diffusers/main/en/_app/pages/using-diffusers/conditional_image_generation.mdx-hf-doc-builder.js

import{S as mt,i as ct,s as ht,e as l,k as u,w as A,t as n,M as dt,c as r,d as t,m,a as f,x as V,h as s,b as p,N as gt,G as o,g as i,y as B,L as bt,q as F,o as R,B as L,v as yt}from"../../chunks/vendor-hf-doc-builder.js";import{I as wt}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as _e}from"../../chunks/CodeBlock-hf-doc-builder.js";import{D as _t}from"../../chunks/DocNotebookDropdown-hf-doc-builder.js";function vt(ze){let g,H,b,y,Y,P,ve,z,$e,K,E,ee,D,Pe,te,w,Ee,x,ke,Te,oe,c,je,U,Je,Ze,k,Me,Ie,ae,h,De,G,xe,Ue,T,Q,Ge,Ne,ie,j,ne,_,We,N,qe,Ce,se,J,le,v,Se,X,Ae,Ve,re,Z,fe,$,Be,M,O,Fe,Re,pe,W,Le,ue,I,me,q,Ye,ce,d,Qe,he;return P=new wt({}),E=new _t({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/conditional_image_generation.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/pytorch/conditional_image_generation.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/tensorflow/conditional_image_generation.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/conditional_image_generation.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/pytorch/conditional_image_generation.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/tensorflow/conditional_image_generation.ipynb"}]}}),j=new _e({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTBBZ2VuZXJhdG9yJTIwJTNEJTIwRGlmZnVzaW9uUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKCUyMnJ1bndheW1sJTJGc3RhYmxlLWRpZmZ1c2lvbi12MS01JTIyJTJDJTIwdXNlX3NhZmV0ZW5zb3JzJTNEVHJ1ZSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>generator = DiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>, use_safetensors=<span class="hljs-literal">True</span>)`}}),J=new _e({props:{code:"Z2VuZXJhdG9yLnRvKCUyMmN1ZGElMjIp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>generator.to(<span class="hljs-string">&quot;cuda&quot;</span>)'}}),Z=new _e({props:{code:"aW1hZ2UlMjAlM0QlMjBnZW5lcmF0b3IoJTIyQW4lMjBpbWFnZSUyMG9mJTIwYSUyMHNxdWlycmVsJTIwaW4lMjBQaWNhc3NvJTIwc3R5bGUlMjIpLmltYWdlcyU1QjAlNUQ=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>image = generator(<span class="hljs-string">&quot;An image of a squirrel in Picasso style&quot;</span>).images[<span class="hljs-number">0</span>]'}}),I=new _e({props:{code:"aW1hZ2Uuc2F2ZSglMjJpbWFnZV9vZl9zcXVpcnJlbF9wYWludGluZy5wbmclMjIp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;image_of_squirrel_painting.png&quot;</span>)'}}),{c(){g=l("meta"),H=u(),b=l("h1"),y=l("a"),Y=l("span"),A(P.$$.fragment),ve=u(),z=l("span"),$e=n("Conditional image generation"),K=u(),A(E.$$.fragment),ee=u(),D=l("p"),Pe=n("Conditional image generation allows you to generate images from a text prompt. The text is converted into embeddings which are used to condition the model to generate an image from noise."),te=u(),w=l("p"),Ee=n("The "),x=l("a"),ke=n("DiffusionPipeline"),Te=n(" is the easiest way to use a pre-trained diffusion system for inference."),oe=u(),c=l("p"),je=n("Start by creating an instance of "),U=l("a"),Je=n("DiffusionPipeline"),Ze=n(" and specify which pipeline "),k=l("a"),Me=n("checkpoint"),Ie=n(" you would like to download."),ae=u(),h=l("p"),De=n("In this guide, you\u2019ll use "),G=l("a"),xe=n("DiffusionPipeline"),Ue=n(" for text-to-image generation with "),T=l("a"),Q=l("code"),Ge=n("runwayml/stable-diffusion-v1-5"),Ne=n(":"),ie=u(),A(j.$$.fragment),ne=u(),_=l("p"),We=n("The "),N=l("a"),qe=n("DiffusionPipeline"),Ce=n(` downloads and caches all modeling, tokenization, and scheduling components.
