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

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27 days ago

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	import{S as Iy,i as gy,s as Cy,e as o,k as f,w as h,t,M as Vy,c as n,d as l,m as c,a as i,x as m,h as a,b as d,G as s,g as p,y,q as M,o as J,B as w,v as Wy}from"../../chunks/vendor-hf-doc-builder.js";import{T as wu}from"../../chunks/Tip-hf-doc-builder.js";import{I as K}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as Z}from"../../chunks/CodeBlock-hf-doc-builder.js";import{D as Ry}from"../../chunks/DocNotebookDropdown-hf-doc-builder.js";function Ny(_e){let u,V,v,E,I,b,D,g;return{c(){u=o("p"),V=t("\u{1F4A1} Skip to the "),v=o("a"),E=t("DiffusionPipeline explained"),I=t(" section if you interested in learning in more detail about how the "),b=o("a"),D=t("DiffusionPipeline"),g=t(" class works."),this.h()},l(_){u=n(_,"P",{});var U=i(u);V=a(U,"\u{1F4A1} Skip to the "),v=n(U,"A",{href:!0});var C=i(v);E=a(C,"DiffusionPipeline explained"),C.forEach(l),I=a(U," section if you interested in learning in more detail about how the "),b=n(U,"A",{href:!0});var W=i(b);D=a(W,"DiffusionPipeline"),W.forEach(l),g=a(U," class works."),U.forEach(l),this.h()},h(){d(v,"href","#diffusionpipeline-explained"),d(b,"href","/docs/diffusers/main/en/api/pipelines/overview#diffusers.DiffusionPipeline")},m(_,U){p(_,u,U),s(u,V),s(u,v),s(v,E),s(u,I),s(u,b),s(b,D),s(u,g)},d(_){_&&l(u)}}}function Dy(_e){let u,V,v,E,I,b,D,g;return{c(){u=o("p"),V=t("\u{1F4A1} When the checkpoints have identical model structures, but they were trained on different datasets and with a different training setup, they should be stored in separate repositories instead of variations (for example, "),v=o("code"),E=t("stable-diffusion-v1-4"),I=t(" and "),b=o("code"),D=t("stable-diffusion-v1-5"),g=t(").")},l(_){u=n(_,"P",{});var U=i(u);V=a(U,"\u{1F4A1} When the checkpoints have identical model structures, but they were trained on different datasets and with a different training setup, they should be stored in separate repositories instead of variations (for example, "),v=n(U,"CODE",{});var C=i(v);E=a(C,"stable-diffusion-v1-4"),C.forEach(l),I=a(U," and "),b=n(U,"CODE",{});var W=i(b);D=a(W,"stable-diffusion-v1-5"),W.forEach(l),g=a(U,")."),U.forEach(l)},m(_,U){p(_,u,U),s(u,V),s(u,v),s(v,E),s(u,I),s(u,b),s(b,D),s(u,g)},d(_){_&&l(u)}}}function $y(_e){let u,V,v,E,I,b,D,g,_,U,C,W,G,Js;return{c(){u=o("p"),V=t("If you load diffusers pipelines or models with "),v=o("code"),E=t('revision="fp16"'),I=t(" or "),b=o("code"),D=t('revision="non_ema"'),g=t(`,
	please make sure to update to code and use `),_=o("code"),U=t('variant="fp16"'),C=t(" or "),W=o("code"),G=t('variation="non_ema"'),Js=t(` respectively
	instead.`)},l(A){u=n(A,"P",{});var R=i(u);V=a(R,"If you load diffusers pipelines or models with "),v=n(R,"CODE",{});var bl=i(v);E=a(bl,'revision="fp16"'),bl.forEach(l),I=a(R," or "),b=n(R,"CODE",{});var ws=i(b);D=a(ws,'revision="non_ema"'),ws.forEach(l),g=a(R,`,
	please make sure to update to code and use `),_=n(R,"CODE",{});var H=i(_);U=a(H,'variant="fp16"'),H.forEach(l),C=a(R," or "),W=n(R,"CODE",{});var Ul=i(W);G=a(Ul,'variation="non_ema"'),Ul.forEach(l),Js=a(R,` respectively
	instead.`),R.forEach(l)},m(A,R){p(A,u,R),s(u,V),s(u,v),s(v,E),s(u,I),s(u,b),s(b,D),s(u,g),s(u,_),s(_,U),s(u,C),s(u,W),s(W,G),s(u,Js)},d(A){A&&l(u)}}}function By(_e){let u,V,v,E,I,b,D,g,_,U,C,W,G,Js,A,R,bl,ws,H,Ul,Zt,Ai,Si,Vo,jl,Qi,Wo,S,_t,Xi,xi,Et,zi,qi,kt,Pi,Yi,It,Fi,Ro,fe,Ee,gt,vs,Hi,Ct,Oi,No,ke,Do,Q,Li,Zl,Ki,er,Ts,sr,lr,_l,tr,ar,$o,bs,Bo,Ie,or,El,nr,ir,Go,Us,Ao,ee,rr,js,Vt,pr,fr,Zs,Wt,cr,dr,So,_s,Qo,ce,ge,Rt,Es,ur,Nt,hr,Xo,X,mr,ks,Dt,yr,Mr,Is,$t,Jr,wr,Bt,vr,Tr,xo,gs,zo,Ce,br,kl,Ur,jr,qo,Cs,Po,Ve,Zr,Il,_r,Er,Yo,de,We,Gt,Vs,kr,At,Ir,Fo,gl,gr,Ho,se,St,Cr,Vr,Qt,Wr,Rr,Xt,Nr,Oo,Re,Dr,xt,$r,Br,Lo,Ws,Ko,N,Gr,Cl,Ar,Sr,Vl,Qr,Xr,Wl,xr,zr,zt,qr,Pr,Rs,Yr,Fr,en,x,Hr,Rl,Or,Lr,qt,Kr,ep,Nl,sp,lp,sn,Ns,ln,ue,Ne,Pt,Ds,tp,Yt,ap,tn,z,op,$s,np,ip,Ft,rp,pp,Ht,fp,cp,an,Bs,on,he,De,Ot,Gs,dp,Lt,up,nn,$e,hp,Dl,mp,yp,rn,As,pn,Be,Mp,Kt,Jp,wp,fn,Ss,cn,$l,vp,dn,Qs,un,me,Ge,ea,Xs,Tp,sa,bp,hn,Bl,Up,mn,Ae,xs,jp,zs,la,Zp,_p,Ep,ta,kp,yn,Se,Mn,le,Ip,aa,gp,Cp,Gl,Vp,Wp,Jn,Qe,oa,ye,na,ia,Rp,Np,ra,pa,Dp,$p,fa,ca,Bp,Gp,Me,Je,da,Ap,Sp,ua,Qp,Xp,wn,xp,we,ha,zp,qp,ma,Pp,Yp,qs,ya,Fp,Hp,Ma,Op,Lp,ve,Ja,Kp,ef,wa,sf,lf,va,Ta,tf,vn,Al,af,Tn,Xe,ba,T,Ua,of,nf,ja,rf,pf,Za,ff,cf,_a,df,uf,Ea,hf,mf,ka,yf,Mf,Ia,Jf,wf,ga,vf,Tf,Ca,bf,Uf,Va,jf,Zf,Wa,_f,Ef,Ra,kf,If,gf,Na,$,Da,Cf,Vf,$a,Wf,Rf,Ps,Ba,Nf,Df,Ga,$f,Bf,Aa,Gf,Af,bn,Ys,Un,te,Sf,Sl,Qf,Xf,Sa,xf,zf,jn,Fs,Zn,ae,qf,Qa,Pf,Yf,Xa,Ff,Hf,_n,Hs,En,Ql,Of,kn,Te,xe,xa,Os,Lf,Ls,Kf,za,ec,sc,In,oe,lc,qa,tc,ac,Xl,oc,nc,gn,Ks,Cn,xl,ic,Vn,ze,rc,Pa,pc,fc,Wn,qe,Rn,be,Pe,Ya,el,cc,Fa,dc,Nn,ne,uc,zl,hc,mc,ql,yc,Mc,Dn,q,Jc,Ha,wc,vc,Oa,Tc,bc,sl,La,Uc,jc,$n,ll,Bn,Ye,Zc,tl,_c,Ec,Gn,al,An,P,kc,Ka,Ic,gc,Pl,Cc,Vc,Yl,Wc,Rc,Sn,ol,Qn,Ue,Fe,eo,nl,Nc,so,Dc,Xn,Y,$c,Fl,Bc,Gc,lo,Ac,Sc,to,Qc,Xc,xn,He,xc,Hl,zc,qc,zn,il,qn,je,Oe,ao,rl,Pc,oo,Yc,Pn,Le,Fc,Ol,Hc,Oc,Yn,Ke,pl,Lc,Ll,Kc,ed,sd,Ze,ld,Kl,td,ad,no,od,nd,Fn,ie,id,et,rd,pd,fl,io,fd,cd,Hn,cl,On,es,dd,st,ud,hd,Ln,k,ss,ro,md,yd,dl,Md,Jd,wd,ls,po,vd,Td,ul,bd,Ud,jd,ts,fo,Zd,_d,lt,Ed,kd,Id,as,co,gd,Cd,hl,Vd,Wd,Rd,os,uo,Nd,Dd,ml,$d,Bd,Gd,ns,ho,Ad,Sd,tt,Qd,Xd,xd,is,mo,zd,qd,at,Pd,Yd,Kn,yl,ei,rs,Fd,Ml,yo,Hd,Od,si,Jl,li,ot,Ld,ti,wl,ai,re,Kd,Mo,eu,su,nt,lu,tu,oi,pe,it,au,Jo,ou,nu,rt,iu,wo,ru,pu,O,fu,vo,cu,du,To,uu,hu,bo,mu,yu,ni,vl,ii;return b=new K({}),C=new Ry({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/loading.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/pytorch/loading.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers_doc/en/tensorflow/loading.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/loading.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/pytorch/loading.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/diffusers_doc/en/tensorflow/loading.ipynb"}]}}),vs=new K({}),ke=new wu({props:{$$slots:{default:[Ny]},$$scope:{ctx:_e}}}),bs=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcmVwb19pZCUyMCUzRCUyMCUyMnJ1bndheW1sJTJGc3RhYmxlLWRpZmZ1c2lvbi12MS01JTIyJTBBcGlwZSUyMCUzRCUyMERpZmZ1c2lvblBpcGVsaW5lLmZyb21fcHJldHJhaW5lZChyZXBvX2lkJTJDJTIwdXNlX3NhZmV0ZW5zb3JzJTNEVHJ1ZSk=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

	repo_id = <span class="hljs-string">"runwayml/stable-diffusion-v1-5"</span>
	pipe = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=<span class="hljs-literal">True</span>)`}}),Us=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFN0YWJsZURpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcmVwb19pZCUyMCUzRCUyMCUyMnJ1bndheW1sJTJGc3RhYmxlLWRpZmZ1c2lvbi12MS01JTIyJTBBcGlwZSUyMCUzRCUyMFN0YWJsZURpZmZ1c2lvblBpcGVsaW5lLmZyb21fcHJldHJhaW5lZChyZXBvX2lkJTJDJTIwdXNlX3NhZmV0ZW5zb3JzJTNEVHJ1ZSk=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline

	repo_id = <span class="hljs-string">"runwayml/stable-diffusion-v1-5"</span>
	pipe = StableDiffusionPipeline.from_pretrained(repo_id, use_safetensors=<span class="hljs-literal">True</span>)`}}),_s=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFN0YWJsZURpZmZ1c2lvbkltZzJJbWdQaXBlbGluZSUwQSUwQXJlcG9faWQlMjAlM0QlMjAlMjJydW53YXltbCUyRnN0YWJsZS1kaWZmdXNpb24tdjEtNSUyMiUwQXBpcGUlMjAlM0QlMjBTdGFibGVEaWZmdXNpb25JbWcySW1nUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKHJlcG9faWQp",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionImg2ImgPipeline

	repo_id = <span class="hljs-string">"runwayml/stable-diffusion-v1-5"</span>
	pipe = StableDiffusionImg2ImgPipeline.from_pretrained(repo_id)`}}),Es=new K({}),gs=new Z({props:{code:"Z2l0LWxmcyUyMGluc3RhbGwlMEFnaXQlMjBjbG9uZSUyMGh0dHBzJTNBJTJGJTJGaHVnZ2luZ2ZhY2UuY28lMkZydW53YXltbCUyRnN0YWJsZS1kaWZmdXNpb24tdjEtNQ==",highlighted:`git-lfs install
	git <span class="hljs-built_in">clone</span> https://huggingface.co/runwayml/stable-diffusion-v1-5`}}),Cs=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcmVwb19pZCUyMCUzRCUyMCUyMi4lMkZzdGFibGUtZGlmZnVzaW9uLXYxLTUlMjIlMEFzdGFibGVfZGlmZnVzaW9uJTIwJTNEJTIwRGlmZnVzaW9uUGlwZWxpbmUuZnJvbV9wcmV0cmFpbmVkKHJlcG9faWQlMkMlMjB1c2Vfc2FmZXRlbnNvcnMlM0RUcnVlKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

	repo_id = <span class="hljs-string">"./stable-diffusion-v1-5"</span>
	stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=<span class="hljs-literal">True</span>)`}}),Vs=new K({}),Ws=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcmVwb19pZCUyMCUzRCUyMCUyMnJ1bndheW1sJTJGc3RhYmxlLWRpZmZ1c2lvbi12MS01JTIyJTBBc3RhYmxlX2RpZmZ1c2lvbiUyMCUzRCUyMERpZmZ1c2lvblBpcGVsaW5lLmZyb21fcHJldHJhaW5lZChyZXBvX2lkJTJDJTIwdXNlX3NhZmV0ZW5zb3JzJTNEVHJ1ZSklMEFzdGFibGVfZGlmZnVzaW9uLnNjaGVkdWxlci5jb21wYXRpYmxlcw==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

	repo_id = <span class="hljs-string">"runwayml/stable-diffusion-v1-5"</span>
	stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=<span class="hljs-literal">True</span>)
	stable_diffusion.scheduler.compatibles`}}),Ns=new Z({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline, EulerDiscreteScheduler, DPMSolverMultistepScheduler

	repo_id = <span class="hljs-string">"runwayml/stable-diffusion-v1-5"</span>

	scheduler = EulerDiscreteScheduler.from_pretrained(repo_id, subfolder=<span class="hljs-string">"scheduler"</span>)

	stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, scheduler=scheduler, use_safetensors=<span class="hljs-literal">True</span>)`}}),Ds=new K({}),Bs=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcmVwb19pZCUyMCUzRCUyMCUyMnJ1bndheW1sJTJGc3RhYmxlLWRpZmZ1c2lvbi12MS01JTIyJTBBc3RhYmxlX2RpZmZ1c2lvbiUyMCUzRCUyMERpZmZ1c2lvblBpcGVsaW5lLmZyb21fcHJldHJhaW5lZChyZXBvX2lkJTJDJTIwc2FmZXR5X2NoZWNrZXIlM0ROb25lJTJDJTIwdXNlX3NhZmV0ZW5zb3JzJTNEVHJ1ZSk=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

	repo_id = <span class="hljs-string">"runwayml/stable-diffusion-v1-5"</span>
	stable_diffusion = DiffusionPipeline.from_pretrained(repo_id, safety_checker=<span class="hljs-literal">None</span>, use_safetensors=<span class="hljs-literal">True</span>)`}}),Gs=new K({}),As=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFN0YWJsZURpZmZ1c2lvblBpcGVsaW5lJTJDJTIwU3RhYmxlRGlmZnVzaW9uSW1nMkltZ1BpcGVsaW5lJTBBJTBBbW9kZWxfaWQlMjAlM0QlMjAlMjJydW53YXltbCUyRnN0YWJsZS1kaWZmdXNpb24tdjEtNSUyMiUwQXN0YWJsZV9kaWZmdXNpb25fdHh0MmltZyUyMCUzRCUyMFN0YWJsZURpZmZ1c2lvblBpcGVsaW5lLmZyb21fcHJldHJhaW5lZChtb2RlbF9pZCUyQyUyMHVzZV9zYWZldGVuc29ycyUzRFRydWUpJTBBJTBBY29tcG9uZW50cyUyMCUzRCUyMHN0YWJsZV9kaWZmdXNpb25fdHh0MmltZy5jb21wb25lbnRz",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline, StableDiffusionImg2ImgPipeline

	model_id = <span class="hljs-string">"runwayml/stable-diffusion-v1-5"</span>
	stable_diffusion_txt2img = StableDiffusionPipeline.from_pretrained(model_id, use_safetensors=<span class="hljs-literal">True</span>)

	components = stable_diffusion_txt2img.components`}}),Ss=new Z({props:{code:"c3RhYmxlX2RpZmZ1c2lvbl9pbWcyaW1nJTIwJTNEJTIwU3RhYmxlRGlmZnVzaW9uSW1nMkltZ1BpcGVsaW5lKCoqY29tcG9uZW50cyk=",highlighted:"stable_diffusion_img2img = StableDiffusionImg2ImgPipeline(**components)"}}),Qs=new Z({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline, StableDiffusionImg2ImgPipeline

	model_id = <span class="hljs-string">"runwayml/stable-diffusion-v1-5"</span>
	stable_diffusion_txt2img = StableDiffusionPipeline.from_pretrained(model_id, use_safetensors=<span class="hljs-literal">True</span>)
	stable_diffusion_img2img = StableDiffusionImg2ImgPipeline(
	vae=stable_diffusion_txt2img.vae,
	text_encoder=stable_diffusion_txt2img.text_encoder,
	tokenizer=stable_diffusion_txt2img.tokenizer,
	unet=stable_diffusion_txt2img.unet,
	scheduler=stable_diffusion_txt2img.scheduler,
	safety_checker=<span class="hljs-literal">None</span>,
	feature_extractor=<span class="hljs-literal">None</span>,
	requires_safety_checker=<span class="hljs-literal">False</span>,
	)`}}),Xs=new K({}),Se=new wu({props:{$$slots:{default:[Dy]},$$scope:{ctx:_e}}}),Ys=new Z({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline
	<span class="hljs-keyword">import</span> torch

	<span class="hljs-comment"># load fp16 variant</span>
	stable_diffusion = DiffusionPipeline.from_pretrained(
	<span class="hljs-string">"runwayml/stable-diffusion-v1-5"</span>, variant=<span class="hljs-string">"fp16"</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>
	)
	<span class="hljs-comment"># load non_ema variant</span>
	stable_diffusion = DiffusionPipeline.from_pretrained(
	<span class="hljs-string">"runwayml/stable-diffusion-v1-5"</span>, variant=<span class="hljs-string">"non_ema"</span>, use_safetensors=<span class="hljs-literal">True</span>
	)`}}),Fs=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTBBJTIzJTIwc2F2ZSUyMGFzJTIwZnAxNiUyMHZhcmlhbnQlMEFzdGFibGVfZGlmZnVzaW9uLnNhdmVfcHJldHJhaW5lZCglMjJydW53YXltbCUyRnN0YWJsZS1kaWZmdXNpb24tdjEtNSUyMiUyQyUyMHZhcmlhbnQlM0QlMjJmcDE2JTIyKSUwQSUyMyUyMHNhdmUlMjBhcyUyMG5vbi1lbWElMjB2YXJpYW50JTBBc3RhYmxlX2RpZmZ1c2lvbi5zYXZlX3ByZXRyYWluZWQoJTIycnVud2F5bWwlMkZzdGFibGUtZGlmZnVzaW9uLXYxLTUlMjIlMkMlMjB2YXJpYW50JTNEJTIybm9uX2VtYSUyMik=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

	<span class="hljs-comment"># save as fp16 variant</span>
	stable_diffusion.save_pretrained(<span class="hljs-string">"runwayml/stable-diffusion-v1-5"</span>, variant=<span class="hljs-string">"fp16"</span>)
	<span class="hljs-comment"># save as non-ema variant</span>
	stable_diffusion.save_pretrained(<span class="hljs-string">"runwayml/stable-diffusion-v1-5"</span>, variant=<span class="hljs-string">"non_ema"</span>)`}}),Hs=new Z({props:{code:"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",highlighted:`<span class="hljs-comment"># \u{1F44E} this won't work</span>
	stable_diffusion = DiffusionPipeline.from_pretrained(
	<span class="hljs-string">"./stable-diffusion-v1-5"</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>
	)
	<span class="hljs-comment"># \u{1F44D} this works</span>
	stable_diffusion = DiffusionPipeline.from_pretrained(
	<span class="hljs-string">"./stable-diffusion-v1-5"</span>, variant=<span class="hljs-string">"fp16"</span>, torch_dtype=torch.float16, use_safetensors=<span class="hljs-literal">True</span>
	)`}}),Os=new K({}),Ks=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcGlwZSUyMCUzRCUyMERpZmZ1c2lvblBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMjJDb21wVmlzJTJGc3RhYmxlLWRpZmZ1c2lvbi12MS00JTIyJTJDJTIwcmV2aXNpb24lM0QlMjJmcDE2JTIyJTJDJTIwdXNlX3NhZmV0ZW5zb3JzJTNEVHJ1ZSk=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

	pipe = DiffusionPipeline.from_pretrained(<span class="hljs-string">"CompVis/stable-diffusion-v1-4"</span>, revision=<span class="hljs-string">"fp16"</span>, use_safetensors=<span class="hljs-literal">True</span>)`}}),qe=new wu({props:{warning:!0,$$slots:{default:[$y]},$$scope:{ctx:_e}}}),el=new K({}),ll=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFVOZXQyRENvbmRpdGlvbk1vZGVsJTBBJTBBcmVwb19pZCUyMCUzRCUyMCUyMnJ1bndheW1sJTJGc3RhYmxlLWRpZmZ1c2lvbi12MS01JTIyJTBBbW9kZWwlMjAlM0QlMjBVTmV0MkRDb25kaXRpb25Nb2RlbC5mcm9tX3ByZXRyYWluZWQocmVwb19pZCUyQyUyMHN1YmZvbGRlciUzRCUyMnVuZXQlMjIlMkMlMjB1c2Vfc2FmZXRlbnNvcnMlM0RUcnVlKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> UNet2DConditionModel

