import{S as jn,i as Nn,s as Vn,e as a,k as f,w as re,t as l,M as Bn,c as i,d as t,m as d,a as r,x as oe,h as s,b as n,N as xi,G as e,g as h,y as le,L as Hn,q as se,o as ne,B as fe,v as qn}from"../../../../chunks/vendor-hf-doc-builder.js";import{D as Un}from"../../../../chunks/Docstring-hf-doc-builder.js";import{C as Rn}from"../../../../chunks/CodeBlock-hf-doc-builder.js";import{I as Va}from"../../../../chunks/IconCopyLink-hf-doc-builder.js";function zn($l){let M,Ba,L,N,Ct,de,Ii,Gt,Pi,Ha,v,yi,Rt,Ai,$i,ue,ki,Mi,he,Li,Oi,ce,Ci,Gi,pe,Ri,ji,qa,$,Ni,me,Vi,Bi,Ye,Hi,qi,Ua,k,Ui,ge,zi,Fi,ve,Zi,Yi,za,_e,jt,Wi,Ji,Fa,We,Je,Ki,Ee,Ke,kl,Za,be,Nt,Qi,Xi,Ya,V,Vt,E,Bt,er,tr,Ht,ar,ir,Qe,rr,or,Xe,lr,sr,p,b,qt,et,nr,fr,Ut,zt,dr,ur,tt,De,at,Ml,hr,it,we,cr,pr,D,Ft,rt,mr,gr,Zt,Yt,vr,_r,ot,Se,lt,Ll,Er,st,Te,br,Dr,w,Wt,nt,wr,Sr,xe,Jt,Tr,xr,Kt,Ir,Pr,ft,Ie,dt,Ol,yr,ut,Ar,$r,S,Qt,ht,kr,Mr,Pe,Xt,Lr,Or,ea,Cr,Gr,ta,Rr,ct,jr,Nr,T,aa,pt,Vr,Br,ye,ia,Hr,qr,ra,Ur,zr,oa,Fr,mt,Ae,Zr,Yr,x,la,gt,Wr,Jr,$e,sa,Kr,Qr,na,Xr,eo,fa,to,vt,ao,io,I,da,_t,ro,oo,ke,ua,lo,so,ha,no,fo,ca,uo,Et,ho,co,P,pa,bt,po,mo,Me,ma,go,vo,ga,_o,Eo,va,bo,Dt,Le,Do,wo,y,_a,wt,So,To,Oe,Ea,xo,Io,ba,Po,yo,Da,Ao,St,Ce,$o,ko,A,wa,Tt,Mo,Lo,Ge,Sa,Oo,Co,Ta,Go,Ro,xa,jo,xt,Re,No,Wa,O,B,Ia,je,Vo,Pa,Bo,Ja,C,H,ya,Ne,Ho,Aa,qo,Ka,c,Uo,It,zo,Fo,$a,Zo,Yo,Pt,Wo,Jo,yt,Ko,Qo,At,Xo,el,$t,tl,al,kt,il,rl,ka,ol,ll,Ma,sl,nl,Mt,fl,dl,Qa,Ve,Xa,G,q,La,Be,ul,Oa,hl,ei,U,cl,Ca,pl,ml,ti,z,He,gl,qe,vl,_l,El,Ue,bl,Ga,Dl,wl,ai,ze,ii,R,F,Ra,Fe,Sl,ja,Tl,ri,j,Ze,xl,Na,Il,oi;return de=new Va({}),je=new Va({}),Ne=new Va({}),Ve=new Rn({props:{code:`from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler

pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
pipeline.scheduler = EulerDiscreteScheduler.from_config(pipeline.scheduler.config)

# or
euler_scheduler = EulerDiscreteScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler")
pipeline = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", scheduler=euler_scheduler)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> StableDiffusionPipeline, EulerDiscreteScheduler

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># or</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>euler_scheduler = EulerDiscreteScheduler.from_pretrained(<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>, subfolder=<span class="hljs-string">&quot;scheduler&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = StableDiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>, scheduler=euler_scheduler)`}}),Be=new Va({}),ze=new Rn({props:{code:`from diffusers import (
    StableDiffusionPipeline,
    StableDiffusionImg2ImgPipeline,
    StableDiffusionInpaintPipeline,
)

text2img = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
img2img = StableDiffusionImg2ImgPipeline(**text2img.components)
inpaint = StableDiffusionInpaintPipeline(**text2img.components)

# now you can use text2img(...), img2img(...), inpaint(...) just like the call methods of each respective pipeline`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> (
<span class="hljs-meta">... </span>    StableDiffusionPipeline,
<span class="hljs-meta">... </span>    StableDiffusionImg2ImgPipeline,
<span class="hljs-meta">... </span>    StableDiffusionInpaintPipeline,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>text2img = StableDiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;CompVis/stable-diffusion-v1-4&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>img2img = StableDiffusionImg2ImgPipeline(**text2img.components)
<span class="hljs-meta">&gt;&gt;&gt; </span>inpaint = StableDiffusionInpaintPipeline(**text2img.components)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># now you can use text2img(...), img2img(...), inpaint(...) just like the call methods of each respective pipeline</span>`}}),Fe=new Va({}),Ze=new Un({props:{name:"class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput",anchor:"diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput",parameters:[{name:"images",val:": typing.Union[typing.List[PIL.Image.Image], numpy.ndarray]"},{name:"nsfw_content_detected",val:": typing.Optional[typing.List[bool]]"}],parametersDescription:[{anchor:"diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput.images",description:`<strong>images</strong> (<code>List[PIL.Image.Image]</code> or <code>np.ndarray</code>) &#x2014;
List of denoised PIL images of length <code>batch_size</code> or numpy array of shape <code>(batch_size, height, width, num_channels)</code>. PIL images or numpy array present the denoised images of the diffusion pipeline.`,name:"images"},{anchor:"diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput.nsfw_content_detected",description:`<strong>nsfw_content_detected</strong> (<code>List[bool]</code>) &#x2014;
List of flags denoting whether the corresponding generated image likely represents &#x201C;not-safe-for-work&#x201D;
(nsfw) content, or <code>None</code> if safety checking could not be performed.`,name:"nsfw_content_detected"}],source:"https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/__init__.py#L22"}}),{c(){M=a("meta"),Ba=f(),L=a("h1"),N=a("a"),Ct=a("span"),re(de.$$.fragment),Ii=f(),Gt=a("span"),Pi=l("Stable diffusion pipelines"),Ha=f(),v=a("p"),yi=l("Stable Diffusion is a text-to-image "),Rt=a("em"),Ai=l("latent diffusion"),$i=l(" model created by the researchers and engineers from "),ue=a("a"),ki=l("CompVis"),Mi=l(", "),he=a("a"),Li=l("Stability AI"),Oi=l(" and "),ce=a("a"),Ci=l("LAION"),Gi=l(". It\u2019s trained on 512x512 images from a subset of the "),pe=a("a"),Ri=l("LAION-5B"),ji=l(" dataset. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. 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