import{S as My,i as Cy,s as Ny,e as a,k as s,w as k,t as n,M as Ly,c as o,d as t,m as d,a as r,x as P,h as i,b as l,N as Se,G as e,g as p,y as R,L as jy,q as O,o as $,B as M,v as qy}from"../../../chunks/vendor-hf-doc-builder.js";import{I as Qe}from"../../../chunks/IconCopyLink-hf-doc-builder.js";import{C as iE}from"../../../chunks/CodeBlock-hf-doc-builder.js";function Vy(sE){let $e,Of,Me,We,fn,ra,$h,hn,Mh,$f,Bo,Ch,Mf,Ze,Nh,la,Lh,jh,Cf,E,cn,Uo,qh,Vh,un,Fo,Hh,Bh,pn,na,Uh,Fh,ia,zh,zo,Yh,Qh,Wh,sa,Zh,da,Jh,Kh,Xh,fa,ec,Yo,tc,ac,Nf,Qo,oc,Lf,I,gn,mn,C,rc,_n,lc,nc,ha,ic,sc,ca,dc,fc,hc,vn,Wo,ua,cc,Zo,uc,pc,gc,En,Jo,pa,mc,Ko,_c,vc,Ec,bn,Xo,ga,bc,er,Tc,yc,jf,N,Tn,Dc,wc,yn,Ic,xc,ma,Ac,Gc,qf,Ce,Je,Dn,_a,Sc,wn,kc,Vf,tr,Pc,Hf,Ke,In,L,xn,Rc,Oc,An,$c,Mc,ar,Cc,Nc,or,Lc,jc,h,j,Gn,rr,qc,Vc,Sn,va,kn,Hc,Bc,lr,Uc,Fc,nr,zc,Yc,q,Pn,ir,Qc,Wc,Rn,Ea,On,Zc,Jc,sr,Kc,Xc,$n,eu,V,Mn,dr,tu,au,Cn,ba,Nn,ou,ru,fr,lu,nu,hr,Ta,cr,dE,iu,H,Ln,ur,su,du,jn,ya,qn,fu,hu,pr,cu,uu,Vn,pu,B,Hn,gr,gu,mu,Bn,Da,Un,_u,vu,mr,Eu,bu,Fn,Tu,U,zn,_r,yu,Du,Yn,wa,Qn,wu,Iu,vr,xu,Au,Wn,Gu,F,Zn,Er,Su,ku,Jn,Ia,Kn,Pu,Ru,br,Ou,$u,Xn,Mu,z,ei,Tr,Cu,Nu,ti,xa,ai,Lu,ju,yr,qu,Vu,Dr,Aa,wr,fE,Hu,Y,oi,Ir,Bu,Uu,ri,Ga,li,Fu,zu,xr,Yu,Qu,Ar,Sa,Gr,hE,Wu,Q,ni,Sr,Zu,Ju,ii,ka,si,Ku,Xu,kr,ep,tp,Pr,Pa,Rr,cE,ap,W,di,Or,op,rp,fi,Ra,hi,lp,np,$r,ip,sp,ci,dp,Z,ui,Mr,fp,hp,pi,Oa,gi,cp,up,Cr,pp,gp,mi,mp,J,_i,Nr,_p,vp,vi,$a,Ei,Ep,bp,Lr,Tp,yp,bi,Dp,K,Ti,jr,wp,Ip,yi,Ma,Di,xp,Ap,qr,Gp,Sp,wi,kp,X,Ii,Vr,Pp,Rp,xi,Ca,Ai,Op,$p,Hr,Mp,Cp,Gi,Np,ee,Si,Br,Lp,jp,ki,Na,Pi,qp,Vp,Ur,Hp,Bp,Ri,Up,te,Oi,Fr,Fp,zp,$i,La,Mi,Yp,Qp,zr,Wp,Zp,Ci,Jp,ae,Ni,Yr,Kp,Xp,Li,ja,ji,eg,tg,Qr,ag,og,qi,rg,oe,Vi,Wr,lg,ng,Hi,qa,Bi,ig,sg,Zr,dg,fg,Jr,Va,Kr,uE,hg,re,Ui,Xr,cg,ug,Fi,Ha,zi,pg,gg,el,mg,_g,tl,Ba,al,pE,vg,le,Yi,ol,Eg,bg,Qi,Ua,Wi,Tg,yg,rl,Dg,wg,ll,Fa,nl,gE,Ig,ne,Zi,il,xg,Ag,Ji,za,Ki,Gg,Sg,sl,kg,Pg,Xi,Rg,ie,es,dl,Og,$g,ts,Ya,as,Mg,Cg,fl,Ng,Lg,os,jg,se,rs,hl,qg,Vg,ls,Qa,ns,Hg,Bg,cl,Ug,Fg,is,zg,de,ss,ul,Yg,Qg,ds,Wa,fs,Wg,Zg,pl,Jg,Kg,hs,Xg,fe,cs,gl,em,tm,us,Za,ps,am,om,ml,rm,lm,gs,nm,he,ms,_l,im,sm,_s,Ja,vs,dm,fm,vl,hm,cm,Es,um,ce,bs,El,pm,gm,Ts,Ka,ys,mm,_m,bl,vm,Em,Ds,bm,ue,ws,Tl,Tm,ym,Is,Xa,xs,Dm,wm,yl,Im,xm,As,Am,pe,Gs,Dl,Gm,Sm,Ss,eo,ks,km,Pm,wl,Rm,Om,Ps,$m,ge,Rs,Il,Mm,Cm,Os,to,$s,Nm,Lm,xl,jm,qm,Ms,Vm,me,Cs,Al,Hm,Bm,Ns,ao,Ls,Um,Fm,Gl,zm,Ym,js,Qm,_e,qs,Sl,Wm,Zm,Vs,oo,Hs,Jm,Km,kl,Xm,e_,Pl,ro,Rl,mE,t_,ve,Bs,Ol,a_,o_,Us,Fs,r_,l_,$l,n_,i_,zs,s_,Ee,Ys,Ml,d_,f_,Qs,Ws,h_,c_,Cl,u_,p_,Zs,g_,be,Js,Nl,m_,__,Ks,lo,Xs,v_,E_,Ll,b_,T_,ed,y_,Te,td,jl,D_,w_,ad,no,I_,x_,ql,A_,G_,od,S_,ye,rd,Vl,k_,P_,ld,io,R_,O_,Hl,$_,M_,nd,C_,De,id,Bl,N_,L_,sd,so,j_,q_,Ul,V_,H_,dd,B_,we,fd,Fl,U_,F_,hd,fo,z_,Y_,zl,Q_,W_,cd,Z_,Ie,ud,Yl,J_,K_,pd,ho,X_,e1,Ql,t1,a1,gd,o1,xe,md,Wl,r1,l1,_d,co,n1,i1,Zl,s1,d1,vd,f1,Ae,Ed,Jl,h1,c1,bd,uo,u1,p1,Kl,g1,m1,Td,Bf,po,yd,_1,v1,Uf,Xe,E1,Xl,b1,T1,Ff,Ne,et,Dd,go,y1,wd,D1,zf,en,w1,Yf,ke,I1,Id,x1,A1,mo,G1,S1,Qf,x,g,_o,xd,k1,P1,R1,Ad,O1,$1,vo,M1,C1,Gd,N1,L1,Sd,j1,q1,kd,V1,H1,Eo,B1,U1,Pd,F1,z1,Rd,Y1,Q1,Od,W1,Z1,$d,J1,K1,X1,_,tn,Md,ev,tv,Cd,av,ov,Nd,rv,lv,Ld,nv,iv,jd,sv,dv,qd,fv,hv,Vd,cv,uv,Hd,pv,gv,mv,T,an,Bd,_v,vv,Ud,Ev,bv,Fd,Tv,yv,zd,Dv,wv,Le,Iv,Yd,xv,Av,Gv,Sv,b,Qd,kv,Pv,Wd,Rv,Ov,Zd,$v,Mv,Jd,Cv,Nv,on,Kd,Lv,jv,bo,qv,Vv,Wf,D,Xd,Hv,Bv,ef,Uv,Fv,To,tf,zv,Yv,yo,Qv,Wv,Zf,je,tt,af,Do,Zv,of,Jv,Jf,y,Kv,rf,Xv,e2,lf,t2,a2,nf,o2,r2,sf,l2,n2,Kf,A,at,df,i2,s2,wo,ff,d2,f2,h2,c2,Pe,hf,u2,p2,cf,g2,m2,uf,_2,v2,E2,Re,pf,b2,T2,Io,y2,D2,rn,w2,I2,x2,G,gf,A2,G2,mf,S2,k2,_f,P2,R2,vf,O2,$2,Xf,qe,ot,Ef,xo,M2,bf,C2,eh,Ve,rt,Tf,Ao,N2,yf,L2,th,Go,ah,He,lt,Df,So,j2,wf,q2,oh,nt,V2,If,H2,B2,rh,ko,lh,Po,U2,Ro,ln,_E,nh,Be,it,xf,Oo,F2,Af,z2,ih,Ue,Y2,$o,Q2,W2,Mo,nn,vE,sh,Fe,st,Gf,Co,Z2,Sf,J2,dh,dt,K2,kf,X2,eE,fh,No,hh,Lo,tE,jo,sn,EE,ch;return ra=new Qe({}),_a=new Qe({}),go=new Qe({}),Do=new Qe({}),xo=new Qe({}),Ao=new Qe({}),Go=new iE({props:{code:`# make sure you're logged in with \`huggingface-cli login\`
from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler

pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipe = pipe.to("cuda")

prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]

image.save("astronaut_rides_horse.png")`,highlighted:`<span class="hljs-comment"># make sure you&#x27;re logged in with \`huggingface-cli login\`</span>
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline, LMSDiscreteScheduler

pipe = StableDiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>)
pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;a photo of an astronaut riding a horse on mars&quot;</span>
image = pipe(prompt).images[<span class="hljs-number">0</span>]

image.save(<span class="hljs-string">&quot;astronaut_rides_horse.png&quot;</span>)`}}),So=new Qe({}),ko=new iE({props:{code:`import requests
from PIL import Image
from io import BytesIO

from diffusers import StableDiffusionImg2ImgPipeline

# load the pipeline
device = "cuda"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to(
    device
)

# let's download an initial image
url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"

response = requests.get(url)
init_image = Image.open(BytesIO(response.content)).convert("RGB")
init_image = init_image.resize((768, 512))

prompt = "A fantasy landscape, trending on artstation"

images = pipe(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images

images[0].save("fantasy_landscape.png")`,highlighted:`<span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO

<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionImg2ImgPipeline

<span class="hljs-comment"># load the pipeline</span>
device = <span class="hljs-string">&quot;cuda&quot;</span>
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(<span class="hljs-string">&quot;runwayml/stable-diffusion-v1-5&quot;</span>, torch_dtype=torch.float16).to(
    device
)

<span class="hljs-comment"># let&#x27;s download an initial image</span>
url = <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg&quot;</span>

response = requests.get(url)
init_image = Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)
init_image = init_image.resize((<span class="hljs-number">768</span>, <span class="hljs-number">512</span>))

prompt = <span class="hljs-string">&quot;A fantasy landscape, trending on artstation&quot;</span>

images = pipe(prompt=prompt, image=init_image, strength=<span class="hljs-number">0.75</span>, guidance_scale=<span class="hljs-number">7.5</span>).images

images[<span class="hljs-number">0</span>].save(<span class="hljs-string">&quot;fantasy_landscape.png&quot;</span>)`}}),Oo=new Qe({}),Co=new Qe({}),No=new iE({props:{code:`import PIL
import requests
import torch
from io import BytesIO

from diffusers import StableDiffusionInpaintPipeline


def download_image(url):
    response = requests.get(url)
    return PIL.Image.open(BytesIO(response.content)).convert("RGB")


img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"

init_image = download_image(img_url).resize((512, 512))
mask_image = download_image(mask_url).resize((512, 512))

pipe = StableDiffusionInpaintPipeline.from_pretrained(
    "runwayml/stable-diffusion-inpainting",
    torch_dtype=torch.float16,
)
pipe = pipe.to("cuda")

prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]`,highlighted:`<span class="hljs-keyword">import</span> PIL
<span class="hljs-keyword">import</span> requests
<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO

<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionInpaintPipeline


<span class="hljs-keyword">def</span> <span class="hljs-title function_">download_image</span>(<span class="hljs-params">url</span>):
    response = requests.get(url)
    <span class="hljs-keyword">return</span> PIL.Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)


img_url = <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png&quot;</span>
mask_url = <span class="hljs-string">&quot;https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png&quot;</span>

init_image = download_image(img_url).resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))
mask_image = download_image(mask_url).resize((<span class="hljs-number">512</span>, <span class="hljs-number">512</span>))

pipe = StableDiffusionInpaintPipeline.from_pretrained(
    <span class="hljs-string">&quot;runwayml/stable-diffusion-inpainting&quot;</span>,
    torch_dtype=torch.float16,
)
pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

prompt = <span class="hljs-string">&quot;Face of a yellow cat, high resolution, sitting on a park bench&quot;</span>
image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[<span class="hljs-number">0</span>]`}}),{c(){$e=a("meta"),Of=s(),Me=a("h1"),We=a("a"),fn=a("span"),k(ra.$$.fragment),$h=s(),hn=a("span"),Mh=n("Pipelines"),$f=s(),Bo=a("p"),Ch=n(`Pipelines provide a simple way to run state-of-the-art diffusion models in inference.
Most diffusion systems consist of multiple independently-trained models and highly adaptable scheduler
components - all of which are needed to have a functioning end-to-end diffusion system.`),Mf=s(),Ze=a("p"),Nh=n("As an example, "),la=a("a"),Lh=n("Stable Diffusion"),jh=n(" has three independently trained models:"),Cf=s(),E=a("ul"),cn=a("li"),Uo=a("a"),qh=n("Autoencoder"),Vh=s(),un=a("li"),Fo=a("a"),Hh=n("Conditional Unet"),Bh=s(),pn=a("li"),na=a("a"),Uh=n("CLIP text encoder"),Fh=s(),ia=a("li"),zh=n("a scheduler component, "),zo=a("a"),Yh=n("scheduler"),Qh=n(","),Wh=s(),sa=a("li"),Zh=n("a "),da=a("a"),Jh=n("CLIPImageProcessor"),Kh=n(","),Xh=s(),fa=a("li"),ec=n("as well as a "),Yo=a("a"),tc=n("safety checker"),ac=n(`.
All of these components are necessary to run stable diffusion in inference even though they were trained
or created independently from each other.`),Nf=s(),Qo=a("p"),oc=n(`To that end, we strive to offer all open-sourced, state-of-the-art diffusion system under a unified API.
More specifically, we strive to provide pipelines that`),Lf=s(),I=a("ul"),gn=a("li"),mn=a("ol"),C=a("li"),rc=n("can load the officially published weights and yield 1-to-1 the same outputs as the original implementation according to the corresponding paper ("),_n=a("em"),lc=n("e.g."),nc=s(),ha=a("a"),ic=n("LDMTextToImagePipeline"),sc=n(", uses the officially released weights of "),ca=a("a"),dc=n("High-Resolution Image Synthesis with Latent Diffusion Models"),fc=n("),"),hc=s(),vn=a("li"),Wo=a("ol"),ua=a("li"),cc=n("have a simple user interface to run the model in inference (see the "),Zo=a("a"),uc=n("Pipelines API"),pc=n(" section),"),gc=s(),En=a("li"),Jo=a("ol"),pa=a("li"),mc=n("are easy to understand with code that is self-explanatory and can be read along-side the official paper (see "),Ko=a("a"),_c=n("Pipelines summary"),vc=n("),"),Ec=s(),bn=a("li"),Xo=a("ol"),ga=a("li"),bc=n("can easily be contributed by the community (see the "),er=a("a"),Tc=n("Contribution"),yc=n(" section)."),jf=s(),N=a("p"),Tn=a("strong"),Dc=n("Note"),wc=n(` that pipelines do not (and should not) offer any training functionality.
If you are looking for `),yn=a("em"),Ic=n("official"),xc=n(" training examples, please have a look at "),ma=a("a"),Ac=n("examples"),Gc=n("."),qf=s(),Ce=a("h2"),Je=a("a"),Dn=a("span"),k(_a.$$.fragment),Sc=s(),wn=a("span"),kc=n("\u{1F9E8} Diffusers Summary"),Vf=s(),tr=a("p"),Pc=n(`The following table summarizes all officially supported pipelines, their corresponding paper, and if
available a colab notebook to directly try them out.`),Hf=s(),Ke=a("table"),In=a("thead"),L=a("tr"),xn=a("th"),Rc=n("Pipeline"),Oc=s(),An=a("th"),$c=n("Paper"),Mc=s(),ar=a("th"),Cc=n("Tasks"),Nc=s(),or=a("th"),Lc=n("Colab"),jc=s(),h=a("tbody"),j=a("tr"),Gn=a("td"),rr=a("a"),qc=n("alt_diffusion"),Vc=s(),Sn=a("td"),va=a("a"),kn=a("strong"),Hc=n("AltDiffusion"),Bc=s(),lr=a("td"),Uc=n("Image-to-Image Text-Guided Generation"),Fc=s(),nr=a("td"),zc=n("-"),Yc=s(),q=a("tr"),Pn=a("td"),ir=a("a"),Qc=n("audio_diffusion"),Wc=s(),Rn=a("td"),Ea=a("a"),On=a("strong"),Zc=n("Audio Diffusion"),Jc=s(),sr=a("td"),Kc=n("Unconditional Audio Generation"),Xc=s(),$n=a("td"),eu=s(),V=a("tr"),Mn=a("td"),dr=a("a"),tu=n("controlnet"),au=s(),Cn=a("td"),ba=a("a"),Nn=a("strong"),ou=n("ControlNet with Stable Diffusion"),ru=s(),fr=a("td"),lu=n("Image-to-Image Text-Guided 