	repo_id = <span class="hljs-string">"runwayml/stable-diffusion-v1-5"</span>
	model = UNet2DConditionModel.from_pretrained(repo_id, subfolder=<span class="hljs-string">"unet"</span>, use_safetensors=<span class="hljs-literal">True</span>)`}}),al=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFVOZXQyRE1vZGVsJTBBJTBBcmVwb19pZCUyMCUzRCUyMCUyMmdvb2dsZSUyRmRkcG0tY2lmYXIxMC0zMiUyMiUwQW1vZGVsJTIwJTNEJTIwVU5ldDJETW9kZWwuZnJvbV9wcmV0cmFpbmVkKHJlcG9faWQlMkMlMjB1c2Vfc2FmZXRlbnNvcnMlM0RUcnVlKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> UNet2DModel

	repo_id = <span class="hljs-string">"google/ddpm-cifar10-32"</span>
	model = UNet2DModel.from_pretrained(repo_id, use_safetensors=<span class="hljs-literal">True</span>)`}}),ol=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFVOZXQyRENvbmRpdGlvbk1vZGVsJTBBJTBBbW9kZWwlMjAlM0QlMjBVTmV0MkRDb25kaXRpb25Nb2RlbC5mcm9tX3ByZXRyYWluZWQoJTBBJTIwJTIwJTIwJTIwJTIycnVud2F5bWwlMkZzdGFibGUtZGlmZnVzaW9uLXYxLTUlMjIlMkMlMjBzdWJmb2xkZXIlM0QlMjJ1bmV0JTIyJTJDJTIwdmFyaWFudCUzRCUyMm5vbi1lbWElMjIlMkMlMjB1c2Vfc2FmZXRlbnNvcnMlM0RUcnVlJTBBKSUwQW1vZGVsLnNhdmVfcHJldHJhaW5lZCglMjIuJTJGbG9jYWwtdW5ldCUyMiUyQyUyMHZhcmlhbnQlM0QlMjJub24tZW1hJTIyKQ==",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> UNet2DConditionModel

	model = UNet2DConditionModel.from_pretrained(
	<span class="hljs-string">"runwayml/stable-diffusion-v1-5"</span>, subfolder=<span class="hljs-string">"unet"</span>, variant=<span class="hljs-string">"non-ema"</span>, use_safetensors=<span class="hljs-literal">True</span>
	)
	model.save_pretrained(<span class="hljs-string">"./local-unet"</span>, variant=<span class="hljs-string">"non-ema"</span>)`}}),nl=new K({}),il=new Z({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline
	<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> (
	DDPMScheduler,
	DDIMScheduler,
	PNDMScheduler,
	LMSDiscreteScheduler,
	EulerDiscreteScheduler,
	EulerAncestralDiscreteScheduler,
	DPMSolverMultistepScheduler,
	)

	repo_id = <span class="hljs-string">"runwayml/stable-diffusion-v1-5"</span>

	ddpm = DDPMScheduler.from_pretrained(repo_id, subfolder=<span class="hljs-string">"scheduler"</span>)
	ddim = DDIMScheduler.from_pretrained(repo_id, subfolder=<span class="hljs-string">"scheduler"</span>)
	pndm = PNDMScheduler.from_pretrained(repo_id, subfolder=<span class="hljs-string">"scheduler"</span>)
	lms = LMSDiscreteScheduler.from_pretrained(repo_id, subfolder=<span class="hljs-string">"scheduler"</span>)
	euler_anc = EulerAncestralDiscreteScheduler.from_pretrained(repo_id, subfolder=<span class="hljs-string">"scheduler"</span>)
	euler = EulerDiscreteScheduler.from_pretrained(repo_id, subfolder=<span class="hljs-string">"scheduler"</span>)
	dpm = DPMSolverMultistepScheduler.from_pretrained(repo_id, subfolder=<span class="hljs-string">"scheduler"</span>)

	<span class="hljs-comment"># replace \`dpm\` with any of \`ddpm\`, \`ddim\`, \`pndm\`, \`lms\`, \`euler_anc\`, \`euler\`</span>
	pipeline = StableDiffusionPipeline.from_pretrained(repo_id, scheduler=dpm, use_safetensors=<span class="hljs-literal">True</span>)`}}),rl=new K({}),cl=new Z({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMERpZmZ1c2lvblBpcGVsaW5lJTBBJTBBcmVwb19pZCUyMCUzRCUyMCUyMnJ1bndheW1sJTJGc3RhYmxlLWRpZmZ1c2lvbi12MS01JTIyJTBBcGlwZWxpbmUlMjAlM0QlMjBEaWZmdXNpb25QaXBlbGluZS5mcm9tX3ByZXRyYWluZWQocmVwb19pZCUyQyUyMHVzZV9zYWZldGVuc29ycyUzRFRydWUpJTBBcHJpbnQocGlwZWxpbmUp",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> DiffusionPipeline

