import{S as Iu,i as Zu,s as Hu,e as l,k as a,w as oe,t as s,M as Nu,c as r,d as e,m as d,a as o,x as ae,h as f,b as i,N as Vu,G as t,g as c,y as de,L as Gu,q as se,o as fe,B as ne,v as Cu}from"../../chunks/vendor-hf-doc-builder.js";import{I as el}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as Uu}from"../../chunks/CodeBlock-hf-doc-builder.js";function Fu(zp){let Zt,na,Ht,Wt,ll,ie,gs,rl,Os,ia,jt,ys,he,ks,Ps,ha,z,As,ce,Ss,$s,Je,Js,xs,xe,Bs,Rs,Be,Us,Is,ca,Nt,qt,ol,pe,Zs,al,Hs,pa,$t,Ns,Te,dl,Vs,Gs,Re,Cs,Fs,Ta,Ee,Ea,Ue,Ws,ua,v,sl,js,qs,ue,Ls,Ys,fl,Xs,zs,nl,Qs,Ks,il,tf,ef,ve,lf,rf,_e,of,af,va,Vt,Lt,hl,me,df,Yt,sf,cl,ff,nf,pl,hf,_a,St,cf,Tl,pf,Tf,Ie,Ef,uf,De,vf,_f,ma,Me,Da,Xt,mf,be,Df,Mf,Ma,Gt,zt,El,we,bf,ul,wf,ba,Qt,vl,_,_l,gf,Of,ml,yf,kf,Dl,Pf,Af,Ml,Sf,$f,bl,Jf,xf,wl,Bf,Rf,gl,Uf,If,E,m,Ol,Zf,Hf,yl,Nf,Vf,kl,Gf,Cf,Pl,Ff,Wf,Al,jf,qf,Sl,Lf,Yf,$l,Xf,zf,D,wa,Qf,ga,Kf,Oa,tn,ya,en,ka,ln,Pa,rn,Aa,on,M,Jl,an,dn,xl,sn,fn,Bl,nn,hn,Rl,cn,pn,Ul,Tn,En,Sa,un,$a,vn,b,Ja,_n,Il,mn,Dn,Zl,Mn,bn,Hl,wn,gn,Nl,On,yn,xa,kn,Ba,Pn,w,Ra,An,Vl,Sn,$n,Gl,Jn,xn,Cl,Bn,Rn,Fl,Un,In,Ua,Zn,Ia,Hn,g,Za,Nn,Wl,Vn,Gn,jl,Cn,Fn,ql,Wn,jn,Ll,qn,Ln,Yl,Yn,Xn,Xl,zn,Qn,O,Ha,Kn,zl,ti,ei,Ql,li,ri,Kl,oi,ai,tr,di,si,er,fi,ni,lr,ii,hi,y,Na,ci,Va,pi,Ga,Ti,Ca,Ei,Fa,ui,Wa,vi,ja,_i,k,rr,mi,Di,or,Mi,bi,ar,wi,gi,dr,Oi,yi,sr,ki,Pi,qa,Ai,La,Si,P,Ya,$i,fr,Ji,xi,nr,Bi,Ri,ir,Ui,Ii,hr,Zi,Hi,Xa,Ni,za,Vi,A,Qa,Gi,cr,Ci,Fi,pr,Wi,ji,Tr,qi,Li,Er,Yi,Xi,Ka,zi,td,Qi,S,ed,Ki,ur,t1,e1,vr,l1,r1,_r,o1,a1,mr,d1,s1,ld,f1,rd,n1,$,od,i1,Dr,h1,c1,Mr,p1,T1,br,E1,u1,wr,v1,_1,gr,m1,D1,Or,M1,ad,Ze,yr,ge,b1,kr,w1,g1,dd,Ct,Kt,Pr,Oe,O1,Ar,y1,sd,te,Sr,J,$r,k1,P1,Jr,A1,S1,xr,$1,J1,Br,x1,B1,Rr,R1,U1,Ur,I1,Z1,Ir,H1,N1,p,x,Zr,V1,G1,Hr,C1,F1,Nr,W1,j1,Vr,q1,L1,Gr,Y1,X1,fd,z1,nd,Q1,B,id,K1,Cr,th,eh,Fr,lh,rh,Wr,oh,ah,jr,dh,sh,hd,fh,cd,nh,R,pd,ih,qr,hh,ch,Lr,ph,Th,Yr,Eh,uh,Xr,vh,_h,zr,mh,Dh,Qr,Mh,bh,U,Td,wh,Kr,gh,Oh,to,yh,kh,eo,Ph,Ah,lo,Sh,$h,ro,Jh,xh,oo,Bh,Rh,I,Ed,Uh,ao,Ih,Zh,so,Hh,Nh,fo,Vh,Gh,no,Ch,Fh,io,Wh,jh,ho,qh,Lh,Z,ud,Yh,vd,Xh,_d,zh,md,Qh,Dd,Kh,Md,tc,bd,ec,H,co,lc,rc,po,oc,ac,To,dc,sc,Eo,fc,nc,uo,ic,hc,wd,cc,gd,pc,N,Od,Tc,vo,Ec,uc,_o,vc,_c,mo,mc,Dc,Do,Mc,bc,yd,wc,kd,gc,V,Pd,Oc,Mo,yc,kc,bo,Pc,Ac,wo,Sc,$c,go,Jc,xc,Ad,Bc,Sd,Rc,G,$d,Uc,Oo,Ic,Zc,yo,Hc,Nc,ko,Vc,Gc,Po,Cc,Fc,Jd,Wc,xd,jc,C,Bd,qc,Ao,Lc,Yc,So,Xc,zc,$o,Qc,Kc,Jo,t2,e2,xo,l2,r2,Bo,o2,a2,F,Rd,d2,Ud,s2,Id,f2,Zd,n2,Hd,i2,Nd,h2,Vd,c2,W,Ro,p2,T2,Uo,E2,u2,Io,v2,_2,Zo,m2,D2,Ho,M2,b2,Gd,w2,Cd,g2,j,Fd,O2,No,y2,k2,Vo,P2,A2,Go,S2,$2,Co,J2,x2,Wd,B2,jd,R2,q,qd,U2,Fo,I2,Z2,Wo,H2,N2,jo,V2,G2,qo,C2,F2,Ld,W2,Yd,j2,L,Xd,q2,Lo,L2,Y2,Yo,X2,z2,Xo,Q2,K2,zo,tp,ep,zd,lp,Qd,rp,Y,Kd,op,Qo,ap,dp,Ko,sp,fp,ta,np,ip,ea,hp,cp,ts,pp,es,ls,Jt,Tp,la,Ep,up,ra,vp,_p,rs,ee,mp,ye,oa,Dp,Mp,os,Ft,le,aa,ke,bp,da,wp,as,xt,gp,Pe,Op,yp,sa,kp,Pp,ds,Bt,Ap,Ae,Sp,$p,He,Jp,xp,ss,Rt,Ne,Bp,Ve,Rp,Up,Ge,Ce,Ip,Zp,Hp,Fe,We,Np,Vp,fs,je,Gp,ns,qe,Le,Qp,is,Ut,Cp,Se,Fp,Wp,$e,jp,qp,hs;return