import{S as io,i as ao,s as ro,e as a,k as f,w as b,t as l,M as po,c as r,d as t,m,a as n,x as w,h as i,b as h,N as oo,G as s,g as p,y,L as no,q as g,o as v,B as _,v as fo}from"../../chunks/vendor-hf-doc-builder.js";import{I as lo}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as ee}from"../../chunks/CodeBlock-hf-doc-builder.js";import{D as mo}from"../../chunks/DocNotebookDropdown-hf-doc-builder.js";function ho(Es){let E,Ze,J,T,he,B,wt,ce,yt,Ie,X,Ue,te,gt,Ge,d,vt,se,_t,Et,z,Jt,$t,Ve,$,M,ue,D,Tt,de,Mt,xe,c,kt,be,Zt,It,S,Ut,Gt,P,Vt,xt,we,Ct,Wt,H,ye,Nt,jt,oe,Bt,Xt,Ce,k,zt,R,Dt,St,We,Z,Pt,ge,Ht,Rt,Ne,Y,je,le,Yt,Be,ie,ae,Js,Xe,re,At,ze,I,Ft,ve,Qt,qt,De,A,Se,U,Ot,_e,Lt,Kt,Pe,F,He,G,es,Ee,ts,ss,Re,Q,Ye,V,os,Je,ls,is,Ae,q,Fe,x,as,$e,rs,ps,Qe,O,qe,pe,ns,Oe,ne,fe,$s,Le,C,fs,Te,ms,hs,Ke,W,cs,Me,us,ds,et,N,bs,ke,ws,ys,tt,L,st,j,gs,K,vs,_s,ot;return B=new lo({}),X=new mo({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/weighted_prompts.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/pytorch/weighted_prompts.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/tensorflow/weighted_prompts.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/weighted_prompts.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/pytorch/weighted_prompts.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/tensorflow/weighted_prompts.ipynb"}]}}),D=new lo({}),Y=new ee({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline, UniPCMultistepScheduler

pipe = StableDiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)

prompt = <span class="hljs-string">&quot;a red cat playing with a ball&quot;</span>

generator = torch.Generator(device=<span class="hljs-string">&quot;cpu&quot;</span>).manual_seed(<span class="hljs-number">33</span>)

image = pipe(prompt, generator=generator, num_inference_steps=<span class="hljs-number">20</span>).images[<span class="hljs-number">0</span>]
image`}}),A=new ee({props:{code:"cGlwJTIwaW5zdGFsbCUyMGNvbXBlbA==",highlighted:'pip <span class="hljs-keyword">install</span> compel'}}),F=new ee({props:{code:"ZnJvbSUyMGNvbXBlbCUyMGltcG9ydCUyMENvbXBlbCUwQSUwQWNvbXBlbF9wcm9jJTIwJTNEJTIwQ29tcGVsKHRva2VuaXplciUzRHBpcGUudG9rZW5pemVyJTJDJTIwdGV4dF9lbmNvZGVyJTNEcGlwZS50ZXh0X2VuY29kZXIp",highlighted:`<span class="hljs-keyword">from</span> compel <span class="hljs-keyword">import</span> Compel

compel_proc = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder)`}}),Q=new ee({props:{code:"cHJvbXB0JTIwJTNEJTIwJTIyYSUyMHJlZCUyMGNhdCUyMHBsYXlpbmclMjB3aXRoJTIwYSUyMGJhbGwlMkIlMkIlMjI=",highlighted:'prompt = <span class="hljs-string">&quot;a red cat playing with a ball++&quot;</span>'}}),q=new ee({props:{code:"cHJvbXB0X2VtYmVkcyUyMCUzRCUyMGNvbXBlbF9wcm9jKHByb21wdCk=",highlighted:"prompt_embeds = compel_proc(prompt)"}}),O=new ee({props:{code:"Z2VuZXJhdG9yJTIwJTNEJTIwdG9yY2guR2VuZXJhdG9yKGRldmljZSUzRCUyMmNwdSUyMikubWFudWFsX3NlZWQoMzMpJTBBJTBBaW1hZ2VzJTIwJTNEJTIwcGlwZShwcm9tcHRfZW1iZWRzJTNEcHJvbXB0X2VtYmVkcyUyQyUyMGdlbmVyYXRvciUzRGdlbmVyYXRvciUyQyUyMG51bV9pbmZlcmVuY2Vfc3RlcHMlM0QyMCkuaW1hZ2VzJTVCMCU1RCUwQWltYWdl",highlighted:`generator = torch.Generator(device=<span class="hljs-string">&quot;cpu&quot;</span>).manual_seed(<span class="hljs-number">33</span>)