	repo_id = <span class="hljs-string">"runwayml/stable-diffusion-v1-5"</span>
	pipeline = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=<span class="hljs-literal">True</span>)
	<span class="hljs-built_in">print</span>(pipeline)`}}),yl=new Z({props:{code:"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",highlighted:`StableDiffusionPipeline <span class="hljs-punctuation">{</span>
	<span class="hljs-attr">"feature_extractor"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">[</span>
	<span class="hljs-string">"transformers"</span><span class="hljs-punctuation">,</span>
	<span class="hljs-string">"CLIPImageProcessor"</span>
	<span class="hljs-punctuation">]</span><span class="hljs-punctuation">,</span>
	<span class="hljs-attr">"safety_checker"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">[</span>
	<span class="hljs-string">"stable_diffusion"</span><span class="hljs-punctuation">,</span>
	<span class="hljs-string">"StableDiffusionSafetyChecker"</span>
	<span class="hljs-punctuation">]</span><span class="hljs-punctuation">,</span>
	<span class="hljs-attr">"scheduler"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">[</span>
	<span class="hljs-string">"diffusers"</span><span class="hljs-punctuation">,</span>
	<span class="hljs-string">"PNDMScheduler"</span>
	<span class="hljs-punctuation">]</span><span class="hljs-punctuation">,</span>
	<span class="hljs-attr">"text_encoder"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">[</span>
	<span class="hljs-string">"transformers"</span><span class="hljs-punctuation">,</span>
	<span class="hljs-string">"CLIPTextModel"</span>
	<span class="hljs-punctuation">]</span><span class="hljs-punctuation">,</span>
	<span class="hljs-attr">"tokenizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">[</span>
	<span class="hljs-string">"transformers"</span><span class="hljs-punctuation">,</span>
	<span class="hljs-string">"CLIPTokenizer"</span>
	<span class="hljs-punctuation">]</span><span class="hljs-punctuation">,</span>
	<span class="hljs-attr">"unet"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">[</span>
	<span class="hljs-string">"diffusers"</span><span class="hljs-punctuation">,</span>
	<span class="hljs-string">"UNet2DConditionModel"</span>
	<span class="hljs-punctuation">]</span><span class="hljs-punctuation">,</span>
	<span class="hljs-attr">"vae"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">[</span>
	<span class="hljs-string">"diffusers"</span><span class="hljs-punctuation">,</span>
	<span class="hljs-string">"AutoencoderKL"</span>
	<span class="hljs-punctuation">]</span>
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	\u251C\u2500\u2500 feature_extractor
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	</span>\u251C\u2500\u2500 model_index.<span class="hljs-keyword">json
	</span>\u251C\u2500\u2500 safety_checker
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	</span>\u2502\xA0\xA0 \u2514\u2500\u2500 <span class="hljs-keyword">scheduler_config.json
	</span>\u251C\u2500\u2500 text_encoder
	\u2502\xA0\xA0 \u251C\u2500\u2500 <span class="hljs-built_in">config</span>.<span class="hljs-keyword">json
	</span>\u2502\xA0\xA0 \u2514\u2500\u2500 pytorch_model.<span class="hljs-keyword">bin
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	\u2502\xA0\xA0 \u251C\u2500\u2500 merges.txt
	\u2502\xA0\xA0 \u251C\u2500\u2500 special_tokens_map.<span class="hljs-keyword">json
	</span>\u2502\xA0\xA0 \u251C\u2500\u2500 tokenizer_config.<span class="hljs-keyword">json
	</span>\u2502\xA0\xA0 \u2514\u2500\u2500 vocab.<span class="hljs-keyword">json
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	CLIPTokenizer(
	name_or_path=<span class="hljs-string">"/root/.cache/huggingface/hub/models--runwayml--stable-diffusion-v1-5/snapshots/39593d5650112b4cc580433f6b0435385882d819/tokenizer"</span>,
	vocab_size=<span class="hljs-number">49408</span>,
	model_max_length=<span class="hljs-number">77</span>,
	is_fast=<span class="hljs-literal">False</span>,
	padding_side=<span class="hljs-string">"right"</span>,
	truncation_side=<span class="hljs-string">"right"</span>,
	special_tokens={
	<span class="hljs-string">"bos_token"</span>: AddedToken(<span class="hljs-string">"<\|startoftext\|>"</span>, rstrip=<span class="hljs-literal">False</span>, lstrip=<span class="hljs-literal">False</span>, single_word=<span class="hljs-literal">False</span>, normalized=<span class="hljs-literal">True</span>),
	<span class="hljs-string">"eos_token"</span>: AddedToken(<span class="hljs-string">"<\|endoftext\|>"</span>, rstrip=<span class="hljs-literal">False</span>, lstrip=<span class="hljs-literal">False</span>, single_word=<span class="hljs-literal">False</span>, normalized=<span class="hljs-literal">True</span>),
	<span class="hljs-string">"unk_token"</span>: AddedToken(<span class="hljs-string">"<\|endoftext\|>"</span>, rstrip=<span class="hljs-literal">False</span>, lstrip=<span class="hljs-literal">False</span>, single_word=<span class="hljs-literal">False</span>, normalized=<span class="hljs-literal">True</span>),
	<span class="hljs-string">"pad_token"</span>: <span class="hljs-string">"<\|endoftext\|>"</span>,
	},
	)`}}),vl=new Z({props:{code:"JTdCJTBBJTIwJTIwJTIyX2NsYXNzX25hbWUlMjIlM0ElMjAlMjJTdGFibGVEaWZmdXNpb25QaXBlbGluZSUyMiUyQyUwQSUyMCUyMCUyMl9kaWZmdXNlcnNfdmVyc2lvbiUyMiUzQSUyMCUyMjAuNi4wJTIyJTJDJTBBJTIwJTIwJTIyZmVhdHVyZV9leHRyYWN0b3IlMjIlM0ElMjAlNUIlMEElMjAlMjAlMjAlMjAlMjJ0cmFuc2Zvcm1lcnMlMjIlMkMlMEElMjAlMjAlMjAlMjAlMjJDTElQSW1hZ2VQcm9jZXNzb3IlMjIlMEElMjAlMjAlNUQlMkMlMEElMjAlMjAlMjJzYWZldHlfY2hlY2tlciUyMiUzQSUyMCU1QiUwQSUyMCUyMCUyMCUyMCUyMnN0YWJsZV9kaWZmdXNpb24lMjIlMkMlMEElMjAlMjAlMjAlMjAlMjJTdGFibGVEaWZmdXNpb25TYWZldHlDaGVja2VyJTIyJTBBJTIwJTIwJTVEJTJDJTBBJTIwJTIwJTIyc2NoZWR1bGVyJTIyJTNBJTIwJTVCJTBBJTIwJTIwJTIwJTIwJTIyZGlmZnVzZXJzJTIyJTJDJTBBJTIwJTIwJTIwJTIwJTIyUE5ETVNjaGVkdWxlciUyMiUwQSUyMCUyMCU1RCUyQyUwQSUyMCUyMCUyMnRleHRfZW5jb2RlciUyMiUzQSUyMCU1QiUwQSUyMCUyMCUyMCUyMCUyMnRyYW5zZm9ybWVycyUyMiUyQyUwQSUyMCUyMCUyMCUyMCUyMkNMSVBUZXh0TW9kZWwlMjIlMEElMjAlMjAlNUQlMkMlMEElMjAlMjAlMjJ0b2tlbml6ZXIlMjIlM0ElMjAlNUIlMEElMjAlMjAlMjAlMjAlMjJ0cmFuc2Zvcm1lcnMlMjIlMkMlMEElMjAlMjAlMjAlMjAlMjJDTElQVG9rZW5pemVyJTIyJTBBJTIwJTIwJTVEJTJDJTBBJTIwJTIwJTIydW5ldCUyMiUzQSUyMCU1QiUwQSUyMCUyMCUyMCUyMCUyMmRpZmZ1c2VycyUyMiUyQyUwQSUyMCUyMCUyMCUyMCUyMlVOZXQyRENvbmRpdGlvbk1vZGVsJTIyJTBBJTIwJTIwJTVEJTJDJTBBJTIwJTIwJTIydmFlJTIyJTNBJTIwJTVCJTBBJTIwJTIwJTIwJTIwJTIyZGlmZnVzZXJzJTIyJTJDJTBBJTIwJTIwJTIwJTIwJTIyQXV0b2VuY29kZXJLTCUyMiUwQSUyMCUyMCU1RCUwQSU3RA==",highlighted:`<span class="hljs-punctuation">{</span>
	<span class="hljs-attr">"_class_name"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"StableDiffusionPipeline"</span><span class="hljs-punctuation">,</span>
	<span class="hljs-attr">"_diffusers_version"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"0.6.0"</span><span class="hljs-punctuation">,</span>
	<span class="hljs-attr">"feature_extractor"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">[</span>
	<span class="hljs-string">"transformers"</span><span class="hljs-punctuation">,</span>
	<span class="hljs-string">"CLIPImageProcessor"</span>
	<span class="hljs-punctuation">]</span><span class="hljs-punctuation">,</span>
	<span class="hljs-attr">"safety_checker"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">[</span>
	<span class="hljs-string">"stable_diffusion"</span><span class="hljs-punctuation">,</span>
	<span class="hljs-string">"StableDiffusionSafetyChecker"</span>
	<span class="hljs-punctuation">]</span><span class="hljs-punctuation">,</span>
	<span class="hljs-attr">"scheduler"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">[</span>
	<span class="hljs-string">"diffusers"</span><span class="hljs-punctuation">,</span>
	<span class="hljs-string">"PNDMScheduler"</span>
	<span class="hljs-punctuation">]</span><span class="hljs-punctuation">,</span>
	<span class="hljs-attr">"text_encoder"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">[</span>
	<span class="hljs-string">"transformers"</span><span class="hljs-punctuation">,</span>
	<span class="hljs-string">"CLIPTextModel"</span>
	<span class="hljs-punctuation">]</span><span class="hljs-punctuation">,</span>
	<span class="hljs-attr">"tokenizer"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">[</span>
	<span class="hljs-string">"transformers"</span><span class="hljs-punctuation">,</span>
	<span class="hljs-string">"CLIPTokenizer"</span>
	<span class="hljs-punctuation">]</span><span class="hljs-punctuation">,</span>
	<span class="hljs-attr">"unet"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">[</span>
	<span class="hljs-string">"diffusers"</span><span class="hljs-punctuation">,</span>
	<span class="hljs-string">"UNet2DConditionModel"</span>
	<span class="hljs-punctuation">]</span><span class="hljs-punctuation">,</span>
	<span class="hljs-attr">"vae"</span><span class="hljs-punctuation">:</span> <span class="hljs-punctuation">[</span>
	<span class="hljs-string">"diffusers"</span><span class="hljs-punctuation">,</span>
