optimum-neuron/v0.2.0.dev4/en/_app/immutable/nodes/26.ccc8cb4d.js

import{s as Se,f as ke,o as Ye,n as Fe}from"../chunks/scheduler.85c25b89.js";import{S as Qe,i as qe,g as s,s as i,r as p,A as Re,h as o,f as n,c as a,j as Ue,u as m,x as d,k as w,y as _e,a as l,v as u,d as h,t as c,w as f}from"../chunks/index.c9bcf812.js";import{T as ze}from"../chunks/Tip.d8f753fa.js";import{D as Ze}from"../chunks/Docstring.af42ec0f.js";import{C as Je}from"../chunks/CodeBlock.c004bd26.js";import{H as Q}from"../chunks/index.9790a2b6.js";function De(D){let r,$="We recommend using a <code>inf2.8xlarge</code> or a larger instance for the model compilation. You will also be able to compile the model with the Optimum CLI on a CPU-only instance (needs ~35 GB memory), and then run the pre-compiled model on <code>inf2.xlarge</code> to reduce the expenses. In this case, don’t forget to disable validation of inference by adding the <code>--disable-validation</code> argument.";return{c(){r=s("p"),r.innerHTML=$},l(g){r=o(g,"P",{"data-svelte-h":!0}),d(r)!=="svelte-1evfral"&&(r.innerHTML=$)},m(g,q){l(g,r,q)},p:Fe,d(g){g&&n(r)}}}function Oe(D){let r,$,g,q,x,O,T,K,U,Ce='<a href="https://huggingface.co/papers/2310.00426" rel="nofollow">PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis</a> is Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li.',ee,v,je="Some notes about this pipeline:",te,_,Pe='<li>It uses a Transformer backbone (instead of a UNet) for denoising. As such it has a similar architecture as <a href="./dit">DiT</a>.</li> <li>It was trained using text conditions computed from T5. This aspect makes the pipeline better at following complex text prompts with intricate details.</li> <li>It is good at producing high-resolution images at different aspect ratios. To get the best results, the authors recommend some size brackets which can be found <a href="https://github.com/PixArt-alpha/PixArt-alpha/blob/08fbbd281ec96866109bdd2cdb75f2f58fb17610/diffusion/data/datasets/utils.py" rel="nofollow">here</a>.</li> <li>It rivals the quality of state-of-the-art text-to-image generation systems (as of this writing) such as Stable Diffusion XL, Imagen, and DALL-E 2, while being more efficient than them.</li>',ne,J,Ae='You can find the original codebase at <a href="https://github.com/PixArt-alpha/PixArt-alpha" rel="nofollow">PixArt-alpha/PixArt-alpha</a> and all the available checkpoints at <a href="https://huggingface.co/PixArt-alpha" rel="nofollow">PixArt-alpha</a>.',le,C,Ge="🤗 <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.",ie,j,ae,P,Ve="To deploy models in the PixArt-α pipeline, you will need to compile them to TorchScript optimized for AWS Neuron. There are four components which need to be exported to the <code>.neuron</code> format to boost the performance:",se,A,Ie="<li>Text encoder</li> <li>Transformer</li> <li>VAE encoder</li> <li>VAE decoder</li>",oe,G,Be="You can either compile and export a PixArt-α Checkpoint via CLI or <code>NeuronPixArtAlphaPipeline</code> class.",re,V,pe,I,me,b,ue,B,he,L,ce,H,fe,X,Le="<code>NeuronPixArtAlphaPipeline</code> class allows you to generate images from a text prompt on neuron devices similar to the experience with <code>Diffusers</code>.",de,E,He="With pre-compiled PixArt-α models, now generate an image with a prompt on Neuron:",ge,N,ye,y,Xe,Me,W,be,Z,Ee="Pipeline for text-to-image generation using PixArt-α.",we,M,S,ve,R,k,$e,Y,Ne='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 🤗 !',xe,z,Te;return x=new Q({props:{title:"PixArt-α",local:"pixart-α",headingTag:"h1"}}),T=new Q({props:{title:"Overview",local:"overview",headingTag:"h2"}}),j=new Q({props:{title:"Export to Neuron",local:"export-to-neuron",headingTag:"h2"}}),V=new Q({props:{title:"Option 1: CLI",local:"option-1-cli",headingTag:"h3"}}),I=new Je({props:{code:"b3B0aW11bS1jbGklMjBleHBvcnQlMjBuZXVyb24lMjAtLW1vZGVsJTIwUGl4QXJ0LWFscGhhJTJGUGl4QXJ0LVhMLTItNTEyeDUxMiUyMC0tYmF0Y2hfc2l6ZSUyMDElMjAtLWhlaWdodCUyMDUxMiUyMC0td2lkdGglMjA1MTIlMjAtLW51bV9pbWFnZXNfcGVyX3Byb21wdCUyMDElMjAtLXRvcmNoX2R0eXBlJTIwYmZsb2F0MTYlMjAtLXNlcXVlbmNlX2xlbmd0aCUyMDEyMCUyMHBpeGFydF9hbHBoYV9uZXVyb25fNTEyJTJG",highlighted:'optimum-cli <span class="hljs-built_in">export</span> neuron --model PixArt-alpha/PixArt-XL-2-512x512 --batch_size 1 --height 512 --width 512 --num_images_per_prompt 1 --torch_dtype bfloat16 --sequence_length 120 pixart_alpha_neuron_512/',wrap:!1}}),b=new ze({props:{warning:!1,$$slots:{default:[De]},$$scope:{ctx:D}}}),B=new Q({props:{title:"Option 2: Python API",local:"option-2-python-api",headingTag:"h3"}}),L=new Je({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> optimum.neuron <span class="hljs-keyword">import</span> NeuronPixArtAlphaPipeline