ie=new el({}),pe=new el({}),Ee=new Uu({props:{code:"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",highlighted:`from diffusers import StableDiffusionPipeline
import tomesd

pipeline = StableDiffusionPipeline.from_pretrained(
      &quot;runwayml/stable-diffusion-v1-5&quot;, torch_dtype=torch.float16
).to(&quot;cuda&quot;)
<span class="hljs-addition">+ tomesd.apply_patch(pipeline, ratio=0.5)</span>

image = pipeline(&quot;a photo of an astronaut riding a horse on mars&quot;).images[0]`}}),me=new el({}),Me=new Uu({props:{code:"LSUyMCU2MGRpZmZ1c2VycyU2MCUyMHZlcnNpb24lM0ElMjAwLjE1LjElMEEtJTIwUHl0aG9uJTIwdmVyc2lvbiUzQSUyMDMuOC4xNiUwQS0lMjBQeVRvcmNoJTIwdmVyc2lvbiUyMChHUFUlM0YpJTNBJTIwMS4xMy4xJTJCY3UxMTYlMjAoVHJ1ZSklMEEtJTIwSHVnZ2luZ2ZhY2VfaHViJTIwdmVyc2lvbiUzQSUyMDAuMTMuMiUwQS0lMjBUcmFuc2Zvcm1lcnMlMjB2ZXJzaW9uJTNBJTIwNC4yNy4yJTBBLSUyMEFjY2VsZXJhdGUlMjB2ZXJzaW9uJTNBJTIwMC4xOC4wJTBBLSUyMHhGb3JtZXJzJTIwdmVyc2lvbiUzQSUyMDAuMC4xNiUwQS0lMjB0b21lc2QlMjB2ZXJzaW9uJTNBJTIwMC4xLjI=",highlighted:`- \`diffusers\` version: 0.15.1
- Python version: 3.8.16
- PyTorch version (GPU?): 1.13.1+cu116 (True)
- Huggingface_hub version: 0.13.2
- Transformers version: 4.27.2
- Accelerate version: 0.18.0
- xFormers version: 0.0.16
- tomesd version: 0.1.2`}}),we=new el({}),Oe=new el({}),ke=new el({}),{c(){Zt=l("meta"),na=a(),Ht=l("h1"),Wt=l("a"),ll=l("span"),oe(ie.$$.fragment),gs=a(),rl=l("span"),Os=s("Token Merging"),ia=a(),jt=l("p"),ys=s("Token Merging (introduced in "),he=l("a"),ks=s("Token Merging: Your ViT But Faster"),Ps=s(") works by merging the redundant tokens / patches progressively in the forward pass of a Transformer-based network. It can speed up the inference latency of the underlying network."),ha=a(),z=l("p"),As=s("After Token Merging (ToMe) was released, the authors released "),ce=l("a"),Ss=s("Token Merging for Fast Stable Diffusion"),$s=s(", which introduced a version of ToMe which is more compatible with Stable Diffusion. We can use ToMe to gracefully speed up the inference latency of a "),Je=l("a"),Js=s("DiffusionPipeline"),xs=s(". This doc discusses how to apply ToMe to the "),xe=l("a"),Bs=s("StableDiffusionPipeline"),Rs=s(", the expected speedups, and the qualitative aspects of using ToMe on the "),Be=l("a"),Us=s("StableDiffusionPipeline"),Is=s("."),ca=a(),Nt=l("h2"),qt=l("a"),ol=l("span"),oe(pe.