images = pipe(prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=<span class="hljs-number">20</span>).images[<span class="hljs-number">0</span>]
image`}}),L=new ee({props:{code:"dGV4dHVhbF9pbnZlcnNpb25fbWFuYWdlciUyMCUzRCUyMERpZmZ1c2Vyc1RleHR1YWxJbnZlcnNpb25NYW5hZ2VyKHBpcGUpJTBBY29tcGVsJTIwJTNEJTIwQ29tcGVsKCUwQSUyMCUyMCUyMCUyMHRva2VuaXplciUzRHBpcGUudG9rZW5pemVyJTJDJTBBJTIwJTIwJTIwJTIwdGV4dF9lbmNvZGVyJTNEcGlwZS50ZXh0X2VuY29kZXIlMkMlMEElMjAlMjAlMjAlMjB0ZXh0dWFsX2ludmVyc2lvbl9tYW5hZ2VyJTNEdGV4dHVhbF9pbnZlcnNpb25fbWFuYWdlcik=",highlighted:`<span class="hljs-attr">textual_inversion_manager</span> = DiffusersTextualInversionManager(pipe)
<span class="hljs-attr">compel</span> = Compel(
    <span class="hljs-attr">tokenizer</span>=pipe.tokenizer,
    <span class="hljs-attr">text_encoder</span>=pipe.text_encoder,
    <span class="hljs-attr">textual_inversion_manager</span>=textual_inversion_manager)`}}),{c(){E=a("meta"),Ze=f(),J=a("h1"),T=a("a"),he=a("span"),b(B.$$.fragment),wt=f(),ce=a("span"),yt=l("Weighting prompts"),Ie=f(),b(X.$$.fragment),Ue=f(),te=a("p"),gt=l(`Text-guided diffusion models generate images based on a given text prompt. The text prompt
can include multiple concepts that the model should generate and it\u2019s often desirable to weight
certain parts of the prompt more or less.`),Ge=f(),d=a("p"),vt=l("Diffusion models work by conditioning the cross attention layers of the diffusion model with contextualized text embeddings (see the "),se=a("a"),_t=l("Stable Diffusion Guide for more information"),Et=l(`).
Thus a simple way to emphasize (or de-emphasize) certain parts of the prompt is by increasing or reducing the scale of the text embedding vector that corresponds to the relevant part of the prompt.
This is called \u201Cprompt-weighting\u201D and has been a highly demanded feature by the community (see issue `),z=a("a"),Jt=l("here"),$t=l(")."),Ve=f(),$=a("h2"),M=a("a"),ue=a("span"),b(D.$$.fragment),Tt=f(),de=a("span"),Mt=l("How to do prompt-weighting in Diffusers"),xe=f(),c=a("p"),kt=l("We believe the role of "),be=a("code"),Zt=l("diffusers"),It=l(" is to be a toolbox that provides essential features that enable other projects, such as "),S=a("a"),Ut=l("InvokeAI"),Gt=l(" or "),P=a("a"),Vt=l("diffuzers"),xt=l(", to build powerful UIs. In order to support arbitrary methods to manipulate prompts, "),we=a("code"),Ct=l("diffusers"),Wt=l(" exposes a "),H=a("a"),ye=a("code"),Nt=l("prompt_embeds"),jt=l(" function argument to many pipelines such as "),oe=a("a"),Bt=l("StableDiffusionPipeline"),Xt=l(", allowing to directly pass the \u201Cprompt-weighted\u201D/scaled text embeddings to the pipeline."),Ce=f(),k=a("p"),zt=l("The "),R=a("a"),Dt=l("compel library"),St=l(" provides an easy way to emphasize or de-emphasize portions of the prompt for you. We strongly recommend it instead of preparing the embeddings yourself."),We=f(),Z=a("p"),Pt=l("Let\u2019s look at a simple example. Imagine you want to generate an image of "),ge=a("code"),Ht=l('"a red cat playing with a ball"'),Rt=l(` as
