optimum-neuron/pr_1113/en/_app/immutable/nodes/35.414ce76a.js

import{s as It,a as $t,o as Zt,n as Wt}from"../chunks/scheduler.56725da7.js";import{S as kt,i as St,e as i,s as a,c as r,h as Gt,a as o,d as n,b as s,f as $,g as p,j as h,k as M,l as de,m as l,n as m,t as c,o as u,p as d}from"../chunks/index.18a26576.js";import{T as Bt}from"../chunks/Tip.5b941656.js";import{C as xt}from"../chunks/CopyLLMTxtMenu.c5feff19.js";import{D as Me}from"../chunks/Docstring.ae0283b4.js";import{C as ye}from"../chunks/CodeBlock.6dd2f5ab.js";import{H as T}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.0f5f04c9.js";function Ct(Te){let f,Z="To apply optimized compute of Unet’s attention score, please configure your environment variable with <code>export NEURON_FUSE_SOFTMAX=1</code>.",w,b,J='Besides, don’t hesitate to tweak the compilation configuration to find the best tradeoff between performance v.s accuracy in your use case. By default, we suggest casting FP32 matrix multiplication operations to BF16 which offers good performance with moderate sacrifice of the accuracy. Check out the guide from <a href="https://awsdocs-neuron.readthedocs-hosted.com/en/latest/general/appnotes/neuronx-cc/neuronx-cc-training-mixed-precision.html#neuronx-cc-training-mixed-precision" rel="nofollow">AWS Neuron documentation</a> to better understand the options for your compilation.';return{c(){f=i("p"),f.innerHTML=Z,w=a(),b=i("p"),b.innerHTML=J},l(g){f=o(g,"P",{"data-svelte-h":!0}),h(f)!=="svelte-6bhicj"&&(f.innerHTML=Z),w=s(g),b=o(g,"P",{"data-svelte-h":!0}),h(b)!=="svelte-1dh7n4s"&&(b.innerHTML=J)},m(g,y){l(g,f,y),l(g,w,y),l(g,b,y)},p:Wt,d(g){g&&(n(f),n(w),n(b))}}}function Vt(Te){let f,Z,w,b,J,g,y,we,W,Je,k,pt='Stable Diffusion is a text-to-image <em>latent diffusion</em> model built upon the work of the original <a href="https://stability.ai/blog/stable-diffusion-public-release" rel="nofollow">Stable Diffusion</a>, and it was led by Robin Rombach and Katherine Crowson from <a href="https://stability.ai/" rel="nofollow">Stability AI</a> and <a href="https://laion.ai/" rel="nofollow">LAION</a>.',_e,S,mt="🤗 <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.",Ne,G,je,B,ct="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:",ve,x,ut="<li>Text encoder</li> <li>U-Net</li> <li>VAE encoder</li> <li>VAE decoder</li>",Ue,C,dt="You can either compile and export a Stable Diffusion Checkpoint via CLI or <code>NeuronStableDiffusionPipeline</code> class.",Ie,V,$e,z,ft="Here is an example of exporting stable diffusion components with <code>Optimum</code> CLI:",Ze,X,We,U,ht="<p>We recommend using a <code>inf2.8xlarge</code> or a larger instance for the model compilation. You will also be able to compile the model with the Optimum CLI on a CPU-only instance (needs ~35 GB memory), and then run the pre-compiled model on <code>inf2.xlarge</code> to reduce the expenses. In this case, don’t forget to disable validation of inference by adding the <code>--disable-validation</code> argument.</p>",ke,R,Se,F,gt="Here is an example of exporting stable diffusion components with <code>NeuronStableDiffusionPipeline</code>:",Ge,I,Be,E,xe,H,Ce,Y,Mt="<code>NeuronStableDiffusionPipeline</code> class allows you to generate images from a text prompt on neuron devices similar to the experience with <code>Diffusers</code>.",Ve,D,bt="With pre-compiled Stable Diffusion models, now generate an image with a prompt on Neuron:",ze,L,Xe,_,yt,Re,P,Fe,q,Tt="With the <code>NeuronStableDiffusionImg2ImgPipeline</code> class, you can generate a new image