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Generation"),$u=s(),Xn=a("td"),Mu=s(),z=a("tr"),ei=a("td"),Tr=a("a"),Cu=n("if"),Nu=s(),ti=a("td"),xa=a("a"),ai=a("strong"),Lu=n("IF"),ju=s(),yr=a("td"),qu=n("Image Generation"),Vu=s(),Dr=a("td"),Aa=a("a"),wr=a("img"),Hu=s(),Y=a("tr"),oi=a("td"),Ir=a("a"),Bu=n("if_img2img"),Uu=s(),ri=a("td"),Ga=a("a"),li=a("strong"),Fu=n("IF"),zu=s(),xr=a("td"),Yu=n("Image-to-Image Generation"),Qu=s(),Ar=a("td"),Sa=a("a"),Gr=a("img"),Wu=s(),Q=a("tr"),ni=a("td"),Sr=a("a"),Zu=n("if_inpainting"),Ju=s(),ii=a("td"),ka=a("a"),si=a("strong"),Ku=n("IF"),Xu=s(),kr=a("td"),ep=n("Image-to-Image Generation"),tp=s(),Pr=a("td"),Pa=a("a"),Rr=a("img"),ap=s(),W=a("tr"),di=a("td"),Or=a("a"),op=n("latent_diffusion"),rp=s(),fi=a("td"),Ra=a("a"),hi=a("strong"),lp=n("High-Resolution Image Synthesis with Latent Diffusion Models"),np=s(),$r=a("td"),ip=n("Text-to-Image Generation"),sp=s(),ci=a("td"),dp=s(),Z=a("tr"),ui=a("td"),Mr=a("a"),fp=n("latent_diffusion"),hp=s(),pi=a("td"),Oa=a("a"),gi=a("strong"),cp=n("High-Resolution 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papers."),Uf=s(),Xe=a("p"),E1=n("However, most of them can be adapted to use different scheduler components or even different model components. Some pipeline examples are shown in the "),Xl=a("a"),b1=n("Examples"),T1=n(" below."),Ff=s(),Ne=a("h2"),et=a("a"),Dd=a("span"),k(go.$$.fragment),y1=s(),wd=a("span"),D1=n("Pipelines API"),zf=s(),en=a("p"),w1=n("Diffusion models often consist of multiple independently-trained models or other previously existing components."),Yf=s(),ke=a("p"),I1=n(`Each model has been trained independently on a different task and the scheduler can easily be swapped out and replaced with a different one.
During inference, we however want to be able to easily load all components and use them in inference - even if one component, `),Id=a("em"),x1=n("e.g."),A1=n(" CLIP\u2019s text encoder, originates from a different library, such as "),mo=a("a"),G1=n("Transformers"),S1=n(". To that end, all pipelines provide the following functionality:"),Qf=s(),x=a("ul"),g=a("li"),_o=a("a"),xd=a("code"),k1=n("from_pretrained"),P1=n(" method"),R1=n(" that accepts a Hugging Face Hub repository id, "),Ad=a("em"),O1=n("e.g."),$1=s(),vo=a("a"),M1=n("runwayml/stable-diffusion-v1-5"),C1=n(" or a path to a local directory, "),Gd=a("em"),N1=n("e.g."),L1=n(`
\u201D./stable-diffusion\u201D. To correctly retrieve which models and components should be loaded, one has to provide a `),Sd=a("code"),j1=n("model_index.json"),q1=n(" file, "),kd=a("em"),V1=n("e.g."),H1=s(),Eo=a("a"),B1=n("runwayml/stable-diffusion-v1-5/model_index.json"),U1=n(`, which defines all components that should be
loaded into the pipelines. More specifically, for each model/component one needs to define the format `),Pd=a("code"),F1=n('<name>: ["<library>", "<class name>"]'),z1=n(". "),Rd=a("code"),Y1=n("<name>"),Q1=n(" is the attribute name given to the loaded instance of "),Od=a("code"),W1=n("<class name>"),Z1=n(" which can be found in the library or pipeline folder called "),$d=a("code"),J1=n('"<library>"'),K1=n("."),X1=s(),_=a("li"),tn=a("a"),Md=a("code"),ev=n("save_pretrained"),tv=n(" that accepts a local path, "),Cd=a("em"),av=n("e.g."),ov=s(),Nd=a("code"),rv=n("./stable-diffusion"),lv=n(" under which all models/components of the pipeline will be saved. For each component/model a folder is created inside the local path that is named after the given attribute name, "),Ld=a("em"),nv=n("e.g."),iv=s(),jd=a("code"),sv=n("./stable_diffusion/unet"),dv=n(`.
In addition, a `),qd=a("code"),fv=n("model_index.json"),hv=n(" file is created at the root of the local path, "),Vd=a("em"),cv=n("e.g."),uv=s(),Hd=a("code"),pv=n("./stable_diffusion/model_index.json"),gv=n(` so that the complete pipeline can again be instantiated
from the local path.`),mv=s(),T=a("li"),an=a("a"),Bd=a("code"),_v=n("to"),vv=n(" which accepts a "),Ud=a("code"),Ev=n("string"),bv=n(" or "),Fd=a("code"),Tv=n("torch.device"),yv=n(" to move all models that are of type "),zd=a("code"),Dv=n("torch.nn.Module"),wv=n(" to the passed device. The behavior is fully analogous to "),Le=a("a"),Iv=n("PyTorch\u2019s "),Yd=a("code"),xv=n("to"),Av=n(" method"),Gv=n("."),Sv=s(),b=a("li"),Qd=a("code"),kv=n("__call__"),Pv=n(" method to use the pipeline in inference. "),Wd=a("code"),Rv=n("__call__"),Ov=n(" defines inference logic of the pipeline and should ideally encompass all aspects of it, from pre-processing to forwarding tensors to the different models and schedulers, as well as post-processing. The API of the "),Zd=a("code"),$v=n("__call__"),Mv=n(" method can strongly vary from pipeline to pipeline. "),Jd=a("em"),Cv=n("E.g."),Nv=n(" a text-to-image pipeline, such as "),on=a("a"),Kd=a("code"),Lv=n("StableDiffusionPipeline"),jv=n(" should accept among other things the text prompt to generate the image. A pure image generation pipeline, such as "),bo=a("a"),qv=n("DDPMPipeline"),Vv=n(` on the other hand can be run without providing any inputs. To better understand what inputs can be adapted for