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a try!`),Ke=f(),W=a("p"),cs=l("If your favorite pipeline does not have a "),Me=a("code"),us=l("prompt_embeds"),ds=l(` input, please make sure to open an issue, the
diffusers team tries to be as responsive as possible.`),et=f(),N=a("p"),bs=l("Compel 1.1.6 adds a utility class to simplify using textual inversions.  Instantiate a "),ke=a("code"),ws=l("DiffusersTextualInversionManager"),ys=l(" and pass it to Compel init:"),tt=f(),b(L.$$.fragment),st=f(),j=a("p"),gs=l("Also, please check out the documentation of the "),K=a("a"),vs=l("compel"),_s=l(` library for
more information.`),this.h()},l(e){const o=po('[data-svelte="svelte-1phssyn"]',document.head);E=r(o,"META",{name:!0,content:!0}),o.forEach(t),Ze=m(e),J=r(e,"H1",{class:!0});var lt=n(J);T=r(lt,"A",{id:!0,class:!0,href:!0});var Ts=n(T);he=r(Ts,"SPAN",{});var Ms=n(he);w(B.$$.fragment,Ms),Ms.forEach(t),Ts.forEach(t),wt=m(lt),ce=r(lt,"SPAN",{});var ks=n(ce);yt=i(ks,"Weighting prompts"),ks.forEach(t),lt.forEach(t),Ie=m(e),w(X.$$.fragment,e),Ue=m(e),te=r(e,"P",{});var Zs=n(te);gt=i(Zs,`Text-guided diffusion models generate images based on a given text prompt. The text prompt
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certain parts of the prompt more or less.`),Zs.forEach(t),Ge=m(e),d=r(e,"P",{});var me=n(d);vt=i(me,"Diffusion models work by conditioning the cross attention layers of the diffusion model with contextualized text embeddings (see the "),se=r(me,"A",{href:!0});var Is=n(se);_t=i(Is,"Stable Diffusion Guide for more information"),Is.forEach(t),Et=i(me,`).
Thus a simple way to emphasize (or de-emphasize) certain parts of the prompt is by increasing or reducing the scale of the text embedding vector that corresponds to the relevant part of the prompt.
This is called \u201Cprompt-weighting\u201D and has been a highly demanded feature by the community (see issue `),z=r(me,"A",{href:!0,rel:!0});var Us=n(z);Jt=i(Us,"here"),Us.forEach(t),$t=i(me,")."),me.forEach(t),Ve=m(e),$=r(e,"H2",{class:!0});var it=n($);M=r(it,"A",{id:!0,class:!0,href:!0});var Gs=n(M);ue=r(Gs,"SPAN",{});var Vs=n(ue);w(D.$$.fragment,Vs),Vs.forEach(t),Gs.forEach(t),Tt=m(it),de=r(it,"SPAN",{});var xs=n(de);Mt=i(xs,"How to do prompt-weighting in Diffusers"),xs.forEach(t),it.forEach(t),xe=m(e),c=r(e,"P",{});var u=n(c);kt=i(u,"We believe the role of "),be=r(u,"CODE",{});var Cs=n(be);Zt=i(Cs,"diffusers"),Cs.forEach(t),It=i(u," is to be a toolbox that provides essential features that enable other projects, such as "),S=r(u,"A",{href:!0,rel:!0});var Ws=n(S);Ut=i(Ws,"InvokeAI"),Ws.forEach(t),Gt=i(u," or "),P=r(u,"A",{href:!0,rel:!0});var Ns=n(P);Vt=i(Ns,"diffuzers"),Ns.forEach(t),xt=i(u,", to build powerful