conditioned on a text prompt and an initial image.",Ee,Q,He,A,wt='<thead><tr><th align="center"><code>image</code></th> <th align="center"><code>prompt</code></th> <th align="center">output</th> <th align="right"></th></tr></thead> <tbody><tr><td align="center"><img src="https://huggingface.co/datasets/optimum/documentation-images/resolve/main/neuron/models/03-sd-img2img-init.png" alt="landscape photo" width="256" height="256"/></td> <td align="center"><strong><em>ghibli style, a fantasy landscape with snowcapped mountains, trees, lake with detailed reflection. warm colors, 8K</em></strong></td> <td align="center"><img src="https://huggingface.co/datasets/optimum/documentation-images/resolve/main/neuron/models/04-sd-img2img.png" alt="drawing" width="250"/></td> <td align="right"></td></tr></tbody>',Ye,K,De,O,Jt="With the <code>NeuronStableDiffusionInpaintPipeline</code> class, you can edit specific parts of an image by providing a mask and a text prompt.",Le,ee,Pe,te,_t='<thead><tr><th align="center"><code>image</code></th> <th align="center"><code>mask_image</code></th> <th align="center"><code>prompt</code></th> <th align="right">output</th></tr></thead> <tbody><tr><td align="center"><img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png" alt="drawing" width="250"/></td> <td align="center"><img src="https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png" alt="drawing" width="250"/></td> <td align="center"><strong><em>Face of a yellow cat, high resolution, sitting on a park bench</em></strong></td> <td align="right"><img src="https://huggingface.co/datasets/optimum/documentation-images/resolve/main/neuron/models/05-sd-inpaint.png" alt="drawing" width="250"/></td></tr></tbody>',qe,ne,Qe,N,le,it,fe,ae,Ae,se,Ke,j,ie,ot,he,oe,Oe,re,et,v,pe,rt,ge,me,tt,ce,Nt='Are there any other diffusion features that you want us to support in 🤗<code>Optimum-neuron</code>? Please file an issue to <a href="https://github.com/huggingface/optimum-neuron" rel="nofollow"><code>Optimum-neuron</code> Github repo</a> or discuss with us on <a href="https://discuss.huggingface.co/c/optimum/" rel="nofollow">HuggingFace’s community forum</a>, cheers 🤗 !',nt,be,lt;return J=new xt({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),y=new T({props:{title:"Stable Diffusion",local:"stable-diffusion",headingTag:"h1"}}),W=new T({props:{title:"Overview",local:"overview",headingTag:"h2"}}),G=new T({props:{title:"Export to Neuron",local:"export-to-neuron",headingTag:"h2"}}),V=new T({props:{title:"Option 1: cli",local:"option-1-cli",headingTag:"h3"}}),X=new ye({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  --batch_size 1 \\\n  --height 512 `<span class="hljs-comment"># height in pixels of generated image, eg. 512, 768` \\</span>\n  --width 512 `<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/',lang:"bash",wrap:!1}}),R=new T({props:{title:"Option 2: Python API",local:"option-2-python-api",headingTag:"h3"}}),I=new Bt({props:{$$slots:{default:[Ct]},$$scope:{ctx:Te}}}),E=new ye({props:{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> NeuronStableDiffusionPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>model_id = <span class="hljs-string">&quot;stable-diffusion-v1-5/stable-diffusion-v1-5&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>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>}
<span class="hljs-meta">&gt;&gt;&gt; </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">512</span>, <span class="hljs-string">&quot;width&quot;</span>: <span class="hljs-number">512</span>}