each pipeline, one should look directly into the respective pipeline.`),Wf=s(),D=a("p"),Xd=a("strong"),Hv=n("Note"),Bv=n(": All pipelines have PyTorch\u2019s autograd disabled by decorating the "),ef=a("code"),Uv=n("__call__"),Fv=n(" method with a "),To=a("a"),tf=a("code"),zv=n("torch.no_grad"),Yv=n(` decorator because pipelines should
not be used for training. If you want to store the gradients during the forward pass, we recommend writing your own pipeline, see also our `),yo=a("a"),Qv=n("community-examples"),Wv=n("."),Zf=s(),je=a("h2"),tt=a("a"),af=a("span"),k(Do.$$.fragment),Zv=s(),of=a("span"),Jv=n("Contribution"),Jf=s(),y=a("p"),Kv=n(`We are more than happy about any contribution to the officially supported pipelines \u{1F917}. We aspire
all of our pipelines to be  `),rf=a("strong"),Xv=n("self-contained"),e2=n(", "),lf=a("strong"),t2=n("easy-to-tweak"),a2=n(", "),nf=a("strong"),o2=n("beginner-friendly"),r2=n(" and for "),sf=a("strong"),l2=n("one-purpose-only"),n2=n("."),Kf=s(),A=a("ul"),at=a("li"),df=a("strong"),i2=n("Self-contained"),s2=n(": A pipeline shall be as self-contained as possible. More specifically, this means that all functionality should be either directly defined in the pipeline file itself, should be inherited from (and only from) the "),wo=a("a"),ff=a("code"),d2=n("DiffusionPipeline"),f2=n(" class"),h2=n(" or be directly attached to the model and scheduler components of the pipeline."),c2=s(),Pe=a("li"),hf=a("strong"),u2=n("Easy-to-use"),p2=n(`: Pipelines should be extremely easy to use - one should be able to load the pipeline and
use it for its designated task, `),cf=a("em"),g2=n("e.g."),m2=n(` text-to-image generation, in just a couple of lines of code. Most
logic including pre-processing, an unrolled diffusion loop, and post-processing should all happen inside the `),uf=a("code"),_2=n("__call__"),v2=n(" method."),E2=s(),Re=a("li"),pf=a("strong"),b2=n("Easy-to-tweak"),T2=n(": Certain pipelines will not be able to handle all use cases and tasks that you might like them to. If you want to use a certain pipeline for a specific use case that is not yet supported, you might have to copy the pipeline file and tweak the code to your needs. We try to make the pipeline code as readable as possible so that each part \u2013from pre-processing to diffusing to post-processing\u2013 can easily be adapted. If you would like the community to benefit from your customized pipeline, we would love to see a contribution to our "),Io=a("a"),y2=n("community-examples"),D2=n(". If you feel that an important pipeline should be part of the official pipelines but isn\u2019t, a contribution to the "),rn=a("a"),w2=n("official pipelines"),I2=n(" would be even better."),x2=s(),G=a("li"),gf=a("strong"),A2=n("One-purpose-only"),G2=n(": Pipelines should be used for one task and one task only. Even if two tasks are very similar from a modeling point of view, "),mf=a("em"),S2=n("e.g."),k2=n(" image2image translation and in-painting, pipelines shall be used for one task only to keep them "),_f=a("em"),P2=n("easy-to-tweak"),R2=n(" and "),vf=a("em"),O2=n("readable"),$2=n("."),Xf=s(),qe=a("h2"),ot=a("a"),Ef=a("span"),k(xo.$$.fragment),M2=s(),bf=a("span"),C2=n("Examples"),eh=s(),Ve=a("h3"),rt=a("a"),Tf=a("span"),k(Ao.$$.fragment),N2=s(),yf=a("span"),L2=n("Text-to-Image generation with Stable Diffusion"),th=s(),k(Go.$$.fragment),ah=s(),He=a("h3"),lt=a("a"),Df=a("span"),k(So.$$.fragment),j2=s(),wf=a("span"),q2=n("Image-to-Image text-guided generation with Stable Diffusion"),oh=s(),nt=a("p"),V2=n("The "),If=a("code"),H2=n("StableDiffusionImg2ImgPipeline"),B2=n(" lets you pass a text prompt and an initial image to condition the generation of new images."),rh=s(),k(ko.$$.fragment),lh=s(),Po=a("p"),U2=n("You can also run this example on colab "),Ro=a("a"),ln=a("img"),nh=s(),Be=a("h3"),it=a("a"),xf=a("span"),k(Oo.$$.fragment),F2=s(),Af=a("span"),z2=n("Tweak prompts reusing seeds and latents"),ih=s(),Ue=a("p"),Y2=n("You can generate your own latents to reproduce results, or tweak your prompt on a specific result you liked. "),$o=a("a"),Q2=n("This notebook"),W2=n(" shows how to do it step by step. You can also run it in Google Colab "),Mo=a("a"),nn=a("img"),sh=s(),Fe=a("h3"),st=a("a"),Gf=a("span"),k(Co.$$.fragment),Z2=s(),Sf=a("span"),J2=n("In-painting using Stable Diffusion"),dh=s(),dt=a("p"),K2=n("The "),kf=a("code"),X2=n("StableDiffusionInpaintPipeline"),eE=n(" lets you edit specific parts of an image by providing a mask and text prompt."),fh=s(),k(No.$$.fragment),hh=s(),Lo=a("p"),tE=n("You can also run this example on colab "),jo=a("a"),sn=a("img"),this.h()},l(f){const u=Ly('[data-svelte="svelte-1phssyn"]',document.head);$e=o(u,"META",{name:!0,content:!0}),u.forEach(t),Of=d(f),Me=o(f,"H1",{class:!0});var uh=r(Me);We=o(uh,"A",{id:!0,class:!0,href:!0});var bE=r(We);fn=o(bE,"SPAN",{});var TE=r(fn);P(ra.$$.fragment,TE),TE.forEach(t),bE.forEach(t),$h=d(uh),hn=o(uh,"SPAN",{});var yE=r(hn);Mh=i(yE,"Pipelines"),yE.forEach(t),uh.forEach(t),$f=d(f),Bo=o(f,"P",{});var DE=r(Bo);Ch=i(DE,`Pipelines provide a simple way to run state-of-the-art diffusion models in inference.
