diffusers/pr_13881/zh/_app/immutable/nodes/28.EgeO8bwW.js

import"../chunks/DsnmJJEf.js";import{i as y,h as d,C as g,H as T,a,E as h,s as f}from"../chunks/DdZvggmf.js";import{p as V,o as C,s as l,f as Q,a as M,b as w,c as U,n as N}from"../chunks/BbekZcyp.js";const R='{"title":"AWS Neuron","local":"aws-neuron","sections":[],"depth":1}';var b=U('<meta name="hf:doc:metadata"/>'),j=U('<p></p> <!> <!> <p>Diffusers 功能可在 <a href="https://aws.amazon.com/ec2/instance-types/inf2/" rel="nofollow">AWS Inf2 实例</a>上使用，这些是由 <a href="https://aws.amazon.com/machine-learning/inferentia/" rel="nofollow">Neuron 机器学习加速器</a>驱动的 EC2 实例。这些实例旨在提供更好的计算性能（更高的吞吐量、更低的延迟）和良好的成本效益，使其成为 AWS 用户将扩散模型部署到生产环境的良好选择。</p> <p><a href="https://huggingface.co/docs/optimum-neuron/en/index" rel="nofollow">Optimum Neuron</a> 是 Hugging Face 库与 AWS 加速器之间的接口，包括 AWS <a href="https://aws.amazon.com/machine-learning/trainium/" rel="nofollow">Trainium</a> 和 AWS <a href="https://aws.amazon.com/machine-learning/inferentia/" rel="nofollow">Inferentia</a>。它支持 Diffusers 中的许多功能，并具有类似的 API，因此如果您已经熟悉 Diffusers，学习起来更容易。一旦您创建了 AWS Inf2 实例，请安装 Optimum Neuron。</p> <!> <blockquote class="tip"><p>我们提供预构建的 <a href="https://aws.amazon.com/marketplace/pp/prodview-gr3e6yiscria2" rel="nofollow">Hugging Face Neuron 深度学习 AMI</a>（DLAMI）和用于 Amazon SageMaker 的 Optimum Neuron 容器。建议正确设置您的环境。</p></blockquote> <p>下面的示例演示了如何在 inf2.8xlarge 实例上使用 Stable Diffusion XL 模型生成图像（一旦模型编译完成，您可以切换到更便宜的 inf2.xlarge 实例）。要生成一些图像，请使用 <code>NeuronStableDiffusionXLPipeline</code> 类，该类类似于 Diffusers 中的 <code>StableDiffusionXLPipeline</code> 类。</p> <p>与 Diffusers 不同，您需要将管道中的模型编译为 Neuron 格式，即 <code>.neuron</code>。运行以下命令将模型导出为 <code>.neuron</code> 格式。</p> <!> <p>现在使用预编译的 SDXL 模型生成一些图像。</p> <!> <img src="https://huggingface.co/datasets/Jingya/document_images/resolve/main/optimum/neuron/sdxl_pig.png" width="256" height="256" alt="peggy generated by sdxl on inf2"/> <p>欢迎查看Optimum Neuron <a href="https://huggingface.co/docs/optimum-neuron/en/inference_tutorials/stable_diffusion#generate-images-with-stable-diffusion-models-on-aws-inferentia" rel="nofollow">文档</a>中更多不同用例的指南和示例！</p> <!> <p></p>',1);function I(c,m){V(m,!1),C(()=>{new URLSearchParams(window.location.search).get("fw")}),y();var e=j();d("11xqcdu",r=>{var p=b();f(p,"content",R),M(r,p)});var n=l(Q(e),2);g(n,{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"});var i=l(n,2);T(i,{title:"AWS Neuron",local:"aws-neuron",headingTag:"h1"});var s=l(i,6);a(s,{code:"cHl0aG9uJTIwLW0lMjBwaXAlMjBpbnN0YWxsJTIwLS11cGdyYWRlLXN0cmF0ZWd5JTIwZWFnZXIlMjBvcHRpbXVtJTVCbmV1cm9ueCU1RA==",highlighted:"python -m pip install --upgrade-strategy eager optimum[neuronx]",lang:"bash",wrap:!1});var t=l(s,8);a(t,{code:"b3B0aW11bS1jbGklMjBleHBvcnQlMjBuZXVyb24lMjAtLW1vZGVsJTIwc3RhYmlsaXR5YWklMkZzdGFibGUtZGlmZnVzaW9uLXhsLWJhc2UtMS4wJTIwJTVDJTBBJTIwJTIwLS1iYXRjaF9zaXplJTIwMSUyMCU1QyUwQSUyMCUyMC0taGVpZ2h0JTIwMTAyNCUyMCU2MCUyMyUyMCVFNyU5NCU5RiVFNiU4OCU5MCVFNSU5QiVCRSVFNSU4MyU4RiVFNyU5QSU4NCVFOSVBQiU5OCVFNSVCQSVBNiVFRiVCQyU4OCVFNSU4MyU4RiVFNyVCNCVBMCVFRiVCQyU4OSVFRiVCQyU4QyVFNCVCRSU4QiVFNSVBNiU4MiUyMDc2OCUyQyUyMDEwMjQlNjAlMjAlNUMlMEElMjAlMjAtLXdpZHRoJTIwMTAyNCUyMCU2MCUyMyUyMCVFNyU5NCU5RiVFNiU4OCU5MCVFNSU5QiVCRSVFNSU4MyU4RiVFNyU5QSU4NCVFNSVBRSVCRCVFNSVCQSVBNiVFRiVCQyU4OCVFNSU4MyU4RiVFNyVCNCVBMCVFRiVCQyU4OSVFRiVCQyU4QyVFNCVCRSU4QiVFNSVBNiU4MiUyMDc2OCUyQyUyMDEwMjQlNjAlMjAlNUMlMEElMjAlMjAtLW51bV9pbWFnZXNfcGVyX3Byb21wdCUyMDElMjAlNjAlMjMlMjAlRTYlQUYlOEYlRTQlQjglQUElRTYlOEYlOTAlRTclQTQlQkElRTclOTQlOUYlRTYlODglOTAlRTclOUElODQlRTUlOUIlQkUlRTUlODMlOEYlRTYlOTUlQjAlRTklODclOEYlRUYlQkMlOEMlRTklQkIlOTglRTglQUUlQTQlRTQlQjglQkElMjAxJTYwJTIwJTVDJTBBJTIwJTIwLS1hdXRvX2Nhc3QlMjBtYXRtdWwlMjAlNjAlMjMlMjAlRTQlQkIlODUlRTglQkQlQUMlRTYlOEQlQTIlRTclOUYlQTklRTklOTglQjUlRTQlQjklOTglRTYlQjMlOTUlRTYlOTMlOEQlRTQlQkQlOUMlNjAlMjAlNUMlMEElMjAlMjAtLWF1dG9fY2FzdF90eXBlJTIwYmYxNiUyMCU2MCUyMyUyMCVFNSVCMCU4NiVFNiU5MyU4RCVFNCVCRCU5QyVFNCVCQiU4RSUyMEZQMzIlMjAlRTglQkQlQUMlRTYlOEQlQTIlRTQlQjglQkElMjBCRjE2JTYwJTIwJTVDJTBBJTIwJTIwc2RfbmV1cm9uX3hsJTJG",highlighted:'optimum-cli <span class="hljs-built_in">export</span> neuron --model stabilityai/stable-diffusion-xl-base-1.0 \\\n  --batch_size 1 \\\n  --height 1024 `# 生成图像的高度（像素），例如 768, 1024` \\\n  --width 1024 `# 生成图像的宽度（像素），例如 768, 1024` \\\n  --num_images_per_prompt 1 `# 每个提示生成的图像数量，默认为 1` \\\n  --auto_cast matmul `# 仅转换矩阵乘法操作` \\\n  --auto_cast_type bf16 `# 将操作从 FP32 转换为 BF16` \\\n  sd_neuron_xl/',lang:"bash",wrap:!1});var o=l(t,4);a(o,{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.neuron <span class="hljs-keyword">import</span> Neu
ronStableDiffusionXLPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>stable_diffusion_xl = NeuronStableDiffusionXLPipeline.from_pretrained(<span class="hljs-string">&quot;sd_neuron_xl/&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;a pig with wings flying in floating US dollar banknotes in the air, skyscrapers behind, warm color palette, muted colors, detailed, 8k&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = stable_diffusion_xl(prompt).images[<span class="hljs-number">0</span>]`,lang:"python",wrap:!1});var u=l(o,6);h(u,{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/zh/optimization/neuron.md"}),N(2),M(c,e),w()}export{I as component};