Because the model consists of roughly 1.4 billion parameters, we strongly recommend running it on a GPU.
You can move the generator object to a GPU, just like you would in PyTorch:`),se=u(),A(J.$$.fragment),le=u(),v=l("p"),Se=n("Now you can use the "),X=l("code"),Ae=n("generator"),Ve=n(" on your text prompt:"),re=u(),A(Z.$$.fragment),fe=u(),$=l("p"),Be=n("The output is by default wrapped into a "),M=l("a"),O=l("code"),Fe=n("PIL.Image"),Re=n(" object."),pe=u(),W=l("p"),Le=n("You can save the image by calling:"),ue=u(),A(I.$$.fragment),me=u(),q=l("p"),Ye=n("Try out the Spaces below, and feel free to play around with the guidance scale parameter to see how it affects the image quality!"),ce=u(),d=l("iframe"),this.h()},l(e){const a=dt('[data-svelte="svelte-1phssyn"]',document.head);g=r(a,"META",{name:!0,content:!0}),a.forEach(t),H=m(e),b=r(e,"H1",{class:!0});var de=f(b);y=r(de,"A",{id:!0,class:!0,href:!0});var Xe=f(y);Y=r(Xe,"SPAN",{});var Oe=f(Y);V(P.$$.fragment,Oe),Oe.forEach(t),Xe.forEach(t),ve=m(de),z=r(de,"SPAN",{});var He=f(z);$e=s(He,"Conditional image generation"),He.forEach(t),de.forEach(t),K=m(e),V(E.$$.fragment,e),ee=m(e),D=r(e,"P",{});var Ke=f(D);Pe=s(Ke,"Conditional image generation allows you to generate images from a text prompt. The text is converted into embeddings which are used to condition the model to generate an image from noise."),Ke.forEach(t),te=m(e),w=r(e,"P",{});var ge=f(w);Ee=s(ge,"The "),x=r(ge,"A",{href:!0});var et=f(x);ke=s(et,"DiffusionPipeline"),et.forEach(t),Te=s(ge," is the easiest way to use a pre-trained diffusion system for inference."),ge.forEach(t),oe=m(e),c=r(e,"P",{});var C=f(c);je=s(C,"Start by creating an instance of "),U=r(C,"A",{href:!0});var tt=f(U);Je=s(tt,"DiffusionPipeline"),tt.forEach(t),Ze=s(C," and specify which pipeline "),k=r(C,"A",{href:!0,rel:!0});var ot=f(k);Me=s(ot,"checkpoint"),ot.forEach(t),Ie=s(C," you would like to download."),C.forEach(t),ae=m(e),h=r(e,"P",{});var S=f(h);De=s(S,"In this guide, you\u2019ll use "),G=r(S,"A",{href:!0});var at=f(G);xe=s(at,"DiffusionPipeline"),at.forEach(t),Ue=s(S," for text-to-image generation with "),T=r(S,"A",{href:!0,rel:!0});var it=f(T);Q=r(it,"CODE",{});var nt=f(Q);Ge=s(nt,"runwayml/stable-diffusion-v1-5"),nt.forEach(t),it.forEach(t),Ne=s(S,":"),S.forEach(t),ie=m(e),V(j.$$.fragment,e),ne=m(e),_=r(e,"P",{});var be=f(_);We=s(be,"The "),N=r(be,"A",{href:!0});var st=f(N);qe=s(st,"DiffusionPipeline"),st.forEach(t),Ce=s(be,` downloads and caches all modeling, tokenization, and scheduling components.
Because the model consists of roughly 1.4 billion parameters, we strongly recommend running it on a GPU.
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