<span class="hljs-meta">&gt;&gt;&gt; </span>stable_diffusion = NeuronStableDiffusionPipeline.from_pretrained(model_id, export=<span class="hljs-literal">True</span>, **compiler_args, **input_shapes)

<span class="hljs-comment"># Save locally or upload to the HuggingFace Hub</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>save_directory = <span class="hljs-string">&quot;sd_neuron/&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>stable_diffusion.save_pretrained(save_directory)
<span class="hljs-meta">&gt;&gt;&gt; </span>stable_diffusion.push_to_hub(
<span class="hljs-meta">... </span>    save_directory, repository_id=<span class="hljs-string">&quot;my-neuron-repo&quot;</span>
<span class="hljs-meta">... </span>)`,lang:"python",wrap:!1}}),H=new T({props:{title:"Text-to-Image",local:"text-to-image",headingTag:"h2"}}),L=new ye({props:{code:"ZnJvbSUyMG9wdGltdW0ubmV1cm9uJTIwaW1wb3J0JTIwTmV1cm9uU3RhYmxlRGlmZnVzaW9uUGlwZWxpbmUlMEElMEFzdGFibGVfZGlmZnVzaW9uJTIwJTNEJTIwTmV1cm9uU3RhYmxlRGlmZnVzaW9uUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKCUyMnNkX25ldXJvbiUyRiUyMiklMEFwcm9tcHQlMjAlM0QlMjAlMjJhJTIwcGhvdG8lMjBvZiUyMGFuJTIwYXN0cm9uYXV0JTIwcmlkaW5nJTIwYSUyMGhvcnNlJTIwb24lMjBtYXJzJTIyJTBBaW1hZ2UlMjAlM0QlMjBzdGFibGVfZGlmZnVzaW9uKHByb21wdCkuaW1hZ2VzJTVCMCU1RA==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> optimum.neuron <span class="hljs-keyword">import</span> NeuronStableDiffusionPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>stable_diffusion = NeuronStableDiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;sd_neuron/&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;a photo of an astronaut riding a horse on mars&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = stable_diffusion(prompt).images[<span class="hljs-number">0</span>]`,lang:"python",wrap:!1}}),P=new T({props:{title:"Image-to-Image",local:"image-to-image",headingTag:"h2"}}),Q=new ye({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO
<span class="hljs-keyword">from</span> optimum.neuron <span class="hljs-keyword">import</span> NeuronStableDiffusionImg2ImgPipeline

<span class="hljs-comment"># compile &amp; save</span>
model_id = <span class="hljs-string">&quot;nitrosocke/Ghibli-Diffusion&quot;</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">512</span>, <span class="hljs-string">&quot;width&quot;</span>: <span class="hljs-number">512</span>}
pipeline = NeuronStableDiffusionImg2ImgPipeline.from_pretrained(model_id, export=<span class="hljs-literal">True</span>, **input_shapes)
pipeline.save_pretrained(<span class="hljs-string">&quot;sd_img2img/&quot;</span>)

url = <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg&quot;</span>

response = requests.get(url)
init_image = Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)
init_image = init_image.resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))

prompt = <span class="hljs-string">&quot;ghibli style, a fantasy landscape with snowcapped mountains, trees, lake with detailed reflection. sunlight and cloud in the sky, warm colors, 8K&quot;</span>

image = pipeline(prompt=prompt, image=init_image, strength=<span class="hljs-number">0.75</span>, guidance_scale=<span class="hljs-number">7.5</span>).images[<span class="hljs-number">0</span>]
image.save(<span class="hljs-string">&quot;fantasy_landscape.png&quot;</span>)`,lang:"python",wrap:!1}}),K=new T({props:{title:"Inpaint",local:"inpaint",headingTag:"h2"}}),ee=new ye({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO
<span class="hljs-keyword">from</span> optimum.neuron <span class="hljs-keyword">import</span> NeuronStableDiffusionInpaintPipeline

model_id = <span class="hljs-string">&quot;stable-diffusion-v1-5/stable-diffusion-inpainting&quot;</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">512</span>, <span class="hljs-string">&quot;width&quot;</span>: <span class="hljs-number">512</span>}
pipeline = NeuronStableDiffusionInpaintPipeline.from_pretrained(model_id, export=<span class="hljs-literal">True</span>, **input_shapes)
pipeline.save_pretrained(<span class="hljs-string">&quot;sd_inpaint/&quot;</span>)

<span class="hljs-keyword">def</span> <span class="hljs-title function_">download_image</span>(<span class="hljs-params">url</span>):
    response = requests.get(url)
    <span class="hljs-keyword">return</span> Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)

img_url = <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png&quot;</span>
mask_url = <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png&quot;</span>

init_image = download_image(img_url).resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))
mask_image = download_image(mask_url).resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))

prompt = <span class="hljs-string">&quot;Face of a yellow cat, high resolution, sitting on a park bench&quot;</span>
image = pipeline(prompt=prompt, image=init_image, mask_image=mask_image).images[<span class="hljs-number">0</span>]
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