diffusers/pr_13896/en/_app/immutable/nodes/151.a683cd74.js

import{s as ce,n as Me,o as ge}from"../chunks/scheduler.53228c21.js";import{S as fe,i as de,e as i,s as n,c as o,h as he,a as p,d as t,b as s,f as oe,g as u,j as c,k as ee,l as ye,m as a,n as M,t as g,o as f,p as d}from"../chunks/index.cac5d66a.js";import{C as Ee}from"../chunks/CopyLLMTxtMenu.f1196f8c.js";import{C as ue}from"../chunks/CodeBlock.606cbaf4.js";import{H as K,E as Te}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.05c95d6f.js";function Ue(le){let r,k,$,v,h,G,y,_,m,te='<img alt="LoRA" src="https://img.shields.io/badge/LoRA-d8b4fe?style=flat"/>',N,E,ae="[ERNIE-Image] is a powerful and highly efficient image generation model with 8B parameters. Currently there’s only two models to be released:",x,T,ne='<thead><tr><th>Model</th> <th>Hugging Face</th></tr></thead> <tbody><tr><td>ERNIE-Image</td> <td><a href="https://huggingface.co/baidu/ERNIE-Image" rel="nofollow">https://huggingface.co/baidu/ERNIE-Image</a></td></tr> <tr><td>ERNIE-Image-Turbo</td> <td><a href="https://huggingface.co/baidu/ERNIE-Image-Turbo" rel="nofollow">https://huggingface.co/baidu/ERNIE-Image-Turbo</a></td></tr></tbody>',V,U,X,j,se="ERNIE-Image is designed with a relatively compact architecture and solid instruction-following capability, emphasizing parameter efficiency. Based on an 8B DiT backbone, it provides performance that is comparable in some scenarios to larger (20B+) models, while maintaining reasonable parameter efficiency. It offers a relatively stable level of performance in instruction understanding and execution, text generation (e.g., English / Chinese / Japanese), and overall stability.",F,b,A,w,ie="ERNIE-Image-Turbo is a distilled variant of ERNIE-Image, requiring only 8 NFEs (Number of Function Evaluations) and offering a more efficient alternative with relatively comparable performance to the full model in certain cases.",Y,I,O,J,pe="Use [ErnieImagePipeline] to generate images from text prompts. The pipeline supports Prompt Enhancer (PE) by default, which enhances the user’s raw prompt to improve output quality, though it may reduce instruction-following accuracy.",S,R,re='We provide a pretrained 3B-parameter PE model; however, using larger language models (e.g., Gemini or ChatGPT) for prompt enhancement may yield better results. The system prompt template is available at: <a href="https://huggingface.co/baidu/ERNIE-Image/blob/main/pe/chat_template.jinja" rel="nofollow">https://huggingface.co/baidu/ERNIE-Image/blob/main/pe/chat_template.jinja</a>.',H,Q,me="If you prefer not to use PE, set use_pe=False.",P,C,z,W,L,Z,q,B,D;return h=new Ee({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),y=new K({props:{title:"Ernie-Image",local:"ernie-image",headingTag:"h1"}}),U=new K({props:{title:"ERNIE-Image",local:"ernie-image",headingTag:"h2"}}),b=new K({props:{title:"ERNIE-Image-Turbo",local:"ernie-image-turbo",headingTag:"h2"}}),I=new K({props:{title:"ErnieImagePipeline",local:"ernieimagepipeline",headingTag:"h2"}}),C=new ue({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> ErnieImagePipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image

pipe = ErnieImagePipeline.from_pretrained(<span class="hljs-string">&quot;baidu/ERNIE-Image&quot;</span>, torch_dtype=torch.bfloat16)
pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)
<span class="hljs-comment"># If you are running low on GPU VRAM, you can enable offloading</span>
pipe.enable_model_cpu_offload()

prompt = <span class="hljs-string">&quot;一只黑白相间的中华田园犬&quot;</span>
images = pipe(
    prompt=prompt,
    height=<span class="hljs-number">1024</span>,
    width=<span class="hljs-number">1024</span>,
    num_inference_steps=<span class="hljs-number">50</span>,
    guidance_scale=<span class="hljs-number">4.0</span>,
    generator=torch.Generator(<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">42</span>),
    use_pe=<span class="hljs-literal">True</span>,
).images
images[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;ernie-image-output.png&quot;</span>)`,lang:"python",wrap:!1}}),W=new ue({props:{code:"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",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> ErnieImagePipeline
<span class="hljs-keyword">from</span> diffusers.utils <span class="hljs-keyword">import</span> load_image

pipe = ErnieImagePipeline.from_pretrained(<span class="hljs-string">&quot;baidu/ERNIE-Image-Turbo&quot;</span>, torch_dtype=torch.bfloat16)
pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)
<span class="hljs-comment"># If you are running low on GPU VRAM, you can enable offloading</span>
pipe.enable_model_cpu_offload()