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components - all of which are needed to have a functioning end-to-end diffusion system.`),DE.forEach(t),Mf=d(f),Ze=o(f,"P",{});var ph=r(Ze);Nh=i(ph,"As an example, "),la=o(ph,"A",{href:!0,rel:!0});var wE=r(la);Lh=i(wE,"Stable Diffusion"),wE.forEach(t),jh=i(ph," has three independently trained models:"),ph.forEach(t),Cf=d(f),E=o(f,"UL",{});var S=r(E);cn=o(S,"LI",{});var IE=r(cn);Uo=o(IE,"A",{href:!0});var xE=r(Uo);qh=i(xE,"Autoencoder"),xE.forEach(t),IE.forEach(t),Vh=d(S),un=o(S,"LI",{});var AE=r(un);Fo=o(AE,"A",{href:!0});var GE=r(Fo);Hh=i(GE,"Conditional Unet"),GE.forEach(t),AE.forEach(t),Bh=d(S),pn=o(S,"LI",{});var SE=r(pn);na=o(SE,"A",{href:!0,rel:!0});var kE=r(na);Uh=i(kE,"CLIP text encoder"),kE.forEach(t),SE.forEach(t),Fh=d(S),ia=o(S,"LI",{});var gh=r(ia);zh=i(gh,"a scheduler component, "),zo=o(gh,"A",{href:!0});var PE=r(zo);Yh=i(PE,"scheduler"),PE.forEach(t),Qh=i(gh,","),gh.forEach(t),Wh=d(S),sa=o(S,"LI",{});var mh=r(sa);Zh=i(mh,"a "),da=o(mh,"A",{href:!0,rel:!0});var RE=r(da);Jh=i(RE,"CLIPImageProcessor"),RE.forEach(t),Kh=i(mh,","),mh.forEach(t),Xh=d(S),fa=o(S,"LI",{});var _h=r(fa);ec=i(_h,"as well as a "),Yo=o(_h,"A",{href:!0});var OE=r(Yo);tc=i(OE,"safety checker"),OE.forEach(t),ac=i(_h,`.
All of these components are necessary to run stable diffusion in inference even though they were trained
or created independently from each other.`),_h.forEach(t),S.forEach(t),Nf=d(f),Qo=o(f,"P",{});var $E=r(Qo);oc=i($E,`To that end, we strive to offer all open-sourced, state-of-the-art diffusion system under a unified API.
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During inference, we however want to be able to easily load all components and use them in inference - even if one component, `),Id=o(dn,"EM",{});var d0=r(Id);x1=i(d0,"e.g."),d0.forEach(t),A1=i(dn," CLIP\u2019s text encoder, originates from a different library, such as "),mo=o(dn,"A",{href:!0,rel:!0});var f0=r(mo);G1=i(f0,"Transformers"),f0.forEach(t),S1=i(dn,". To that end, all pipelines provide the following functionality:"),dn.forEach(t),Qf=d(f),x=o(f,"UL",{});var aa=r(x);g=o(aa,"LI",{});var m=r(g);_o=o(m,"A",{href:!0});var oE=r(_o);xd=o(oE,"CODE",{});var h0=r(xd);k1=i(h0,"from_pretrained"),h0.forEach(t),P1=i(oE," method"),oE.forEach(t),R1=i(m," that accepts a Hugging Face Hub repository id, "),Ad=o(m,"EM",{});var c0=r(Ad);O1=i(c0,"e.g."),c0.forEach(t),$1=d(m),vo=o(m,"A",{href:!0,rel:!0});var u0=r(vo);M1=i(u0,"runwayml/stable-diffusion-v1-5"),u0.forEach(t),C1=i(m," or a path to a local directory, "),Gd=o(m,"EM",{});var p0=r(Gd);N1=i(p0,"e.g."),p0.forEach(t),L1=i(m,`
\u201D./stable-diffusion\u201D. To correctly retrieve which models and components should be loaded, one has to provide a `),Sd=o(m,"CODE",{});var g0=r(Sd);j1=i(g0,"model_index.json"),g0.forEach(t),q1=i(m," file, "),kd=o(m,"EM",{});var m0=r(kd);V1=i(m0,"e.g."),m0.forEach(t),H1=d(m),Eo=o(m,"A",{href:!0,rel:!0});var _0=r(Eo);B1=i(_0,"runwayml/stable-diffusion-v1-5/model_index.json"),_0.forEach(t),U1=i(m,`, which defines all components that should be
loaded into the pipelines. More specifically, for each model/component one needs to define the format `),Pd=o(m,"CODE",{});var v0=r(Pd);F1=i(v0,'<name>: ["<library>", "<class name>"]'),v0.forEach(t),z1=i(m,". "),Rd=o(m,"CODE",{});var E0=r(Rd);Y1=i(E0,"<name>"),E0.forEach(t),Q1=i(m," is the attribute name given to the loaded instance of "),Od=o(m,"CODE",{});var b0=r(Od);W1=i(b0,"<class name>"),b0.forEach(t),Z1=i(m," which can be found in the library or pipeline folder called "),$d=o(m,"CODE",{});var T0=r($d);J1=i(T0,'"<library>"'),T0.forEach(t),K1=i(m,"."),m.forEach(t),X1=d(aa),_=o(aa,"LI",{});var v=r(_);tn=o(v,"A",{href:!0});var y0=r(tn);Md=o(y0,"CODE",{});var D0=r(Md);ev=i(D0,"save_pretrained"),D0.forEach(t),y0.forEach(t),tv=i(v," that accepts a local path, "),Cd=o(v,"EM",{});var w0=r(Cd);av=i(w0,"e.g."),w0.forEach(t),ov=d(v),Nd=o(v,"CODE",{});var I0=r(Nd);rv=i(I0,"./stable-diffusion"),I0.forEach(t),lv=i(v," under which all models/components of the pipeline will be saved. For each component/model a folder is created inside the local path that is named after the given attribute name, "),Ld=o(v,"EM",{});var x0=r(Ld);nv=i(x0,"e.g."),x0.forEach(t),iv=d(v),jd=o(v,"CODE",{});var A0=r(jd);sv=i(A0,"./stable_diffusion/unet"),A0.forEach(t),dv=i(v,`.