	<span class="hljs-string">"AutoencoderKL"</span>
	<span class="hljs-punctuation">]</span>
	<span class="hljs-punctuation">}</span>`}}),{c(){u=o("meta"),V=f(),v=o("h1"),E=o("a"),I=o("span"),h(b.$$.fragment),D=f(),g=o("span"),_=t("Load pipelines, models, and schedulers"),U=f(),h(C.$$.fragment),W=f(),G=o("p"),Js=t("Having an easy way to use a diffusion system for inference is essential to \u{1F9E8} Diffusers. Diffusion systems often consist of multiple components like parameterized models, tokenizers, and schedulers that interact in complex ways. 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The "),zt=o("code"),qr=t('subfolder="scheduler"'),Pr=t(" argument is required to load the scheduler configuration from the correct "),Rs=o("a"),Yr=t("subfolder"),Fr=t(" of the pipeline repository."),en=f(),x=o("p"),Hr=t("Then you can pass the new "),Rl=o("a"),Or=t("EulerDiscreteScheduler"),Lr=t(" instance to the "),qt=o("code"),Kr=t("scheduler"),ep=t(" argument in "),Nl=o("a"),sp=t("DiffusionPipeline"),lp=t(":"),sn=f(),h(Ns.$$.fragment),ln=f(),ue=o("h3"),Ne=o("a"),Pt=o("span"),h(Ds.$$.fragment),tp=f(),Yt=o("span"),ap=t("Safety checker"),tn=f(),z=o("p"),op=t("Diffusion models like Stable Diffusion can generate harmful content, which is why \u{1F9E8} Diffusers has a "),$s=o("a"),np=t("safety checker"),ip=t(" to check generated outputs against known hardcoded NSFW content. If you\u2019d like to disable the safety checker for whatever reason, pass "),Ft=o("code"),rp=t("None"),pp=t(" to the "),Ht=o("code"),fp=t("safety_checker"),cp=t(" argument:"),an=f(),h(Bs.$$.fragment),on=f(),he=o("h3"),De=o("a"),Ot=o("span"),h(Gs.$$.fragment),dp=f(),Lt=o("span"),up=t("Reuse components across pipelines"),nn=f(),$e=o("p"),hp=t("You can also reuse the same components in multiple pipelines to avoid loading the weights into RAM twice. Use the "),Dl=o("a"),mp=t("components"),yp=t(" method to save the components:"),rn=f(),h(As.$$.fragment),pn=f(),Be=o("p"),Mp=t("Then you can pass the "),Kt=o("code"),Jp=t("components"),wp=t(" to another pipeline without reloading the weights into RAM:"),fn=f(),h(Ss.$$.fragment),cn=f(),$l=o("p"),vp=t("You can also pass the components individually to the pipeline if you want more flexibility over which components to reuse or disable. For example, to reuse the same components in the text-to-image pipeline, except for the safety checker and feature extractor, in the image-to-image pipeline:"),dn=f(),h(Qs.$$.fragment),un=f(),me=o("h2"),Ge=o("a"),ea=o("span"),h(Xs.$$.fragment),Tp=f(),sa=o("span"),bp=t("Checkpoint variants"),hn=f(),Bl=o("p"),Up=t("A checkpoint variant is usually a checkpoint where it\u2019s weights are:"),mn=f(),Ae=o("ul"),xs=o("li"),jp=t("Stored in a different floating point type for lower precision and lower storage, such as "),zs=o("a"),la=o("code"),Zp=t("torch.float16"),_p=t(", because it only requires half the bandwidth and storage to download. You can\u2019t use this variant if you\u2019re continuing training or using a CPU."),Ep=f(),ta=o("li"),kp=t("Non-exponential mean averaged (EMA) weights which shouldn\u2019t be used for inference. You should use these to continue finetuning a model."),yn=f(),h(Se.$$.fragment),Mn=f(),le=o("p"),Ip=t("Otherwise, a variant is "),aa=o("strong"),gp=t("identical"),Cp=t(" to the original checkpoint. They have exactly the same serialization format (like "),Gl=o("a"),Vp=t("Safetensors"),Wp=t("), model structure, and weights have identical tensor shapes."),Jn=f(),Qe=o("table"),oa=o("thead"),ye=o("tr"),na=o("th"),ia=o("strong"),Rp=t("checkpoint type"),Np=f(),ra=o("th"),pa=o("strong"),Dp=t("weight name"),$p=f(),fa=o("th"),ca=o("strong"),Bp=t("argument for loading weights"),Gp=f(),Me=o("tbody"),Je=o("tr"),da=o("td"),Ap=t("original"),Sp=f(),ua=o("td"),Qp=t("diffusion_pytorch_model.bin"),Xp=f(),wn=o("td"),xp=f(),we=o("tr"),ha=o("td"),zp=t("floating point"),qp=f(),ma=o("td"),Pp=t("diffusion_pytorch_model.fp16.bin"),Yp=f(),qs=o("td"),ya=o("code"),Fp=t("variant"),Hp=t(", "),Ma=o("code"),Op=t("torch_dtype"),Lp=f(),ve=o("tr"),Ja=o("td"),Kp=t("non-EMA"),ef=f(),wa=o("td"),sf=t("diffusion_pytorch_model.non_ema.bin"),lf=f(),va=o("td"),Ta=o("code"),tf=t("variant"),vn=f(),Al=o("p"),af=t("There are two important arguments to know for loading variants:"),Tn=f(),Xe=o("ul"),ba=o("li"),T=o("p"),Ua=o("code"),of=t("torch_dtype"),nf=t(" defines the floating point precision of the loaded checkpoints. For example, if you want to save bandwidth by loading a "),ja=o("code"),rf=t("fp16"),pf=t(" variant, you should specify "),Za=o("code"),ff=t("torch_dtype=torch.float16"),cf=t(" to "),_a=o("em"),df=t("convert the weights"),uf=t(" to "),Ea=o("code"),hf=t("fp16"),mf=t(". Otherwise, the "),ka=o("code"),yf=t("fp16"),Mf=t(" weights are converted to the default "),Ia=o("code"),Jf=t("fp32"),wf=t(" precision. You can also load the original checkpoint without defining the "),ga=o("code"),vf=t("variant"),Tf=t(" argument, and convert it to "),Ca=o("code"),bf=t("fp16"),Uf=t(" with "),Va=o("code"),jf=t("torch_dtype=torch.float16"),Zf=t(". In this case, the default "),Wa=o("code"),_f=t("fp32"),Ef=t(" weights are downloaded first, and then they\u2019re converted to "),Ra=o("code"),kf=t("fp16"),If=t(" after loading."),gf=f(),Na=o("li"),$=o("p"),Da=o("code"),Cf=t("variant"),Vf=t(" defines which files should be loaded from the repository. For example, if you want to load a "),$a=o("code"),Wf=t("non_ema"),Rf=t(" variant from the "),Ps=o("a"),Ba=o("code"),Nf=t("diffusers/stable-diffusion-variants"),Df=t(" repository, you should specify "),Ga=o("code"),$f=t('variant="non_ema"'),Bf=t(" to download the "),Aa=o("code"),Gf=t("non_ema"),Af=t(" files."),bn=f(),h(Ys.$$.fragment),Un=f(),te=o("p"),Sf=t("To save a checkpoint stored in a different floating point type or as a non-EMA variant, use the "),Sl=o("a"),Qf=t("DiffusionPipeline.save_pretrained()"),Xf=t(" method and specify the "),Sa=o("code"),xf=t("variant"),zf=t(" argument. You should try and save a variant to the same folder as the original checkpoint, so you can load both from the same folder:"),jn=f(),h(Fs.$$.fragment),Zn=f(),ae=o("p"),qf=t("If you don\u2019t save the variant to an existing folder, you must specify the "),Qa=o("code"),Pf=t("variant"),Yf=t(" argument otherwise it\u2019ll throw an "),Xa=o("code"),Ff=t("Exception"),Hf=t(" because it can\u2019t find the original checkpoint:"),_n=f(),h(Hs.$$.fragment),En=f(),Ql=o("p"),Of=t(`<!\u2014
	TODO(Patrick) - Make sure to uncomment this part as soon as things are deprecated.`),kn=f(),Te=o("h4"),xe=o("a"),xa=o("span"),h(Os.$$.fragment),Lf=f(),Ls=o("span"),Kf=t("Using "),za=o("code"),ec=t("revision"),sc=t(" to load pipeline variants is deprecated"),In=f(),oe=o("p"),lc=t("Previously the "),qa=o("code"),tc=t("revision"),ac=t(" argument of "),Xl=o("a"),oc=t("DiffusionPipeline.from_pretrained()"),nc=t(` was heavily used to
	load model variants, e.g.:`),gn=f(),h(Ks.$$.fragment),Cn=f(),xl=o("p"),ic=t("However, this behavior is now deprecated since the \u201Crevision\u201D argument should (just as it\u2019s done in GitHub) better be used to load model checkpoints from a specific commit or branch in development."),Vn=f(),ze=o("p"),rc=t("The above example is therefore deprecated and won\u2019t be supported anymore for "),Pa=o("code"),pc=t("diffusers >= 1.0.0"),fc=t("."),Wn=f(),h(qe.$$.fragment),Rn=t(`
	-->
	`),be=o("h2"),Pe=o("a"),Ya=o("span"),h(el.$$.fragment),cc=f(),Fa=o("span"),dc=t("Models"),Nn=f(),ne=o("p"),uc=t("Models are loaded from the "),zl=o("a"),hc=t("ModelMixin.from_pretrained()"),mc=t(" method, which downloads and caches the latest version of the model weights and configurations. If the latest files are available in the local cache, "),ql=o("a"),yc=t("from_pretrained()"),Mc=t(" reuses files in the cache instead of redownloading them."),Dn=f(),q=o("p"),Jc=t("Models can be loaded from a subfolder with the "),Ha=o("code"),wc=t("subfolder"),vc=t(" argument. For example, the model weights for "),Oa=o("code"),Tc=t("runwayml/stable-diffusion-v1-5"),bc=t(" are stored in the "),sl=o("a"),La=o("code"),Uc=t("unet"),jc=t(" subfolder:"),$n=f(),h(ll.$$.fragment),Bn=f(),Ye=o("p"),Zc=t("Or directly from a repository\u2019s "),tl=o("a"),_c=t("directory"),Ec=t(":"),Gn=f(),h(al.$$.fragment),An=f(),P=o("p"),kc=t("You can also load and save model variants by specifying the "),Ka=o("code"),Ic=t("variant"),gc=t(" argument in "),Pl=o("a"),Cc=t("ModelMixin.from_pretrained()"),Vc=t(" and "),Yl=o("a"),Wc=t("ModelMixin.save_pretrained()"),Rc=t(":"),Sn=f(),h(ol.$$.fragment),Qn=f(),Ue=o("h2"),Fe=o("a"),eo=o("span"),h(nl.$$.fragment),Nc=f(),so=o("span"),Dc=t("Schedulers"),Xn=f(),Y=o("p"),$c=t("Schedulers are loaded from the "),Fl=o("a"),Bc=t("SchedulerMixin.from_pretrained()"),Gc=t(" method, and unlike models, schedulers are "),lo=o("strong"),Ac=t("not parameterized"),Sc=t(" or "),to=o("strong"),Qc=t("trained"),Xc=t("; they are defined by a configuration file."),xn=f(),He=o("p"),xc=t(`Loading schedulers does not consume any significant amount of memory and the same configuration file can be used for a variety of different schedulers.
