr><tr><td><strong>NF4</strong></td><td><code>nf4 weight only</code>, <code>nf4 double block quantization</code></td><td><code>quant_type</code>, <code>quant_type + channel_quantize + block_quantize + scale_channel_quantize</code> + <code>scale_block_quantize</code></td><td><code>channel_quantize = -1 and scale_channel_quantize = -1 are only supported for now</code></td></tr><tr><td><strong>NVFP4</strong></td><td><code>nvfp4 weight only</code>, <code>nvfp4 block quantization</code></td><td><code>quant_type</code>, <code>quant_type + channel_quantize + block_quantize</code></td><td><code>channel_quantize = -1 is only supported for now</code></td></tr></tbody></table> <p>利用可能な量子化手法と、利用可能な設定オプションの包括的なリストについて詳しく理解するには、<a href="https://nvidia.github.io/Model-Optimizer/" rel="nofollow">公式の modelopt ドキュメント</a>を参照してください。</p> <!--[1--><h2 class="relative group"><a id="serializing-and-deserializing-quantized-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="#serializing-and-deserializing-quantized-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><!---->量子化モデルのシリアライズとデシリアライズ<!----></span></h2><!--]--><!----> <p>指定された dtype で量子化モデルをシリアライズするには、まず希望する量子化 dtype でモデルを読み込み、<a href="/docs/diffusers/v0.43.0/ja/api/models/overview#diffusers.ModelMixin.save_pretrained">save_pretrained()</a> メソッドを使用して保存します。</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> コピーしました</div><!----></button><!----></div> <pre class="language-python "><!----><span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoModel, NVIDIAModelOptConfig
<span class="hljs-keyword">from</span> modelopt.torch.opt <span class="hljs-keyword">import</span> enable_huggingface_checkpointing

enable_huggingface_checkpointing()

model_id = <span class="hljs-string">&quot;Efficient-Large-Model/Sana_600M_1024px_diffusers&quot;</span>
quant_config_fp8 = {<span class="hljs-string">&quot;quant_type&quot;</span>: <span class="hljs-string">&quot;FP8&quot;</span>, <span class="hljs-string">&quot;quant_method&quot;</span>: <span class="hljs-string">&quot;modelopt&quot;</span>}
quant_config_fp8 = NVIDIAModelOptConfig(**quant_config_fp8)
model = AutoModel.from_pretrained(
    model_id,
    subfolder=<span class="hljs-string">&quot;transformer&quot;</span>,
    quantization_config=quant_config_fp8,
    dtype=torch.bfloat16,
)
model.save_pretrained(<span class="hljs-string">&#x27;path/to/sana_fp8&#x27;</span>, safe_serialization=<span class="hljs-literal">False</span>)<!----></pre></div><!----> <p>シリアライズされた量子化モデルを読み込むには、<a href="/docs/diffusers/v0.43.0/ja/api/models/overview#diffusers.ModelMixin.from_pretrained">from_pretrained()</a>メソッドを使用します。</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> コピーしました</div><!----></button><!----></div> <pre class="language-python "><!----><span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> AutoModel, NVIDIAModelOptConfig, SanaPipeline
<span class="hljs-keyword">from</span> modelopt.torch.opt <span class="hljs-keyword">import</span> enable_huggingface_checkpointing

enable_huggingface_checkpointing()

quantization_config = NVIDIAModelOptConfig(quant_type=<span class="hljs-string">&quot;FP8&quot;</span>, quant_method=<span class="hljs-string">&quot;modelopt&quot;</span>)
transformer = AutoModel.from_pretrained(
    <span class="hljs-string">&quot;path/to/sana_fp8&quot;</span>,
    subfolder=<span class="hljs-string">&quot;transformer&quot;</span>,
    quantization_config=quantization_config,
    dtype=torch.bfloat16,
)
pipe = SanaPipeline.from_pretrained(
    <span class="hljs-string">&quot;Efficient-Large-Model/Sana_600M_1024px_diffusers&quot;</span>,
    transformer=transformer,
    dtype=torch.bfloat16,
)
pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)  <span class="hljs-comment"># or &quot;mps&quot;, &quot;xpu&quot;, &quot;cpu&quot;</span>
prompt = <span class="hljs-string">&quot;A cat holding a sign that says hello world&quot;</span>
image = pipe(
    prompt, num_inference_steps=<span class="hljs-number">50</span>, guidance_scale=<span class="hljs-number">4.5</span>, max_sequence_length=<span class="hljs-number">512</span>
).images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">&quot;output.png&quot;</span>)<!----></pre></div><!----> <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/quantization/modelopt.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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