prompt = <span class="hljs-string">&quot;一只黑白相间的中华田园犬&quot;</span>
images = pipe(
    prompt=prompt,
    height=<span class="hljs-number">1024</span>,
    width=<span class="hljs-number">1024</span>,
    num_inference_steps=<span class="hljs-number">8</span>,
    guidance_scale=<span class="hljs-number">1.0</span>,
    generator=torch.Generator(<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">42</span>),
    use_pe=<span class="hljs-literal">True</span>,
).images
images[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;ernie-image-turbo-output.png&quot;</span>)`,lang:"python",wrap:!1}}),Z=new Te({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/api/pipelines/ernie_image.md"}}),{c(){r=i("meta"),k=n(),$=i("p"),v=n(),o(h.$$.fragment),G=n(),o(y.$$.fragment),_=n(),m=i("div"),m.innerHTML=te,N=n(),E=i("p"),E.textContent=ae,x=n(),T=i("table"),T.innerHTML=ne,V=n(),o(U.$$.fragment),X=n(),j=i("p"),j.textContent=se,F=n(),o(b.$$.fragment),A=n(),w=i("p"),w.textContent=ie,Y=n(),o(I.$$.fragment),O=n(),J=i("p"),J.textContent=pe,S=n(),R=i("p"),R.innerHTML=re,H=n(),Q=i("p"),Q.textContent=me,P=n(),o(C.$$.fragment),z=n(),o(W.$$.fragment),L=n(),o(Z.$$.fragment),q=n(),B=i("p"),this.h()},l(e){const l=he("svelte-u9bgzb",document.head);r=p(l,"META",{name:!0,content:!0}),l.forEach(t),k=s(e),$=p(e,"P",{}),oe($).forEach(t),v=s(e),u(h.$$.fragment,e),G=s(e),u(y.$$.fragment,e),_=s(e),m=p(e,"DIV",{class:!0,"data-svelte-h":!0}),c(m)!=="svelte-si9ct8"&&(m.innerHTML=te),N=s(e),E=p(e,"P",{"data-svelte-h":!0}),c(E)!=="svelte-1ctw5l6"&&(E.textContent=ae),x=s(e),T=p(e,"TABLE",{"data-svelte-h":!0}),c(T)!=="svelte-ol15kr"&&(T.innerHTML=ne),V=s(e),u(U.$$.fragment,e),X=s(e),j=p(e,"P",{"data-svelte-h":!0}),c(j)!=="svelte-e9r35z"&&(j.textContent=se),F=s(e),u(b.$$.fragment,e),A=s(e),w=p(e,"P",{"data-svelte-h":!0}),c(w)!=="svelte-1mgkt88"&&(w.textContent=ie),Y=s(e),u(I.$$.fragment,e),O=s(e),J=p(e,"P",{"data-svelte-h":!0}),c(J)!=="svelte-etigmt"&&(J.textContent=pe),S=s(e),R=p(e,"P",{"data-svelte-h":!0}),c(R)!=="svelte-8yxakn"&&(R.innerHTML=re),H=s(e),Q=p(e,"P",{"data-svelte-h":!0}),c(Q)!=="svelte-1suhr09"&&(Q.textContent=me),P=s(e),u(C.$$.fragment,e),z=s(e),u(W.$$.fragment,e),L=s(e),u(Z.$$.fragment,e),q=s(e),B=p(e,"P",{}),oe(B).forEach(t),this.h()},h(){ee(r,"name","hf:doc:metadata"),ee(r,"content",je),ee(m,"class","flex flex-wrap space-x-1")},m(e,l){ye(document.head,r),a(e,k,l),a(e,$,l),a(e,v,l),M(h,e,l),a(e,G,l),M(y,e,l),a(e,_,l),a(e,m,l),a(e,N,l),a(e,E,l),a(e,x,l),a(e,T,l),a(e,V,l),M(U,e,l),a(e,X,l),a(e,j,l),a(e,F,l),M(b,e,l),a(e,A,l),a(e,w,l),a(e,Y,l),M(I,e,l),a(e,O,l),a(e,J,l),a(e,S,l),a(e,R,l),a(e,H,l),a(e,Q,l),a(e,P,l),M(C,e,l),a(e,z,l),M(W,e,l),a(e,L,l),M(Z,e,l),a(e,q,l),a(e,B,l),D=!0},p:Me,i(e){D||(g(h.$$.fragment,e),g(y.$$.fragment,e),g(U.$$.fragment,e),g(b.$$.fragment,e),g(I.$$.fragment,e),g(C.$$.fragment,e),g(W.$$.fragment,e),g(Z.$$.fragment,e),D=!0)},o(e){f(h.$$.fragment,e),f(y.$$.fragment,e),f(U.$$.fragment,e),f(b.$$.fragment,e),f(I.$$.fragment,e),f(C.$$.fragment,e),f(W.$$.fragment,e),f(Z.$$.fragment,e),D=!1},d(e){e&&(t(k),t($),t(v),t(G),t(_),t(m),t(N),t(E),t(x),t(T),t(V),t(X),t(j),t(F),t(A),t(w),t(Y),t(O),t(J),t(S),t(R),t(H),t(Q),t(P),t(z),t(L),t(q),t(B)),t(r),d(h,e),d(y,e),d(U,e),d(b,e),d(I,e),d(C,e),d(W,e),d(Z,e)}}}const je='{"title":"Ernie-Image","local":"ernie-image","sections":[{"title":"ERNIE-Image","local":"ernie-image","sections":[],"depth":2},{"title":"ERNIE-Image-Turbo","local":"ernie-image-turbo","sections":[],"depth":2},{"title":"ErnieImagePipeline","local":"ernieimagepipeline","sections":[],"depth":2}],"depth":1}';function be(le){return ge(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Ce extends fe{constructor(r){super(),de(this,r,be,Ue,ce,{})}}export{Ce as component};