	For example, the following schedulers are compatible with `),Hl=o("a"),zc=t("StableDiffusionPipeline"),qc=t(" which means you can load the same scheduler configuration file in any of these classes:"),zn=f(),h(il.$$.fragment),qn=f(),je=o("h2"),Oe=o("a"),ao=o("span"),h(rl.$$.fragment),Pc=f(),oo=o("span"),Yc=t("DiffusionPipeline explained"),Pn=f(),Le=o("p"),Fc=t("As a class method, "),Ol=o("a"),Hc=t("DiffusionPipeline.from_pretrained()"),Oc=t(" is responsible for two things:"),Yn=f(),Ke=o("ul"),pl=o("li"),Lc=t("Download the latest version of the folder structure required for inference and cache it. If the latest folder structure is available in the local cache, "),Ll=o("a"),Kc=t("DiffusionPipeline.from_pretrained()"),ed=t(" reuses the cache and won\u2019t redownload the files."),sd=f(),Ze=o("li"),ld=t("Load the cached weights into the correct pipeline "),Kl=o("a"),td=t("class"),ad=t(" - retrieved from the "),no=o("code"),od=t("model_index.json"),nd=t(" file - and return an instance of it."),Fn=f(),ie=o("p"),id=t("The pipelines underlying folder structure corresponds directly with their class instances. For example, the "),et=o("a"),rd=t("StableDiffusionPipeline"),pd=t(" corresponds to the folder structure in "),fl=o("a"),io=o("code"),fd=t("runwayml/stable-diffusion-v1-5"),cd=t("."),Hn=f(),h(cl.$$.fragment),On=f(),es=o("p"),dd=t("You\u2019ll see pipeline is an instance of "),st=o("a"),ud=t("StableDiffusionPipeline"),hd=t(", which consists of seven components:"),Ln=f(),k=o("ul"),ss=o("li"),ro=o("code"),md=t('"feature_extractor"'),yd=t(": a "),dl=o("a"),Md=t("CLIPFeatureExtractor"),Jd=t(" from \u{1F917} Transformers."),wd=f(),ls=o("li"),po=o("code"),vd=t('"safety_checker"'),Td=t(": a "),ul=o("a"),bd=t("component"),Ud=t(" for screening against harmful content."),jd=f(),ts=o("li"),fo=o("code"),Zd=t('"scheduler"'),_d=t(": an instance of "),lt=o("a"),Ed=t("PNDMScheduler"),kd=t("."),Id=f(),as=o("li"),co=o("code"),gd=t('"text_encoder"'),Cd=t(": a "),hl=o("a"),Vd=t("CLIPTextModel"),Wd=t(" from \u{1F917} Transformers."),Rd=f(),os=o("li"),uo=o("code"),Nd=t('"tokenizer"'),Dd=t(": a "),ml=o("a"),$d=t("CLIPTokenizer"),Bd=t(" from \u{1F917} Transformers."),Gd=f(),ns=o("li"),ho=o("code"),Ad=t('"unet"'),Sd=t(": an instance of "),tt=o("a"),Qd=t("UNet2DConditionModel"),Xd=t("."),xd=f(),is=o("li"),mo=o("code"),zd=t('"vae"'),qd=t(" an instance of "),at=o("a"),Pd=t("AutoencoderKL"),Yd=t("."),Kn=f(),h(yl.$$.fragment),ei=f(),rs=o("p"),Fd=t("Compare the components of the pipeline instance to the "),Ml=o("a"),yo=o("code"),Hd=t("runwayml/stable-diffusion-v1-5"),Od=t(" folder structure, and you\u2019ll see there is a separate folder for each of the components in the repository:"),si=f(),h(Jl.$$.fragment),li=f(),ot=o("p"),Ld=t("You can access each of the components of the pipeline as an attribute to view its configuration:"),ti=f(),h(wl.$$.fragment),ai=f(),re=o("p"),Kd=t("Every pipeline expects a "),Mo=o("code"),eu=t("model_index.json"),su=t(" file that tells the "),nt=o("a"),lu=t("DiffusionPipeline"),tu=t(":"),oi=f(),pe=o("ul"),it=o("li"),au=t("which pipeline class to load from "),Jo=o("code"),ou=t("_class_name"),nu=f(),rt=o("li"),iu=t("which version of \u{1F9E8} Diffusers was used to create the model in "),wo=o("code"),ru=t("_diffusers_version"),pu=f(),O=o("li"),fu=t("what components from which library are stored in the subfolders ("),vo=o("code"),cu=t("name"),du=t(" corresponds to the component and subfolder name, "),To=o("code"),uu=t("library"),hu=t(" corresponds to the name of the library to load the class from, and "),bo=o("code"),mu=t("class"),yu=t(" corresponds to the class name)"),ni=f(),h(vl.$$.fragment),this.h()},l(e){const r=Vy('[data-svelte="svelte-1phssyn"]',document.head);u=n(r,"META",{name:!0,content:!0}),r.forEach(l),V=c(e),v=n(e,"H1",{class:!0});var Tl=i(v);E=n(Tl,"A",{id:!0,class:!0,href:!0});var Uo=i(E);I=n(Uo,"SPAN",{});var jo=i(I);m(b.$$.fragment,jo),jo.forEach(l),Uo.forEach(l),D=c(Tl),g=n(Tl,"SPAN",{});var vu=i(g);_=a(vu,"Load pipelines, models, and schedulers"),vu.forEach(l),Tl.forEach(l),U=c(e),m(C.$$.fragment,e),W=c(e),G=n(e,"P",{});var ri=i(G);Js=a(ri,"Having an easy way to use a diffusion system for inference is essential to \u{1F9E8} Diffusers. Diffusion systems often consist of multiple components like parameterized models, tokenizers, and schedulers that interact in complex ways. That is why we designed the "),A=n(ri,"A",{href:!0});var Tu=i(A);R=a(Tu,"DiffusionPipeline"),Tu.forEach(l),bl=a(ri," to wrap the complexity of the entire diffusion system into an easy-to-use API, while remaining flexible enough to be adapted for other use cases, such as loading each component individually as building blocks to assemble your own diffusion system."),ri.forEach(l),ws=c(e),H=n(e,"P",{});var pi=i(H);Ul=a(pi,"Everything you need for inference or training is accessible with the "),Zt=n(pi,"CODE",{});var bu=i(Zt);Ai=a(bu,"from_pretrained()"),bu.forEach(l),Si=a(pi," method."),pi.forEach(l),Vo=c(e),jl=n(e,"P",{});var Uu=i(jl);Qi=a(Uu,"This guide will show you how to load:"),Uu.forEach(l),Wo=c(e),S=n(e,"UL",{});var ps=i(S);_t=n(ps,"LI",{});var ju=i(_t);Xi=a(ju,"pipelines from the Hub and locally"),ju.forEach(l),xi=c(ps),Et=n(ps,"LI",{});var Zu=i(Et);zi=a(Zu,"different components into a pipeline"),Zu.forEach(l),qi=c(ps),kt=n(ps,"LI",{});var _u=i(kt);Pi=a(_u,"checkpoint variants such as different floating point types or non-exponential mean averaged (EMA) weights"),_u.forEach(l),Yi=c(ps),It=n(ps,"LI",{});var Eu=i(It);Fi=a(Eu,"models and schedulers"),Eu.forEach(l),ps.forEach(l),Ro=c(e),fe=n(e,"H2",{class:!0});var fi=i(fe);Ee=n(fi,"A",{id:!0,class:!0,href:!0});var ku=i(Ee);gt=n(ku,"SPAN",{});var Iu=i(gt);m(vs.$$.fragment,Iu),Iu.forEach(l),ku.forEach(l),Hi=c(fi),Ct=n(fi,"SPAN",{});var gu=i(Ct);Oi=a(gu,"Diffusion Pipeline"),gu.forEach(l),fi.forEach(l),No=c(e),m(ke.$$.fragment,e),Do=c(e),Q=n(e,"P",{});var fs=i(Q);Li=a(fs,"The "),Zl=n(fs,"A",{href:!0});var Cu=i(Zl);Ki=a(Cu,"DiffusionPipeline"),Cu.forEach(l),er=a(fs," class is the simplest and most generic way to load any diffusion model from the "),Ts=n(fs,"A",{href:!0,rel:!0});var Vu=i(Ts);sr=a(Vu,"Hub"),Vu.forEach(l),lr=a(fs,". The "),_l=n(fs,"A",{href:!0});var Wu=i(_l);tr=a(Wu,"DiffusionPipeline.from_pretrained()"),Wu.forEach(l),ar=a(fs," method automatically detects the correct pipeline class from the checkpoint, downloads and caches all the required configuration and weight files, and returns a pipeline instance ready for inference."),fs.forEach(l),$o=c(e),m(bs.$$.fragment,e),Bo=c(e),Ie=n(e,"P",{});var ci=i(Ie);or=a(ci,"You can also load a checkpoint with it\u2019s specific pipeline class. The example above loaded a Stable Diffusion model; to get the same result, use the "),El=n(ci,"A",{href:!0});var Ru=i(El);nr=a(Ru,"StableDiffusionPipeline"),Ru.forEach(l),ir=a(ci," class:"),ci.forEach(l),Go=c(e),m(Us.$$.fragment,e),Ao=c(e),ee=n(e,"P",{});var pt=i(ee);rr=a(pt,"A checkpoint (such as "),js=n(pt,"A",{href:!0,rel:!0});var Nu=i(js);Vt=n(Nu,"CODE",{});var Du=i(Vt);pr=a(Du,"CompVis/stable-diffusion-v1-4"),Du.forEach(l),Nu.forEach(l),fr=a(pt," or "),Zs=n(pt,"A",{href:!0,rel:!0});var $u=i(Zs);Wt=n($u,"CODE",{});var Bu=i(Wt);cr=a(Bu,"runwayml/stable-diffusion-v1-5"),Bu.forEach(l),$u.forEach(l),dr=a(pt,") may also be used for more than one task, like text-to-image or image-to-image. To differentiate what task you want to use the checkpoint for, you have to load it directly with it\u2019s corresponding task-specific pipeline class:"),pt.forEach(l),So=c(e),m(_s.$$.fragment,e),Qo=c(e),ce=n(e,"H3",{class:!0});var di=i(ce);ge=n(di,"A",{id:!0,class:!0,href:!0});var Gu=i(ge);Rt=n(Gu,"SPAN",{});var Au=i(Rt);m(Es.$$.fragment,Au),Au.forEach(l),Gu.forEach(l),ur=c(di),Nt=n(di,"SPAN",{});var Su=i(Nt);hr=a(Su,"Local pipeline"),Su.forEach(l),di.forEach(l),Xo=c(e),X=n(e,"P",{});var cs=i(X);mr=a(cs,"To load a diffusion pipeline locally, use "),ks=n(cs,"A",{href:!0,rel:!0});var Qu=i(ks);Dt=n(Qu,"CODE",{});var Xu=i(Dt);yr=a(Xu,"git-lfs"),Xu.forEach(l),Qu.forEach(l),Mr=a(cs," to manually download the checkpoint (in this case, "),Is=n(cs,"A",{href:!0,rel:!0});var xu=i(Is);$t=n(xu,"CODE",{});var zu=i($t);Jr=a(zu,"runwayml/stable-diffusion-v1-5"),zu.forEach(l),xu.forEach(l),wr=a(cs,") to your local disk. This creates a local folder, "),Bt=n(cs,"CODE",{});var qu=i(Bt);vr=a(qu,"./stable-diffusion-v1-5"),qu.forEach(l),Tr=a(cs,", on your disk:"),cs.forEach(l),xo=c(e),m(gs.$$.fragment,e),zo=c(e),Ce=n(e,"P",{});var ui=i(Ce);br=a(ui,"Then pass the local path to "),kl=n(ui,"A",{href:!0});var Pu=i(kl);Ur=a(Pu,"from_pretrained()"),Pu.forEach(l),jr=a(ui,":"),ui.forEach(l),qo=c(e),m(Cs.$$.fragment,e),Po=c(e),Ve=n(e,"P",{});var hi=i(Ve);Zr=a(hi,"The "),Il=n(hi,"A",{href:!0});var Yu=i(Il);_r=a(Yu,"from_pretrained()"),Yu.forEach(l),Er=a(hi," method won\u2019t download any files from the Hub when it detects a local path, but this also means it won\u2019t download and cache the latest changes to a checkpoint."),hi.forEach(l),Yo=c(e),de=n(e,"H3",{class:!0});var mi=i(de);We=n(mi,"A",{id:!0,class:!0,href:!0});var Fu=i(We);Gt=n(Fu,"SPAN",{});var Hu=i(Gt);m(Vs.