optimum-neuron/pr_1109/en/_app/immutable/nodes/24.a6786d3c.js

import{s as Xl,n as Vl,o as _l}from"../chunks/scheduler.56725da7.js";import{S as Nl,i as Sl,e as i,s as a,c as p,h as Yl,a as o,d as e,b as n,f as Bl,g as m,j as u,k as vl,l as Rl,m as t,n as M,t as y,o as c,p as r}from"../chunks/index.18a26576.js";import{C as xl}from"../chunks/CopyLLMTxtMenu.a1f2bcd7.js";import{C as U}from"../chunks/CodeBlock.d6d1e300.js";import{H as jl}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.9f98faf7.js";function Ql(wl){let h,A,k,E,d,q,j,F,w,fl="<code>🤗 Optimum</code> extends <code>🤗 Diffusers</code> to support inference on the second generation of Neuron devices(powering Trainium and Inferentia 2). It aims at inheriting the ease of Diffusers on Neuron.",z,f,Jl='To get started, make sure you have <a href="https://huggingface.co/docs/optimum-neuron/installation" rel="nofollow">configured your inf2 / trn1 instance</a>, and installed optimum:',L,J,D,b,P,T,bl="To deploy models, you will need to compile them to TorchScript optimized for AWS Neuron. In the case of Stable Diffusion, there are four components which need to be exported to the <code>.neuron</code> format to boost the performance:",K,g,Tl="<li>Text encoder</li> <li>U-Net</li> <li>VAE encoder</li> <li>VAE decoder</li>",O,I,gl=`You can either compile and export a Stable Diffusion Checkpoint via CLI or <code>NeuronStableDiffusionPipeline</code> class.
In this tutorial, we will export <a href="https://huggingface.co/stabilityai/stable-diffusion-2-1" rel="nofollow"><code>stabilityai/stable-diffusion-2-1</code></a> with the API.`,ll,C,sl,Z,el,G,tl,W,Il="Feel free to use the following command as well:",al,$,nl,B,Cl="We Recommend <code>inf2.8xlarge</code> or larger for compilation. You will also be able to compile the models with a CPU-only instance <em>(needs ~35GB memory)</em> using the CLI with <code>--disable-validation</code>, which disables the validation of inference on neuron devices.",il,v,Zl="In the following section, we will run the pre-compiled model on Neuron devices, to reduce expenses, you can run inference with <code>inf2.xlarge</code> instance.",pl,X,ol,V,Gl="If you have pre-compiled Stable Diffusion models, you can load them directly to skip the compilation:",ml,_,Ml,N,Wl="Now generate images with your prompts on Neuron devices:",yl,S,cl,Y,rl,R,ul,x,$l=`[Inference Time] 6.09 seconds.
`,Ul,Q,hl,H,dl;return d=new xl({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),j=new jl({props:{title:"Stable Diffusion with Neuronx: Text to image",local:"stable-diffusion-with-neuronx-text-to-image",headingTag:"h1"}}),J=new U({props:{code:"IXBpcCUyMGluc3RhbGwlMjAlMjJvcHRpbXVtLW5ldXJvbiU1Qm5ldXJvbnglNUQlMjIlMjBkaWZmdXNlcnMlMjBtYXRwbG90bGli",highlighted:'!pip install <span class="hljs-string">&quot;optimum-neuron[neuronx]&quot;</span> diffusers matplotlib',lang:"python",wrap:!1}}),b=new jl({props:{title:"Compilation",local:"compilation",headingTag:"h2"}}),C=new U({props:{code:"ZnJvbSUyMG9wdGltdW0ubmV1cm9uJTIwaW1wb3J0JTIwTmV1cm9uU3RhYmxlRGlmZnVzaW9uUGlwZWxpbmUlMEElMEElMEFtb2RlbF9pZCUyMCUzRCUyMCUyMnN0YWJpbGl0eWFpJTJGc3RhYmxlLWRpZmZ1c2lvbi0yLTElMjIlMEFudW1faW1hZ2VfcGVyX3Byb21wdCUyMCUzRCUyMDElMEFpbnB1dF9zaGFwZXMlMjAlM0QlMjAlN0IlMjJiYXRjaF9zaXplJTIyJTNBJTIwMSUyQyUyMCUyMmhlaWdodCUyMiUzQSUyMDc2OCUyQyUyMCUyMndpZHRoJTIyJTNBJTIwNzY4JTJDJTIwJTIybnVtX2ltYWdlX3Blcl9wcm9tcHQlMjIlM0ElMjBudW1faW1hZ2VfcGVyX3Byb21wdCU3RCUwQWNvbXBpbGVyX2FyZ3MlMjAlM0QlMjAlN0IlMjJhdXRvX2Nhc3QlMjIlM0ElMjAlMjJtYXRtdWwlMjIlMkMlMjAlMjJhdXRvX2Nhc3RfdHlwZSUyMiUzQSUyMCUyMmJmMTYlMjIlN0Q=",highlighted:`<span class="hljs-keyword">from</span> optimum.neuron <span class="hljs-keyword">import</span> NeuronStableDiffusionPipeline