UIs. In order to support arbitrary methods to manipulate prompts, "),we=r(u,"CODE",{});var js=n(we);Ct=i(js,"diffusers"),js.forEach(t),Wt=i(u," exposes a "),H=r(u,"A",{href:!0,rel:!0});var Bs=n(H);ye=r(Bs,"CODE",{});var Xs=n(ye);Nt=i(Xs,"prompt_embeds"),Xs.forEach(t),Bs.forEach(t),jt=i(u," function argument to many pipelines such as "),oe=r(u,"A",{href:!0});var zs=n(oe);Bt=i(zs,"StableDiffusionPipeline"),zs.forEach(t),Xt=i(u,", allowing to directly pass the \u201Cprompt-weighted\u201D/scaled text embeddings to the pipeline."),u.forEach(t),Ce=m(e),k=r(e,"P",{});var at=n(k);zt=i(at,"The "),R=r(at,"A",{href:!0,rel:!0});var Ds=n(R);Dt=i(Ds,"compel library"),Ds.forEach(t),St=i(at," provides an easy way to emphasize or de-emphasize portions of the prompt for you. We strongly recommend it instead of preparing the embeddings yourself."),at.forEach(t),We=m(e),Z=r(e,"P",{});var rt=n(Z);Pt=i(rt,"Let\u2019s look at a simple example. Imagine you want to generate an image of "),ge=r(rt,"CODE",{});var Ss=n(ge);Ht=i(Ss,'"a red cat playing with a ball"'),Ss.forEach(t),Rt=i(rt,` as
follows:`),rt.forEach(t),Ne=m(e),w(Y.$$.fragment,e),je=m(e),le=r(e,"P",{});var Ps=n(le);Yt=i(Ps,"This gives you:"),Ps.forEach(t),Be=m(e),ie=r(e,"P",{});var Hs=n(ie);ae=r(Hs,"IMG",{src:!0,alt:!0}),Hs.forEach(t),Xe=m(e),re=r(e,"P",{});var Rs=n(re);At=i(Rs,"As you can see, there is no \u201Cball\u201D in the image. Let\u2019s emphasize this part!"),Rs.forEach(t),ze=m(e),I=r(e,"P",{});var pt=n(I);Ft=i(pt,"For this we should install the "),ve=r(pt,"CODE",{});var Ys=n(ve);Qt=i(Ys,"compel"),Ys.forEach(t),qt=i(pt," library:"),pt.forEach(t),De=m(e),w(A.$$.fragment,e),Se=m(e),U=r(e,"P",{});var nt=n(U);Ot=i(nt,"and then create a "),_e=r(nt,"CODE",{});var As=n(_e);Lt=i(As,"Compel"),As.forEach(t),Kt=i(nt," object:"),nt.forEach(t),Pe=m(e),w(F.$$.fragment,e),He=m(e),G=r(e,"P",{});var ft=n(G);es=i(ft,"Now we emphasize the part \u201Cball\u201D with the "),Ee=r(ft,"CODE",{});var Fs=n(Ee);ts=i(Fs,'"++"'),Fs.forEach(t),ss=i(ft," syntax:"),ft.forEach(t),Re=m(e),w(Q.$$.fragment,e),Ye=m(e),V=r(e,"P",{});var mt=n(V);os=i(mt,"and instead of passing this to the pipeline directly, we have to process it using "),Je=r(mt,"CODE",{});var Qs=n(Je);ls=i(Qs,"compel_proc"),Qs.forEach(t),is=i(mt,":"),mt.forEach(t),Ae=m(e),w(q.$$.fragment,e),Fe=m(e),x=r(e,"P",{});var ht=n(x);as=i(ht,"Now we can pass "),$e=r(ht,"CODE",{});var qs=n($e);rs=i(qs,"prompt_embeds"),qs.forEach(t),ps=i(ht," directly to the pipeline:"),ht.forEach(t),Qe=m(e),w(O.$$.fragment,e),qe=m(e),pe=r(e,"P",{});var Os=n(pe);ns=i(Os,"We now get the following image which has a \u201Cball\u201D!"),Os.forEach(t),Oe=m(e),ne=r(e,"P",{});var Ls=n(ne);fe=r(Ls,"IMG",{src:!0,alt:!0}),Ls.forEach(t),Le=m(e),C=r(e,"P",{});var ct=n(C);fs=i(ct,"Similarly, we de-emphasize parts of the sentence by using the "),Te=r(ct,"CODE",{});var Ks=n(Te);ms=i(Ks,"--"),Ks.forEach(t),hs=i(ct,` suffix for words, feel free to give it
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