$$.fragment),Zs=a(),al=l("span"),Hs=s("Using ToMe"),pa=a(),$t=l("p"),Ns=s("The authors of ToMe released a convenient Python library called "),Te=l("a"),dl=l("code"),Vs=s("tomesd"),Gs=s(" that lets us apply ToMe to a "),Re=l("a"),Cs=s("DiffusionPipeline"),Fs=s(" like so:"),Ta=a(),oe(Ee.$$.fragment),Ea=a(),Ue=l("p"),Ws=s("And that\u2019s it!"),ua=a(),v=l("p"),sl=l("code"),js=s("tomesd.apply_patch()"),qs=s(" exposes "),ue=l("a"),Ls=s("a number of arguments"),Ys=s(" to let us strike a balance between the pipeline inference speed and the quality of the generated tokens. Amongst those arguments, the most important one is "),fl=l("code"),Xs=s("ratio"),zs=s(". "),nl=l("code"),Qs=s("ratio"),Ks=s(" controls the number of tokens that will be merged during the forward pass. For more details on "),il=l("code"),tf=s("tomesd"),ef=s(", please refer to the original repository "),ve=l("a"),lf=s("https://github.com/dbolya/tomesd"),rf=s(" and "),_e=l("a"),of=s("the paper"),af=s("."),va=a(),Vt=l("h2"),Lt=l("a"),hl=l("span"),oe(me.$$.fragment),df=a(),Yt=l("span"),sf=s("Benchmarking "),cl=l("code"),ff=s("tomesd"),nf=s(" with "),pl=l("code"),hf=s("StableDiffusionPipeline"),_a=a(),St=l("p"),cf=s("We benchmarked the impact of using "),Tl=l("code"),pf=s("tomesd"),Tf=s(" on "),Ie=l("a"),Ef=s("StableDiffusionPipeline"),uf=s(" along with "),De=l("a"),vf=s("xformers"),_f=s(" across different image resolutions. 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cu=o(Xo);Q2=f(cu,"13"),cu.forEach(e),K2=d(Pt),zo=r(Pt,"TD",{});var pu=o(zo);tp=f(pu,"10.78"),pu.forEach(e),ep=d(Pt),zd=r(Pt,"TD",{}),o(zd).forEach(e),lp=d(Pt),Qd=r(Pt,"TD",{}),o(Qd).forEach(e),Pt.forEach(e),rp=d(T),Y=r(T,"TR",{});var At=o(Y);Kd=r(At,"TD",{}),o(Kd).forEach(e),op=d(At),Qo=r(At,"TD",{});var Tu=o(Qo);ap=f(Tu,"1"),Tu.forEach(e),dp=d(At),Ko=r(At,"TD",{});var Eu=o(Ko);sp=f(Eu,"OOM"),Eu.forEach(e),fp=d(At),ta=r(At,"TD",{});var uu=o(ta);np=f(uu,"6.66"),uu.forEach(e),ip=d(At),ea=r(At,"TD",{});var vu=o(ea);hp=f(vu,"5.54"),vu.forEach(e),cp=d(At),ts=r(At,"TD",{}),o(ts).forEach(e),pp=d(At),es=r(At,"TD",{}),o(es).forEach(e),At.forEach(e),T.forEach(e),Ms.forEach(e),ls=d(n),Jt=r(n,"P",{});var Xe=o(Jt);Tp=f(Xe,"As seen in the tables above, the speedup with "),la=r(Xe,"CODE",{});var _u=o(la);Ep=f(_u,"tomesd"),_u.forEach(e),up=f(Xe," becomes more pronounced for larger image resolutions. It is also interesting to note that with "),ra=r(Xe,"CODE",{});var mu=o(ra);vp=f(mu,"tomesd"),mu.forEach(e),_p=f(Xe,", it becomes possible to run the pipeline on a higher resolution, like 1024x1024."),Xe.forEach(e),rs=d(n),ee=r(n,"P",{});var bs=o(ee);mp=f(bs,"It might be possible to speed up inference even further with "),ye=r(bs,"A",{href:!0,rel:!0});var Du=o(ye);oa=r(Du,"CODE",{});var Mu=o(oa);Dp=f(Mu,"torch.compile()"),Mu.forEach(e),Du.forEach(e),Mp=f(bs,"."),bs.forEach(e),os=d(n),Ft=r(n,"H2",{class:!0});var ws=o(Ft);le=r(ws,"A",{id:!0,class:!0,href:!0});var bu=o(le);aa=r(bu,"SPAN",{});var wu=o(aa);ae(ke.