model_id = <span class="hljs-string">&quot;stabilityai/stable-diffusion-2-1&quot;</span>
num_image_per_prompt = <span class="hljs-number">1</span>
input_shapes = {<span class="hljs-string">&quot;batch_size&quot;</span>: <span class="hljs-number">1</span>, <span class="hljs-string">&quot;height&quot;</span>: <span class="hljs-number">768</span>, <span class="hljs-string">&quot;width&quot;</span>: <span class="hljs-number">768</span>, <span class="hljs-string">&quot;num_image_per_prompt&quot;</span>: num_image_per_prompt}
compiler_args = {<span class="hljs-string">&quot;auto_cast&quot;</span>: <span class="hljs-string">&quot;matmul&quot;</span>, <span class="hljs-string">&quot;auto_cast_type&quot;</span>: <span class="hljs-string">&quot;bf16&quot;</span>}`,lang:"python",wrap:!1}}),Z=new U({props:{code:"JTIzJTIwQ29tcGlsZSUyMGFuZCUyMHNhdmUlMEFzdGFibGVfZGlmZnVzaW9uJTIwJTNEJTIwTmV1cm9uU3RhYmxlRGlmZnVzaW9uUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMG1vZGVsX2lkJTJDJTIwZXhwb3J0JTNEVHJ1ZSUyQyUyMGRldmljZV9pZHMlM0QlNUIwJTJDJTIwMSU1RCUyQyUyMCoqY29tcGlsZXJfYXJncyUyQyUyMCoqaW5wdXRfc2hhcGVzJTBBKSUwQSUwQXNhdmVfZGlyZWN0b3J5JTIwJTNEJTIwJTIyc2RfbmV1cm9uXzc2OCUyRiUyMiUwQXN0YWJsZV9kaWZmdXNpb24uc2F2ZV9wcmV0cmFpbmVkKHNhdmVfZGlyZWN0b3J5KQ==",highlighted:`<span class="hljs-comment"># Compile and save</span>
stable_diffusion = NeuronStableDiffusionPipeline.from_pretrained(
    model_id, export=<span class="hljs-literal">True</span>, device_ids=[<span class="hljs-number">0</span>, <span class="hljs-number">1</span>], **compiler_args, **input_shapes
)

save_directory = <span class="hljs-string">&quot;sd_neuron_768/&quot;</span>
stable_diffusion.save_pretrained(save_directory)`,lang:"python",wrap:!1}}),G=new U({props:{code:"JTIzJTIwUHVzaCUyMGFuZCUyMHNoYXJlJTIweW91ciUyMG1vZGVsJTIwdG8lMjB0aGUlMjBIdWdnaW5nRmFjZSUyMGh1YiUwQXJlcG9zaXRvcnlfaWQlMjAlM0QlMjAoJTBBJTIwJTIwJTIwJTIwJTIyeW91ci11c2VybmFtZSUyRnlvdXItYXdlc29tZS1tb2RlbCUyMiUyMCUyMCUyMyUyMFJlcGxhY2UlMjB3aXRoJTIweW91ciUyMHJlcG8lMjBpZCUyQyUyMGVnLiUyMCUyMkppbmd5YSUyRnN0YWJsZS1kaWZmdXNpb24tMi0xLW5ldXJvbnglMjIuJTBBKSUwQXN0YWJsZV9kaWZmdXNpb24ucHVzaF90b19odWIoc2F2ZV9kaXJlY3RvcnklMkMlMjByZXBvc2l0b3J5X2lkJTNEcmVwb3NpdG9yeV9pZCUyQyUyMHVzZV9hdXRoX3Rva2VuJTNEVHJ1ZSk=",highlighted:`<span class="hljs-comment"># Push and share your model to the HuggingFace hub</span>
repository_id = (
    <span class="hljs-string">&quot;your-username/your-awesome-model&quot;</span>  <span class="hljs-comment"># Replace with your repo id, eg. &quot;Jingya/stable-diffusion-2-1-neuronx&quot;.</span>
)