$$.fragment,Hu),Hu.forEach(l),Fu.forEach(l),kr=c(mi),At=n(mi,"SPAN",{});var Ou=i(At);Ir=a(Ou,"Swap components in a pipeline"),Ou.forEach(l),mi.forEach(l),Fo=c(e),gl=n(e,"P",{});var Lu=i(gl);gr=a(Lu,"You can customize the default components of any pipeline with another compatible component. Customization is important because:"),Lu.forEach(l),Ho=c(e),se=n(e,"UL",{});var ft=i(se);St=n(ft,"LI",{});var Ku=i(St);Cr=a(Ku,"Changing the scheduler is important for exploring the trade-off between generation speed and quality."),Ku.forEach(l),Vr=c(ft),Qt=n(ft,"LI",{});var eh=i(Qt);Wr=a(eh,"Different components of a model are typically trained independently and you can swap out a component with a better-performing one."),eh.forEach(l),Rr=c(ft),Xt=n(ft,"LI",{});var sh=i(Xt);Nr=a(sh,"During finetuning, usually only some components - like the UNet or text encoder - are trained."),sh.forEach(l),ft.forEach(l),Oo=c(e),Re=n(e,"P",{});var yi=i(Re);Dr=a(yi,"To find out which schedulers are compatible for customization, you can use the "),xt=n(yi,"CODE",{});var lh=i(xt);$r=a(lh,"compatibles"),lh.forEach(l),Br=a(yi," method:"),yi.forEach(l),Lo=c(e),m(Ws.$$.fragment,e),Ko=c(e),N=n(e,"P",{});var F=i(N);Gr=a(F,"Let\u2019s use the "),Cl=n(F,"A",{href:!0});var th=i(Cl);Ar=a(th,"SchedulerMixin.from_pretrained()"),th.forEach(l),Sr=a(F," method to replace the default "),Vl=n(F,"A",{href:!0});var ah=i(Vl);Qr=a(ah,"PNDMScheduler"),ah.forEach(l),Xr=a(F," with a more performant scheduler, "),Wl=n(F,"A",{href:!0});var oh=i(Wl);xr=a(oh,"EulerDiscreteScheduler"),oh.forEach(l),zr=a(F,". The "),zt=n(F,"CODE",{});var nh=i(zt);qr=a(nh,'subfolder="scheduler"'),nh.forEach(l),Pr=a(F," argument is required to load the scheduler configuration from the correct "),Rs=n(F,"A",{href:!0,rel:!0});var ih=i(Rs);Yr=a(ih,"subfolder"),ih.forEach(l),Fr=a(F," of the pipeline repository."),F.forEach(l),en=c(e),x=n(e,"P",{});var ds=i(x);Hr=a(ds,"Then you can pass the new "),Rl=n(ds,"A",{href:!0});var rh=i(Rl);Or=a(rh,"EulerDiscreteScheduler"),rh.forEach(l),Lr=a(ds," instance to the "),qt=n(ds,"CODE",{});var ph=i(qt);Kr=a(ph,"scheduler"),ph.forEach(l),ep=a(ds," argument in "),Nl=n(ds,"A",{href:!0});var fh=i(Nl);sp=a(fh,"DiffusionPipeline"),fh.forEach(l),lp=a(ds,":"),ds.forEach(l),sn=c(e),m(Ns.$$.fragment,e),ln=c(e),ue=n(e,"H3",{class:!0});var Mi=i(ue);Ne=n(Mi,"A",{id:!0,class:!0,href:!0});var ch=i(Ne);Pt=n(ch,"SPAN",{});var dh=i(Pt);m(Ds.$$.fragment,dh),dh.forEach(l),ch.forEach(l),tp=c(Mi),Yt=n(Mi,"SPAN",{});var uh=i(Yt);ap=a(uh,"Safety checker"),uh.forEach(l),Mi.forEach(l),tn=c(e),z=n(e,"P",{});var us=i(z);op=a(us,"Diffusion models like Stable Diffusion can generate harmful content, which is why \u{1F9E8} Diffusers has a "),$s=n(us,"A",{href:!0,rel:!0});var hh=i($s);np=a(hh,"safety checker"),hh.forEach(l),ip=a(us," to check generated outputs against known hardcoded NSFW content. If you\u2019d like to disable the safety checker for whatever reason, pass "),Ft=n(us,"CODE",{});var mh=i(Ft);rp=a(mh,"None"),mh.forEach(l),pp=a(us," to the "),Ht=n(us,"CODE",{});var yh=i(Ht);fp=a(yh,"safety_checker"),yh.forEach(l),cp=a(us," argument:"),us.forEach(l),an=c(e),m(Bs.$$.fragment,e),on=c(e),he=n(e,"H3",{class:!0});var Ji=i(he);De=n(Ji,"A",{id:!0,class:!0,href:!0});var Mh=i(De);Ot=n(Mh,"SPAN",{});var Jh=i(Ot);m(Gs.$$.fragment,Jh),Jh.forEach(l),Mh.forEach(l),dp=c(Ji),Lt=n(Ji,"SPAN",{});var wh=i(Lt);up=a(wh,"Reuse components across pipelines"),wh.forEach(l),Ji.forEach(l),nn=c(e),$e=n(e,"P",{});var wi=i($e);hp=a(wi,"You can also reuse the same components in multiple pipelines to avoid loading the weights into RAM twice. Use the "),Dl=n(wi,"A",{href:!0});var vh=i(Dl);mp=a(vh,"components"),vh.forEach(l),yp=a(wi," method to save the components:"),wi.forEach(l),rn=c(e),m(As.$$.fragment,e),pn=c(e),Be=n(e,"P",{});var vi=i(Be);Mp=a(vi,"Then you can pass the "),Kt=n(vi,"CODE",{});var Th=i(Kt);Jp=a(Th,"components"),Th.forEach(l),wp=a(vi," to another pipeline without reloading the weights into RAM:"),vi.forEach(l),fn=c(e),m(Ss.$$.fragment,e),cn=c(e),$l=n(e,"P",{});var bh=i($l);vp=a(bh,"You can also pass the components individually to the pipeline if you want more flexibility over which components to reuse or disable. For example, to reuse the same components in the text-to-image pipeline, except for the safety checker and feature extractor, in the image-to-image pipeline:"),bh.forEach(l),dn=c(e),m(Qs.$$.fragment,e),un=c(e),me=n(e,"H2",{class:!0});var Ti=i(me);Ge=n(Ti,"A",{id:!0,class:!0,href:!0});var Uh=i(Ge);ea=n(Uh,"SPAN",{});var jh=i(ea);m(Xs.$$.fragment,jh),jh.forEach(l),Uh.forEach(l),Tp=c(Ti),sa=n(Ti,"SPAN",{});var Zh=i(sa);bp=a(Zh,"Checkpoint variants"),Zh.forEach(l),Ti.forEach(l),hn=c(e),Bl=n(e,"P",{});var _h=i(Bl);Up=a(_h,"A checkpoint variant is usually a checkpoint where it\u2019s weights are:"),_h.forEach(l),mn=c(e),Ae=n(e,"UL",{});var bi=i(Ae);xs=n(bi,"LI",{});var Ui=i(xs);jp=a(Ui,"Stored in a different floating point type for lower precision and lower storage, such as "),zs=n(Ui,"A",{href:!0,rel:!0});var Eh=i(zs);la=n(Eh,"CODE",{});var kh=i(la);Zp=a(kh,"torch.float16"),kh.forEach(l),Eh.forEach(l),_p=a(Ui,", because it only requires half the bandwidth and storage to download. You can\u2019t use this variant if you\u2019re continuing training or using a CPU."),Ui.forEach(l),Ep=c(bi),ta=n(bi,"LI",{});var Ih=i(ta);kp=a(Ih,"Non-exponential mean averaged (EMA) weights which shouldn\u2019t be used for inference. You should use these to continue finetuning a model."),Ih.forEach(l),bi.forEach(l),yn=c(e),m(Se.$$.fragment,e),Mn=c(e),le=n(e,"P",{});var ct=i(le);Ip=a(ct,"Otherwise, a variant is "),aa=n(ct,"STRONG",{});var gh=i(aa);gp=a(gh,"identical"),gh.forEach(l),Cp=a(ct," to the original checkpoint. They have exactly the same serialization format (like "),Gl=n(ct,"A",{href:!0});var Ch=i(Gl);Vp=a(Ch,"Safetensors"),Ch.forEach(l),Wp=a(ct,"), model structure, and weights have identical tensor shapes."),ct.forEach(l),Jn=c(e),Qe=n(e,"TABLE",{});var ji=i(Qe);oa=n(ji,"THEAD",{});var Vh=i(oa);ye=n(Vh,"TR",{});var dt=i(ye);na=n(dt,"TH",{});var Wh=i(na);ia=n(Wh,"STRONG",{});var Rh=i(ia);Rp=a(Rh,"checkpoint type"),Rh.forEach(l),Wh.forEach(l),Np=c(dt),ra=n(dt,"TH",{});var Nh=i(ra);pa=n(Nh,"STRONG",{});var Dh=i(pa);Dp=a(Dh,"weight name"),Dh.forEach(l),Nh.forEach(l),$p=c(dt),fa=n(dt,"TH",{});var $h=i(fa);ca=n($h,"STRONG",{});var Bh=i(ca);Bp=a(Bh,"argument for loading weights"),Bh.forEach(l),$h.forEach(l),dt.forEach(l),Vh.forEach(l),Gp=c(ji),Me=n(ji,"TBODY",{});var ut=i(Me);Je=n(ut,"TR",{});var ht=i(Je);da=n(ht,"TD",{});var Gh=i(da);Ap=a(Gh,"original"),Gh.forEach(l),Sp=c(ht),ua=n(ht,"TD",{});var Ah=i(ua);Qp=a(Ah,"diffusion_pytorch_model.bin"),Ah.forEach(l),Xp=c(ht),wn=n(ht,"TD",{}),i(wn).forEach(l),ht.forEach(l),xp=c(ut),we=n(ut,"TR",{});var mt=i(we);ha=n(mt,"TD",{});var Sh=i(ha);zp=a(Sh,"floating point"),Sh.forEach(l),qp=c(mt),ma=n(mt,"TD",{});var Qh=i(ma);Pp=a(Qh,"diffusion_pytorch_model.fp16.bin"),Qh.forEach(l),Yp=c(mt),qs=n(mt,"TD",{});var Zi=i(qs);ya=n(Zi,"CODE",{});var Xh=i(ya);Fp=a(Xh,"variant"),Xh.forEach(l),Hp=a(Zi,", "),Ma=n(Zi,"CODE",{});var xh=i(Ma);Op=a(xh,"torch_dtype"),xh.forEach(l),Zi.forEach(l),mt.forEach(l),Lp=c(ut),ve=n(ut,"TR",{});var yt=i(ve);Ja=n(yt,"TD",{});var zh=i(Ja);Kp=a(zh,"non-EMA"),zh.forEach(l),ef=c(yt),wa=n(yt,"TD",{});var qh=i(wa);sf=a(qh,"diffusion_pytorch_model.non_ema.bin"),qh.forEach(l),lf=c(yt),va=n(yt,"TD",{});var Ph=i(va);Ta=n(Ph,"CODE",{});var Yh=i(Ta);tf=a(Yh,"variant"),Yh.forEach(l),Ph.forEach(l),yt.forEach(l),ut.forEach(l),ji.forEach(l),vn=c(e),Al=n(e,"P",{});var Fh=i(Al);af=a(Fh,"There are two important arguments to know for loading variants:"),Fh.forEach(l),Tn=c(e),Xe=n(e,"UL",{});var _i=i(Xe);ba=n(_i,"LI",{});var Hh=i(ba);T=n(Hh,"P",{});var j=i(T);Ua=n(j,"CODE",{});var Oh=i(Ua);of=a(Oh,"torch_dtype"),Oh.forEach(l),nf=a(j," defines the floating point precision of the loaded checkpoints. For example, if you want to save bandwidth by loading a "),ja=n(j,"CODE",{});var Lh=i(ja);rf=a(Lh,"fp16"),Lh.forEach(l),pf=a(j," variant, you should specify "),Za=n(j,"CODE",{});var Kh=i(Za);ff=a(Kh,"torch_dtype=torch.float16"),Kh.forEach(l),cf=a(j," to "),_a=n(j,"EM",{});var em=i(_a);df=a(em,"convert the weights"),em.forEach(l),uf=a(j," to "),Ea=n(j,"CODE",{});var sm=i(Ea);hf=a(sm,"fp16"),sm.forEach(l),mf=a(j,". Otherwise, the "),ka=n(j,"CODE",{});var lm=i(ka);yf=a(lm,"fp16"),lm.forEach(l),Mf=a(j," weights are converted to the default "),Ia=n(j,"CODE",{});var tm=i(Ia);Jf=a(tm,"fp32"),tm.forEach(l),wf=a(j," precision. You can also load the original checkpoint without defining the "),ga=n(j,"CODE",{});var am=i(ga);vf=a(am,"variant"),am.forEach(l),Tf=a(j," argument, and convert it to "),Ca=n(j,"CODE",{});var om=i(Ca);bf=a(om,"fp16"),om.forEach(l),Uf=a(j," with "),Va=n(j,"CODE",{});var nm=i(Va);jf=a(nm,"torch_dtype=torch.float16"),nm.forEach(l),Zf=a(j,". In this case, the default "),Wa=n(j,"CODE",{});var im=i(Wa);_f=a(im,"fp32"),im.forEach(l),Ef=a(j," weights are downloaded first, and then they\u2019re converted to "),Ra=n(j,"CODE",{});var rm=i(Ra);kf=a(rm,"fp16"),rm.forEach(l),If=a(j," after loading."),j.forEach(l),Hh.forEach(l),gf=c(_i),Na=n(_i,"LI",{});var pm=i(Na);$=n(pm,"P",{});var L=i($);Da=n(L,"CODE",{});var fm=i(Da);Cf=a(fm,"variant"),fm.forEach(l),Vf=a(L," defines which files should be loaded from the repository. For example, if you want to load a "),$a=n(L,"CODE",{});var cm=i($a);Wf=a(cm,"non_ema"),cm.forEach(l),Rf=a(L," variant from the "),Ps=n(L,"A",{href:!0,rel:!0});var dm=i(Ps);Ba=n(dm,"CODE",{});var um=i(Ba);Nf=a(um,"diffusers/stable-diffusion-variants"),um.forEach(l),dm.forEach(l),Df=a(L," repository, you should specify "),Ga=n(L,"CODE",{});var hm=i(Ga);$f=a(hm,'variant="non_ema"'),hm.forEach(l),Bf=a(L," to download the "),Aa=n(L,"CODE",{});var mm=i(Aa);Gf=a(mm,"non_ema"),mm.forEach(l),Af=a(L," files."),L.forEach(l),pm.forEach(l),_i.forEach(l),bn=c(e),m(Ys.$$.fragment,e),Un=c(e),te=n(e,"P",{});var Mt=i(te);Sf=a(Mt,"To save a checkpoint stored in a different floating point type or as a non-EMA variant, use the "),Sl=n(Mt,"A",{href:!0});var ym=i(Sl);Qf=a(ym,"DiffusionPipeline.save_pretrained()"),ym.forEach(l),Xf=a(Mt," method and specify the "),Sa=n(Mt,"CODE",{});var Mm=i(Sa);xf=a(Mm,"variant"),Mm.forEach(l),zf=a(Mt," argument. You should try and save a variant to the same folder as the original checkpoint, so you can load both from the same folder:"),Mt.forEach(l),jn=c(e),m(Fs.$$.fragment,e),Zn=c(e),ae=n(e,"P",{});var Jt=i(ae);qf=a(Jt,"If you don\u2019t save the variant to an existing folder, you must specify the "),Qa=n(Jt,"CODE",{});var Jm=i(Qa);Pf=a(Jm,"variant"),Jm.forEach(l),Yf=a(Jt," argument otherwise it\u2019ll throw an "),Xa=n(Jt,"CODE",{});var wm=i(Xa);Ff=a(wm,"Exception"),wm.forEach(l),Hf=a(Jt," because it can\u2019t find the original checkpoint:"),Jt.forEach(l),_n=c(e),m(Hs.$$.fragment,e),En=c(e),Ql=n(e,"P",{});var vm=i(Ql);Of=a(vm,`<!\u2014
	TODO(Patrick) - Make sure to uncomment this part as soon as things are deprecated.`),vm.forEach(l),kn=c(e),Te=n(e,"H4",{class:!0});var Ei=i(Te);xe=n(Ei,"A",{id:!0,class:!0,href:!0});var Tm=i(xe);xa=n(Tm,"SPAN",{});var bm=i(xa);m(Os.$$.fragment,bm),bm.forEach(l),Tm.forEach(l),Lf=c(Ei),Ls=n(Ei,"SPAN",{});var ki=i(Ls);Kf=a(ki,"Using "),za=n(ki,"CODE",{});var Um=i(za);ec=a(Um,"revision"),Um.forEach(l),sc=a(ki," to load pipeline variants is deprecated"),ki.forEach(l),Ei.forEach(l),In=c(e),oe=n(e,"P",{});var wt=i(oe);lc=a(wt,"Previously the "),qa=n(wt,"CODE",{});var jm=i(qa);tc=a(jm,"revision"),jm.forEach(l),ac=a(wt," argument of "),Xl=n(wt,"A",{href:!0});var Zm=i(Xl);oc=a(Zm,"DiffusionPipeline.from_pretrained()"),Zm.forEach(l),nc=a(wt,` was heavily used to
	load model variants, e.g.:`),wt.forEach(l),gn=c(e),m(Ks.$$.fragment,e),Cn=c(e),xl=n(e,"P",{});var _m=i(xl);ic=a(_m,"However, this behavior is now deprecated since the \u201Crevision\u201D argument should (just as it\u2019s done in GitHub) better be used to load model checkpoints from a specific commit or branch in development."),_m.forEach(l),Vn=c(e),ze=n(e,"P",{});var Ii=i(ze);rc=a(Ii,"The above example is therefore deprecated and won\u2019t be supported anymore for "),Pa=n(Ii,"CODE",{});var Em=i(Pa);pc=a(Em,"diffusers >= 1.0.0"),Em.forEach(l),fc=a(Ii,"."),Ii.forEach(l),Wn=c(e),m(qe.$$.fragment,e),Rn=a(e,`
	-->
	`),be=n(e,"H2",{class:!0});var gi=i(be);Pe=n(gi,"A",{id:!0,class:!0,href:!0});var km=i(Pe);Ya=n(km,"SPAN",{});var Im=i(Ya);m(el.$$.fragment,Im),Im.forEach(l),km.forEach(l),cc=c(gi),Fa=n(gi,"SPAN",{});var gm=i(Fa);dc=a(gm,"Models"),gm.forEach(l),gi.forEach(l),Nn=c(e),ne=n(e,"P",{});var vt=i(ne);uc=a(vt,"Models are loaded from the "),zl=n(vt,"A",{href:!0});var Cm=i(zl);hc=a(Cm,"ModelMixin.from_pretrained()"),Cm.forEach(l),mc=a(vt," method, which downloads and caches the latest version of the model weights and configurations. If the latest files are available in the local cache, "),ql=n(vt,"A",{href:!0});var Vm=i(ql);yc=a(Vm,"from_pretrained()"),Vm.forEach(l),Mc=a(vt," reuses files in the cache instead of redownloading them."),vt.forEach(l),Dn=c(e),q=n(e,"P",{});var hs=i(q);Jc=a(hs,"Models can be loaded from a subfolder with the "),Ha=n(hs,"CODE",{});var Wm=i(Ha);wc=a(Wm,"subfolder"),Wm.forEach(l),vc=a(hs," argument. For example, the model weights for "),Oa=n(hs,"CODE",{});var Rm=i(Oa);Tc=a(Rm,"runwayml/stable-diffusion-v1-5"),Rm.forEach(l),bc=a(hs," are stored in the "),sl=n(hs,"A",{href:!0,rel:!0});var Nm=i(sl);La=n(Nm,"CODE",{});var Dm=i(La);Uc=a(Dm,"unet"),Dm.forEach(l),Nm.forEach(l),jc=a(hs," subfolder:"),hs.forEach(l),$n=c(e),m(ll.$$.fragment,e),Bn=c(e),Ye=n(e,"P",{});var Ci=i(Ye);Zc=a(Ci,"Or directly from a repository\u2019s "),tl=n(Ci,"A",{href:!0,rel:!0});var $m=i(tl);_c=a($m,"directory"),$m.forEach(l),Ec=a(Ci,":"),Ci.forEach(l),Gn=c(e),m(al.$$.fragment,e),An=c(e),P=n(e,"P",{});var ms=i(P);kc=a(ms,"You can also load and save model variants by specifying the "),Ka=n(ms,"CODE",{});var Bm=i(Ka);Ic=a(Bm,"variant"),Bm.forEach(l),gc=a(ms," argument in "),Pl=n(ms,"A",{href:!0});var Gm=i(Pl);Cc=a(Gm,"ModelMixin.from_pretrained()"),Gm.forEach(l),Vc=a(ms," and "),Yl=n(ms,"A",{href:!0});var Am=i(Yl);Wc=a(Am,"ModelMixin.save_pretrained()"),Am.forEach(l),Rc=a(ms,":"),ms.forEach(l),Sn=c(e),m(ol.$$.fragment,e),Qn=c(e),Ue=n(e,"H2",{class:!0});var Vi=i(Ue);Fe=n(Vi,"A",{id:!0,class:!0,href:!0});var Sm=i(Fe);eo=n(Sm,"SPAN",{});var Qm=i(eo);m(nl.$$.fragment,Qm),Qm.forEach(l),Sm.forEach(l),Nc=c(Vi),so=n(Vi,"SPAN",{});var Xm=i(so);Dc=a(Xm,"Schedulers"),Xm.forEach(l),Vi.forEach(l),Xn=c(e),Y=n(e,"P",{});var ys=i(Y);$c=a(ys,"Schedulers are loaded from the "),Fl=n(ys,"A",{href:!0});var xm=i(Fl);Bc=a(xm,"SchedulerMixin.from_pretrained()"),xm.forEach(l),Gc=a(ys," method, and unlike models, schedulers are "),lo=n(ys,"STRONG",{});var zm=i(lo);Ac=a(zm,"not parameterized"),zm.forEach(l),Sc=a(ys," or "),to=n(ys,"STRONG",{});var qm=i(to);Qc=a(qm,"trained"),qm.forEach(l),Xc=a(ys,"; they are defined by a configuration file."),ys.forEach(l),xn=c(e),He=n(e,"P",{});var Wi=i(He);xc=a(Wi,`Loading schedulers does not consume any significant amount of memory and the same configuration file can be used for a variety of different schedulers.
	For example, the following schedulers are compatible with `),Hl=n(Wi,"A",{href:!0});var Pm=i(Hl);zc=a(Pm,"StableDiffusionPipeline"),Pm.forEach(l),qc=a(Wi," which means you can load the same scheduler configuration file in any of these classes:"),Wi.forEach(l),zn=c(e),m(il.$$.fragment,e),qn=c(e),je=n(e,"H2",{class:!0});var Ri=i(je);Oe=n(Ri,"A",{id:!0,class:!0,href:!0});var Ym=i(Oe);ao=n(Ym,"SPAN",{});var Fm=i(ao);m(rl.$$.fragment,Fm),Fm.forEach(l),Ym.forEach(l),Pc=c(Ri),oo=n(Ri,"SPAN",{});var Hm=i(oo);Yc=a(Hm,"DiffusionPipeline explained"),Hm.forEach(l),Ri.forEach(l),Pn=c(e),Le=n(e,"P",{});var Ni=i(Le);Fc=a(Ni,"As a class method, "),Ol=n(Ni,"A",{href:!0});var Om=i(Ol);Hc=a(Om,"DiffusionPipeline.from_pretrained()"),Om.forEach(l),Oc=a(Ni," is responsible for two things:"),Ni.forEach(l),Yn=c(e),Ke=n(e,"UL",{});var Di=i(Ke);pl=n(Di,"LI",{});var $i=i(pl);Lc=a($i,"Download the latest version of the folder structure required for inference and cache it. If the latest folder structure is available in the local cache, "),Ll=n($i,"A",{href:!0});var Lm=i(Ll);Kc=a(Lm,"DiffusionPipeline.from_pretrained()"),Lm.forEach(l),ed=a($i," reuses the cache and won\u2019t redownload the files."),$i.forEach(l),sd=c(Di),Ze=n(Di,"LI",{});var Tt=i(Ze);ld=a(Tt,"Load the cached weights into the correct pipeline "),Kl=n(Tt,"A",{href:!0});var Km=i(Kl);td=a(Km,"class"),Km.forEach(l),ad=a(Tt," - retrieved from the "),no=n(Tt,"CODE",{});var ey=i(no);od=a(ey,"model_index.json"),ey.forEach(l),nd=a(Tt," file - and return an instance of it."),Tt.forEach(l),Di.forEach(l),Fn=c(e),ie=n(e,"P",{});var bt=i(ie);id=a(bt,"The pipelines underlying folder structure corresponds directly with their class instances. For example, the "),et=n(bt,"A",{href:!0});var sy=i(et);rd=a(sy,"StableDiffusionPipeline"),sy.forEach(l),pd=a(bt," corresponds to the folder structure in "),fl=n(bt,"A",{href:!0,rel:!0});var ly=i(fl);io=n(ly,"CODE",{});var ty=i(io);fd=a(ty,"runwayml/stable-diffusion-v1-5"),ty.forEach(l),ly.forEach(l),cd=a(bt,"."),bt.forEach(l),Hn=c(e),m(cl.$$.fragment,e),On=c(e),es=n(e,"P",{});var Bi=i(es);dd=a(Bi,"You\u2019ll see pipeline is an instance of "),st=n(Bi,"A",{href:!0});var ay=i(st);ud=a(ay,"StableDiffusionPipeline"),ay.forEach(l),hd=a(Bi,", which consists of seven components:"),Bi.forEach(l),Ln=c(e),k=n(e,"UL",{});var B=i(k);ss=n(B,"LI",{});var Zo=i(ss);ro=n(Zo,"CODE",{});var oy=i(ro);md=a(oy,'"feature_extractor"'),oy.forEach(l),yd=a(Zo,": a "),dl=n(Zo,"A",{href:!0,rel:!0});var ny=i(dl);Md=a(ny,"CLIPFeatureExtractor"),ny.forEach(l),Jd=a(Zo," from \u{1F917} Transformers."),Zo.forEach(l),wd=c(B),ls=n(B,"LI",{});var _o=i(ls);po=n(_o,"CODE",{});var 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