In addition, a `),qd=o(v,"CODE",{});var G0=r(qd);fv=i(G0,"model_index.json"),G0.forEach(t),hv=i(v," file is created at the root of the local path, "),Vd=o(v,"EM",{});var S0=r(Vd);cv=i(S0,"e.g."),S0.forEach(t),uv=d(v),Hd=o(v,"CODE",{});var k0=r(Hd);pv=i(k0,"./stable_diffusion/model_index.json"),k0.forEach(t),gv=i(v,` so that the complete pipeline can again be instantiated
from the local path.`),v.forEach(t),mv=d(aa),T=o(aa,"LI",{});var Ge=r(T);an=o(Ge,"A",{href:!0});var P0=r(an);Bd=o(P0,"CODE",{});var R0=r(Bd);_v=i(R0,"to"),R0.forEach(t),P0.forEach(t),vv=i(Ge," which accepts a "),Ud=o(Ge,"CODE",{});var O0=r(Ud);Ev=i(O0,"string"),O0.forEach(t),bv=i(Ge," or "),Fd=o(Ge,"CODE",{});var $0=r(Fd);Tv=i($0,"torch.device"),$0.forEach(t),yv=i(Ge," to move all models that are of type "),zd=o(Ge,"CODE",{});var M0=r(zd);Dv=i(M0,"torch.nn.Module"),M0.forEach(t),wv=i(Ge," to the passed device. The behavior is fully analogous to "),Le=o(Ge,"A",{href:!0,rel:!0});var Ih=r(Le);Iv=i(Ih,"PyTorch\u2019s "),Yd=o(Ih,"CODE",{});var C0=r(Yd);xv=i(C0,"to"),C0.forEach(t),Av=i(Ih," method"),Ih.forEach(t),Gv=i(Ge,"."),Ge.forEach(t),Sv=d(aa),b=o(aa,"LI",{});var w=r(b);Qd=o(w,"CODE",{});var N0=r(Qd);kv=i(N0,"__call__"),N0.forEach(t),Pv=i(w," method to use the pipeline in inference. "),Wd=o(w,"CODE",{});var L0=r(Wd);Rv=i(L0,"__call__"),L0.forEach(t),Ov=i(w," defines inference logic of the pipeline and should ideally encompass all aspects of it, from pre-processing to forwarding tensors to the different models and schedulers, as well as post-processing. The API of the "),Zd=o(w,"CODE",{});var j0=r(Zd);$v=i(j0,"__call__"),j0.forEach(t),Mv=i(w," method can strongly vary from pipeline to pipeline. "),Jd=o(w,"EM",{});var q0=r(Jd);Cv=i(q0,"E.g."),q0.forEach(t),Nv=i(w," a text-to-image pipeline, such as "),on=o(w,"A",{href:!0});var V0=r(on);Kd=o(V0,"CODE",{});var H0=r(Kd);Lv=i(H0,"StableDiffusionPipeline"),H0.forEach(t),V0.forEach(t),jv=i(w," should accept among other things the text prompt to generate the image. A pure image generation pipeline, such as "),bo=o(w,"A",{href:!0,rel:!0});var B0=r(bo);qv=i(B0,"DDPMPipeline"),B0.forEach(t),Vv=i(w,` on the other hand can be run without providing any inputs. To better understand what inputs can be adapted for
each pipeline, one should look directly into the respective pipeline.`),w.forEach(t),aa.forEach(t),Wf=d(f),D=o(f,"P",{});var ze=r(D);Xd=o(ze,"STRONG",{});var U0=r(Xd);Hv=i(U0,"Note"),U0.forEach(t),Bv=i(ze,": All pipelines have PyTorch\u2019s autograd disabled by decorating the "),ef=o(ze,"CODE",{});var F0=r(ef);Uv=i(F0,"__call__"),F0.forEach(t),Fv=i(ze," method with a "),To=o(ze,"A",{href:!0,rel:!0});var z0=r(To);tf=o(z0,"CODE",{});var Y0=r(tf);zv=i(Y0,"torch.no_grad"),Y0.forEach(t),z0.forEach(t),Yv=i(ze,` decorator because pipelines should
not be used for training. If you want to store the gradients during the forward pass, we recommend writing your own pipeline, see also our `),yo=o(ze,"A",{href:!0,rel:!0});var Q0=r(yo);Qv=i(Q0,"community-examples"),Q0.forEach(t),Wv=i(ze,"."),ze.forEach(t),Zf=d(f),je=o(f,"H2",{class:!0});var xh=r(je);tt=o(xh,"A",{id:!0,class:!0,href:!0});var W0=r(tt);af=o(W0,"SPAN",{});var Z0=r(af);P(Do.$$.fragment,Z0),Z0.forEach(t),W0.forEach(t),Zv=d(xh),of=o(xh,"SPAN",{});var J0=r(of);Jv=i(J0,"Contribution"),J0.forEach(t),xh.forEach(t),Jf=d(f),y=o(f,"P",{});var Oe=r(y);Kv=i(Oe,`We are more than happy about any contribution to the officially supported pipelines \u{1F917}. We aspire
all of our pipelines to be  `),rf=o(Oe,"STRONG",{});var K0=r(rf);Xv=i(K0,"self-contained"),K0.forEach(t),e2=i(Oe,", "),lf=o(Oe,"STRONG",{});var X0=r(lf);t2=i(X0,"easy-to-tweak"),X0.forEach(t),a2=i(Oe,", "),nf=o(Oe,"STRONG",{});var ey=r(nf);o2=i(ey,"beginner-friendly"),ey.forEach(t),r2=i(Oe," and for "),sf=o(Oe,"STRONG",{});var ty=r(sf);l2=i(ty,"one-purpose-only"),ty.forEach(t),n2=i(Oe,"."),Oe.forEach(t),Kf=d(f),A=o(f,"UL",{});var oa=r(A);at=o(oa,"LI",{});var Pf=r(at);df=o(Pf,"STRONG",{});var ay=r(df);i2=i(ay,"Self-contained"),ay.forEach(t),s2=i(Pf,": A pipeline shall be as self-contained as possible. More specifically, this means that all functionality should be either directly defined in the pipeline file itself, should be inherited from (and only from) the "),wo=o(Pf,"A",{href:!0});var rE=r(wo);ff=o(rE,"CODE",{});var oy=r(ff);d2=i(oy,"DiffusionPipeline"),oy.forEach(t),f2=i(rE," class"),rE.forEach(t),h2=i(Pf," or be directly attached to the model and scheduler components of the pipeline."),Pf.forEach(t),c2=d(oa),Pe=o(oa,"LI",{});var Vo=r(Pe);hf=o(Vo,"STRONG",{});var ry=r(hf);u2=i(ry,"Easy-to-use"),ry.forEach(t),p2=i(Vo,`: Pipelines should be extremely easy to use - one should be able to load the pipeline and