$$.fragment,wu),wu.forEach(e),bu.forEach(e),bp=d(ws),da=r(ws,"SPAN",{});var gu=o(da);wp=f(gu,"Quality"),gu.forEach(e),ws.forEach(e),as=d(n),xt=r(n,"P",{});var ze=o(xt);gp=f(ze,"As reported in "),Pe=r(ze,"A",{href:!0,rel:!0});var Ou=o(Pe);Op=f(Ou,"the paper"),Ou.forEach(e),yp=f(ze,", ToMe can preserve the quality of the generated images to a great extent while speeding up inference. By increasing the "),sa=r(ze,"CODE",{});var yu=o(sa);kp=f(yu,"ratio"),yu.forEach(e),Pp=f(ze,", it is possible to further speed up inference, but that might come at the cost of a deterioration in the image quality."),ze.forEach(e),ds=d(n),Bt=r(n,"P",{});var Qe=o(Bt);Ap=f(Qe,"To test the quality of the generated samples using our setup, we sampled a few prompts from the \u201CParti Prompts\u201D (introduced in "),Ae=r(Qe,"A",{href:!0,rel:!0});var ku=o(Ae);Sp=f(ku,"Parti"),ku.forEach(e),$p=f(Qe,") and performed inference with the "),He=r(Qe,"A",{href:!0});var Pu=o(He);Jp=f(Pu,"StableDiffusionPipeline"),Pu.forEach(e),xp=f(Qe," in the following settings:"),Qe.forEach(e),ss=d(n),Rt=r(n,"UL",{});var Ke=o(Rt);Ne=r(Ke,"LI",{});var Lp=o(Ne);Bp=f(Lp,"Vanilla "),Ve=r(Lp,"A",{href:!0});var Au=o(Ve);Rp=f(Au,"StableDiffusionPipeline"),Au.forEach(e),Lp.forEach(e),Up=d(Ke),Ge=r(Ke,"LI",{});var Yp=o(Ge);Ce=r(Yp,"A",{href:!0});var Su=o(Ce);Ip=f(Su,"StableDiffusionPipeline"),Su.forEach(e),Zp=f(Yp," + ToMe"),Yp.forEach(e),Hp=d(Ke),Fe=r(Ke,"LI",{});var Xp=o(Fe);We=r(Xp,"A",{href:!0});var $u=o(We);Np=f($u,"StableDiffusionPipeline"),$u.forEach(e),Vp=f(Xp," + ToMe + xformers"),Xp.forEach(e),Ke.forEach(e),fs=d(n),je=r(n,"P",{});var Ju=o(je);Gp=f(Ju,"We didn\u2019t notice any significant decrease in the quality of the generated samples. 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Wu={local:"token-merging",sections:[{local:"using-tome",title:"Using ToMe"},{local:"benchmarking-tomesd-with-stablediffusionpipeline",sections:[{local:"a100",title:"A100"},{local:"v100",title:"V100"}],title:"Benchmarking `tomesd` with `StableDiffusionPipeline`"},{local:"quality",title:"Quality"}],title:"Token Merging"};function ju(zp){return Cu(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Xu extends Iu{constructor(Zt){super();Zu(this,Zt,ju,Fu,Hu,{})}}export{Xu as default,Wu as metadata};