stable_diffusion.push_to_hub(save_directory, repository_id=repository_id, use_auth_token=<span class="hljs-literal">True</span>)`,lang:"python",wrap:!1}}),$=new U({props:{code:"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",highlighted:'optimum-cli <span class="hljs-built_in">export</span> neuron --model stabilityai/stable-diffusion-2-1-base \\\n  --task stable-diffusion \\\n  --batch_size 1 \\\n  --height 768 `<span class="hljs-comment"># height in pixels of generated image, eg. 512, 768` \\</span>\n  --width 768 `<span class="hljs-comment"># width in pixels of generated image, eg. 512, 768` \\</span>\n  --num_images_per_prompt 1 `<span class="hljs-comment"># number of images to generate per prompt, defaults to 1` \\</span>\n  --auto_cast matmul `<span class="hljs-comment"># cast only matrix multiplication operations` \\</span>\n  --auto_cast_type bf16 `<span class="hljs-comment"># cast operations from FP32 to BF16` \\</span>\n  sd_neuron_768/',lang:"bash",wrap:!1}}),X=new jl({props:{title:"Text-to-image Inference",local:"text-to-image-inference",headingTag:"h2"}}),_=new U({props:{code:"JTIzJTIwc3RhYmxlX2RpZmZ1c2lvbiUyMCUzRCUyME5ldXJvblN0YWJsZURpZmZ1c2lvblBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMjJ5b3VyLXVzZXJuYW1lJTJGeW91ci1hd2Vzb21lLW1vZGVsJTIyKSUyMCUyMCUyMyUyMFBhc3MlMjBhJTIwbG9jYWwlMjBwYXRoJTIwb3IlMjB5b3VyJTIwcmVwbyUyMGlkJTIwb24lMjB0aGUlMjBIdWdnaW5nRmFjZSUyMGh1Yi4=",highlighted:'<span class="hljs-comment"># stable_diffusion = NeuronStableDiffusionPipeline.from_pretrained(&quot;your-username/your-awesome-model&quot;)  # Pass a local path or your repo id on the HuggingFace hub.</span>',lang:"python",wrap:!1}}),S=new U({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERQTVNvbHZlck11bHRpc3RlcFNjaGVkdWxlciUwQSUwQSUwQXN0YWJsZV9kaWZmdXNpb24uc2NoZWR1bGVyJTIwJTNEJTIwRFBNU29sdmVyTXVsdGlzdGVwU2NoZWR1bGVyLmZyb21fY29uZmlnKHN0YWJsZV9kaWZmdXNpb24uc2NoZWR1bGVyLmNvbmZpZyk=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DPMSolverMultistepScheduler


stable_diffusion.scheduler = DPMSolverMultistepScheduler.from_config(stable_diffusion.scheduler.config)`,lang:"python",wrap:!1}}),Y=new U({props:{code:"aW1wb3J0JTIwdGltZSUwQSUwQWltcG9ydCUyMG51bXB5JTIwYXMlMjBucCUwQWZyb20lMjBtYXRwbG90bGliJTIwaW1wb3J0JTIwaW1hZ2UlMjBhcyUyMG1waW1nJTBBZnJvbSUyMG1hdHBsb3RsaWIlMjBpbXBvcnQlMjBweXBsb3QlMjBhcyUyMHBsdA==",highlighted:`<span class="hljs-keyword">import</span> time

<span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">from</span> matplotlib <span class="hljs-keyword">import</span> image <span class="hljs-keyword">as</span> mpimg
<span class="hljs-keyword">from</span> matplotlib <span class="hljs-keyword">import</span> pyplot <span class="hljs-keyword">as</span> plt`,lang:"python",wrap:!1}}),R=new U({props:{code:"JTIzJTIwUnVuJTIwcGlwZWxpbmUlMEFwcm9tcHQlMjAlM0QlMjAlNUIlMEElMjAlMjAlMjAlMjAlMjJhJTIwcGhvdG8lMjBvZiUyMGFuJTIwYXN0cm9uYXV0JTIwcmlkaW5nJTIwYSUyMGhvcnNlJTIwb24lMjBtYXJzJTIyJTJDJTBBJTIwJTIwJTIwJTIwJTIyc29uaWMlMjBvbiUyMHRoZSUyMG1vb24lMjIlMkMlMEElMjAlMjAlMjAlMjAlMjJlbHZpcyUyMHBsYXlpbmclMjBndWl0YXIlMjB3aGlsZSUyMGVhdGluZyUyMGElMjBob3Rkb2clMjIlMkMlMEElMjAlMjAlMjAlMjAlMjJzYXZlZCUyMGJ5JTIwdGhlJTIwYmVsbCUyMiUyQyUwQSUyMCUyMCUyMCUyMCUyMmVuZ2luZWVycyUyMGVhdGluZyUyMGx1bmNoJTIwYXQlMjB0aGUlMjBvcGVyYSUyMiUyQyUwQSUyMCUyMCUyMCUyMCUyMnBhbmRhJTIwZWF0aW5nJTIwYmFtYm9vJTIwb24lMjBhJTIwcGxhbmUlMjIlMkMlMEElMjAlMjAlMjAlMjAlMjJBJTIwZGlnaXRhbCUyMGlsbHVzdHJhdGlvbiUyMG9mJTIwYSUyMHN0ZWFtcHVuayUyMGZseWluZyUyMG1hY2hpbmUlMjBpbiUyMHRoZSUyMHNreSUyMHdpdGglMjBjb2dzJTIwYW5kJTIwbWVjaGFuaXNtcyUyQyUyMDRrJTJDJTIwZGV0YWlsZWQlMkMlMjB0cmVuZGluZyUyMGluJTIwYXJ0c3RhdGlvbiUyQyUyMGZhbnRhc3klMjB2aXZpZCUyMGNvbG9ycyUyMiUyQyUwQSUyMCUyMCUyMCUyMCUyMmtpZHMlMjBwbGF5aW5nJTIwc29jY2VyJTIwYXQlMjB0aGUlMjBGSUZBJTIwV29ybGQlMjBDdXAlMjIlMkMlMEElNUQlMEElMEElMEFwbHQudGl0bGUoJTIySW1hZ2UlMjIpJTBBcGx0LnhsYWJlbCglMjJYJTIwcGl4ZWwlMjBzY2FsaW5nJTIyKSUwQXBsdC55bGFiZWwoJTIyWSUyMHBpeGVscyUyMHNjYWxpbmclMjIpJTBBJTBBdG90YWxfdGltZSUyMCUzRCUyMDAlMEFmb3IlMjB4JTIwaW4lMjBwcm9tcHQlM0ElMEElMjAlMjAlMjAlMjBzdGFydF90aW1lJTIwJTNEJTIwdGltZS50aW1lKCklMEElMjAlMjAlMjAlMjBpbWFnZSUyMCUzRCUyMHN0YWJsZV9kaWZmdXNpb24oeCkuaW1hZ2VzJTVCMCU1RCUwQSUyMCUyMCUyMCUyMGluZl90aW1lJTIwJTNEJTIwdGltZS50aW1lKCklMjAtJTIwc3RhcnRfdGltZSUwQSUyMCUyMCUyMCUyMHByaW50KGYlMjIlNUJJbmZlcmVuY2UlMjBUaW1lJTVEJTIwJTdCbnAucm91bmQoaW5mX3RpbWUlMkMlMjAyKSU3RCUyMHNlY29uZHMuJTIyKSUwQSUyMCUyMCUyMCUyMGltYWdlLnNhdmUoJTIyaW1hZ2UucG5nJTIyKSUwQSUyMCUyMCUyMCUyMGltYWdlJTIwJTNEJTIwbXBpbWcuaW1yZWFkKCUyMmltYWdlLnBuZyUyMiklMEElMjAlMjAlMjAlMjAlMjMlMjBjbGVhcl9vdXRwdXQod2FpdCUzRFRydWUpJTBBJTIwJTIwJTIwJTIwcGx0Lmltc2hvdyhpbWFnZSklMEElMjAlMjAlMjAlMjBwbHQuc2hvdygp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Run pipeline</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = [
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;a photo of an astronaut riding a horse on mars&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;sonic on the moon&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;elvis playing guitar while eating a hotdog&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;saved by the bell&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;engineers eating lunch at the opera&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;panda eating bamboo on a plane&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;A digital illustration of a steampunk flying machine in the sky with cogs and mechanisms, 4k, detailed, trending in artstation, fantasy vivid colors&quot;</span>,
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;kids playing soccer at the FIFA World Cup&quot;</span>,
<span class="hljs-meta">... </span>]


<span class="hljs-meta">&gt;&gt;&gt; </span>plt.title(<span class="hljs-string">&quot;Image&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>plt.xlabel(<span class="hljs-string">&quot;X pixel scaling&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>plt.ylabel(<span class="hljs-string">&quot;Y pixels scaling&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>total_time = <span class="hljs-number">0</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> x <span class="hljs-keyword">in</span> prompt:
<span class="hljs-meta">... </span>    start_time = time.time()
<span class="hljs-meta">... </span>    image = stable_diffusion(x).images[<span class="hljs-number">0</span>]
<span class="hljs-meta">... </span>    inf_time = time.time() - start_time
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;[Inference Time] <span class="hljs-subst">{np.<span class="hljs-built_in">round</span>(inf_time, <span class="hljs-number">2</span>)}</span> seconds.&quot;</span>)
<span class="hljs-meta">... </span>    image.save(<span class="hljs-string">&quot;image.png&quot;</span>)
<span class="hljs-meta">... </span>    image = mpimg.imread(<span class="hljs-string">&quot;image.png&quot;</span>)
<span class="hljs-meta">... </span>    <span class="hljs-comment"># clear_output(wait=True)</span>
<span class="hljs-meta">... </span>    plt.imshow(image)
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