use it for its designated task, `),cf=o(Vo,"EM",{});var ly=r(cf);g2=i(ly,"e.g."),ly.forEach(t),m2=i(Vo,` text-to-image generation, in just a couple of lines of code. Most
logic including pre-processing, an unrolled diffusion loop, and post-processing should all happen inside the `),uf=o(Vo,"CODE",{});var ny=r(uf);_2=i(ny,"__call__"),ny.forEach(t),v2=i(Vo," method."),Vo.forEach(t),E2=d(oa),Re=o(oa,"LI",{});var Ho=r(Re);pf=o(Ho,"STRONG",{});var iy=r(pf);b2=i(iy,"Easy-to-tweak"),iy.forEach(t),T2=i(Ho,": Certain pipelines will not be able to handle all use cases and tasks that you might like them to. If you want to use a certain pipeline for a specific use case that is not yet supported, you might have to copy the pipeline file and tweak the code to your needs. We try to make the pipeline code as readable as possible so that each part \u2013from pre-processing to diffusing to post-processing\u2013 can easily be adapted. If you would like the community to benefit from your customized pipeline, we would love to see a contribution to our "),Io=o(Ho,"A",{href:!0,rel:!0});var sy=r(Io);y2=i(sy,"community-examples"),sy.forEach(t),D2=i(Ho,". If you feel that an important pipeline should be part of the official pipelines but isn\u2019t, a contribution to the "),rn=o(Ho,"A",{href:!0});var dy=r(rn);w2=i(dy,"official pipelines"),dy.forEach(t),I2=i(Ho," would be even better."),Ho.forEach(t),x2=d(oa),G=o(oa,"LI",{});var Ye=r(G);gf=o(Ye,"STRONG",{});var fy=r(gf);A2=i(fy,"One-purpose-only"),fy.forEach(t),G2=i(Ye,": Pipelines should be used for one task and one task only. Even if two tasks are very similar from a modeling point of view, "),mf=o(Ye,"EM",{});var hy=r(mf);S2=i(hy,"e.g."),hy.forEach(t),k2=i(Ye," image2image translation and in-painting, pipelines shall be used for one task only to keep them "),_f=o(Ye,"EM",{});var cy=r(_f);P2=i(cy,"easy-to-tweak"),cy.forEach(t),R2=i(Ye," and "),vf=o(Ye,"EM",{});var uy=r(vf);O2=i(uy,"readable"),uy.forEach(t),$2=i(Ye,"."),Ye.forEach(t),oa.forEach(t),Xf=d(f),qe=o(f,"H2",{class:!0});var Ah=r(qe);ot=o(Ah,"A",{id:!0,class:!0,href:!0});var py=r(ot);Ef=o(py,"SPAN",{});var gy=r(Ef);P(xo.$$.fragment,gy),gy.forEach(t),py.forEach(t),M2=d(Ah),bf=o(Ah,"SPAN",{});var my=r(bf);C2=i(my,"Examples"),my.forEach(t),Ah.forEach(t),eh=d(f),Ve=o(f,"H3",{class:!0});var Gh=r(Ve);rt=o(Gh,"A",{id:!0,class:!0,href:!0});var _y=r(rt);Tf=o(_y,"SPAN",{});var vy=r(Tf);P(Ao.$$.fragment,vy),vy.forEach(t),_y.forEach(t),N2=d(Gh),yf=o(Gh,"SPAN",{});var Ey=r(yf);L2=i(Ey,"Text-to-Image generation with Stable Diffusion"),Ey.forEach(t),Gh.forEach(t),th=d(f),P(Go.$$.fragment,f),ah=d(f),He=o(f,"H3",{class:!0});var Sh=r(He);lt=o(Sh,"A",{id:!0,class:!0,href:!0});var by=r(lt);Df=o(by,"SPAN",{});var Ty=r(Df);P(So.$$.fragment,Ty),Ty.forEach(t),by.forEach(t),j2=d(Sh),wf=o(Sh,"SPAN",{});var yy=r(wf);q2=i(yy,"Image-to-Image text-guided generation with Stable Diffusion"),yy.forEach(t),Sh.forEach(t),oh=d(f),nt=o(f,"P",{});var kh=r(nt);V2=i(kh,"The "),If=o(kh,"CODE",{});var Dy=r(If);H2=i(Dy,"StableDiffusionImg2ImgPipeline"),Dy.forEach(t),B2=i(kh," lets you pass a text prompt and an initial image to condition the generation of new images."),kh.forEach(t),rh=d(f),P(ko.$$.fragment,f),lh=d(f),Po=o(f,"P",{});var lE=r(Po);U2=i(lE,"You can also run this example on colab "),Ro=o(lE,"A",{href:!0,rel:!0});var wy=r(Ro);ln=o(wy,"IMG",{src:!0,alt:!0}),wy.forEach(t),lE.forEach(t),nh=d(f),Be=o(f,"H3",{class:!0});var Ph=r(Be);it=o(Ph,"A",{id:!0,class:!0,href:!0});var Iy=r(it);xf=o(Iy,"SPAN",{});var xy=r(xf);P(Oo.$$.fragment,xy),xy.forEach(t),Iy.forEach(t),F2=d(Ph),Af=o(Ph,"SPAN",{});var Ay=r(Af);z2=i(Ay,"Tweak prompts reusing seeds and latents"),Ay.forEach(t),Ph.forEach(t),ih=d(f),Ue=o(f,"P",{});var Rf=r(Ue);Y2=i(Rf,"You can generate your own latents to reproduce results, or tweak your prompt on a specific result you liked. 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Hy={local:"pipelines",sections:[{local:"diffusers-summary",title:"\u{1F9E8} Diffusers Summary"},{local:"pipelines-api",title:"Pipelines API"},{local:"contribution",title:"Contribution"},{local:"examples",sections:[{local:"texttoimage-generation-with-stable-diffusion",title:"Text-to-Image generation with Stable Diffusion"},{local:"imagetoimage-textguided-generation-with-stable-diffusion",title:"Image-to-Image text-guided generation with Stable Diffusion"},{local:"tweak-prompts-reusing-seeds-and-latents",title:"Tweak prompts reusing seeds and latents"},{local:"inpainting-using-stable-diffusion",title:"In-painting using Stable Diffusion"}],title:"Examples"}],title:"Pipelines"};function By(sE){return qy(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Yy extends My{constructor($e){super();Cy(this,$e,By,Vy,Ny,{})}}export{Yy as default,Hy as metadata};