<span class="hljs-comment"># Compile</span>
compiler_args = {<span class="hljs-string">&quot;auto_cast&quot;</span>: <span class="hljs-string">&quot;none&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>, <span class="hljs-string">&quot;sequence_length&quot;</span>: <span class="hljs-number">120</span>}

neuron_model = NeuronPixArtAlphaPipeline.from_pretrained(<span class="hljs-string">&quot;PixArt-alpha/PixArt-XL-2-512x512&quot;</span>, torch_dtype=torch.bfloat16, export=<span class="hljs-literal">True</span>, disable_neuron_cache=<span class="hljs-literal">True</span>, **compiler_args, **input_shapes)

<span class="hljs-comment"># Save locally</span>
neuron_model.save_pretrained(<span class="hljs-string">&quot;pixart_alpha_neuron_512/&quot;</span>)

<span class="hljs-comment"># Upload to the HuggingFace Hub</span>
neuron_model.push_to_hub(
    <span class="hljs-string">&quot;pixart_alpha_neuron_512/&quot;</span>, repository_id=<span class="hljs-string">&quot;Jingya/PixArt-XL-2-512x512-neuronx&quot;</span>  <span class="hljs-comment"># Replace with your HF Hub repo id</span>
)`,wrap:!1}}),H=new Q({props:{title:"Text-to-Image",local:"text-to-image",headingTag:"h2"}}),N=new Je({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> optimum.neuron <span class="hljs-keyword">import</span> NeuronPixArtAlphaPipeline

neuron_model = NeuronPixArtAlphaPipeline.from_pretrained(<span class="hljs-string">&quot;pixart_alpha_neuron_512/&quot;</span>)
prompt = <span class="hljs-string">&quot;Oppenheimer sits on the beach on a chair, watching a nuclear exposition with a huge mushroom cloud, 120mm.&quot;</span>
image = neuron_model(prompt=prompt).images[<span class="hljs-number">0</span>]`,wrap:!1}}),W=new Q({props:{title:"NeuronPixArtAlphaPipeline",local:"optimum.neuron.NeuronPixArtAlphaPipeline",headingTag:"h2"}}),S=new Ze({props:{name:"class optimum.neuron.NeuronPixArtAlphaPipeline",anchor:"optimum.neuron.NeuronPixArtAlphaPipeline",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/optimum-neuron/blob/v0.2.0.dev4/optimum/neuron/modeling_diffusion.py#L1601"}}),k=new 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