import{S as Fo,i as Xo,s as Ro,e as n,k as c,w as b,t as r,M as qo,c as s,d as a,m as p,a as i,x as y,h as l,b as d,G as e,g as m,y as w,q as M,o as D,B as T,v as Lo,L as Ut}from"../../../chunks/vendor-hf-doc-builder.js";import{D as J}from"../../../chunks/Docstring-hf-doc-builder.js";import{C as at}from"../../../chunks/CodeBlock-hf-doc-builder.js";import{I as ye}from"../../../chunks/IconCopyLink-hf-doc-builder.js";import{E as Zt}from"../../../chunks/ExampleCodeBlock-hf-doc-builder.js";function zo(k){let g,V,_,u,v;return u=new at({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFZlcnNhdGlsZURpZmZ1c2lvblBpcGVsaW5lJTBBaW1wb3J0JTIwdG9yY2glMEFpbXBvcnQlMjByZXF1ZXN0cyUwQWZyb20lMjBpbyUyMGltcG9ydCUyMEJ5dGVzSU8lMEFmcm9tJTIwUElMJTIwaW1wb3J0JTIwSW1hZ2UlMEElMEElMjMlMjBsZXQncyUyMGRvd25sb2FkJTIwYW4lMjBpbml0aWFsJTIwaW1hZ2UlMEF1cmwlMjAlM0QlMjAlMjJodHRwcyUzQSUyRiUyRmh1Z2dpbmdmYWNlLmNvJTJGZGF0YXNldHMlMkZkaWZmdXNlcnMlMkZpbWFnZXMlMkZyZXNvbHZlJTJGbWFpbiUyRmJlbnouanBnJTIyJTBBJTBBcmVzcG9uc2UlMjAlM0QlMjByZXF1ZXN0cy5nZXQodXJsKSUwQWltYWdlJTIwJTNEJTIwSW1hZ2Uub3BlbihCeXRlc0lPKHJlc3BvbnNlLmNvbnRlbnQpKS5jb252ZXJ0KCUyMlJHQiUyMiklMEF0ZXh0JTIwJTNEJTIwJTIyYSUyMHJlZCUyMGNhciUyMGluJTIwdGhlJTIwc3VuJTIyJTBBJTBBcGlwZSUyMCUzRCUyMFZlcnNhdGlsZURpZmZ1c2lvblBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJzaGktbGFicyUyRnZlcnNhdGlsZS1kaWZmdXNpb24lMjIlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYlMEEpJTBBcGlwZSUyMCUzRCUyMHBpcGUudG8oJTIyY3VkYSUyMiklMEElMEFnZW5lcmF0b3IlMjAlM0QlMjB0b3JjaC5HZW5lcmF0b3IoZGV2aWNlJTNEJTIyY3VkYSUyMikubWFudWFsX3NlZWQoMCklMEF0ZXh0X3RvX2ltYWdlX3N0cmVuZ3RoJTIwJTNEJTIwMC43NSUwQSUwQWltYWdlJTIwJTNEJTIwcGlwZS5kdWFsX2d1aWRlZCglMEElMjAlMjAlMjAlMjBwcm9tcHQlM0R0ZXh0JTJDJTIwaW1hZ2UlM0RpbWFnZSUyQyUyMHRleHRfdG9faW1hZ2Vfc3RyZW5ndGglM0R0ZXh0X3RvX2ltYWdlX3N0cmVuZ3RoJTJDJTIwZ2VuZXJhdG9yJTNEZ2VuZXJhdG9yJTBBKS5pbWFnZXMlNUIwJTVEJTBBaW1hZ2Uuc2F2ZSglMjIuJTJGY2FyX3ZhcmlhdGlvbi5wbmclMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> VersatileDiffusionPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># let&#x27;s download an initial image</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>response = requests.get(url)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;a red car in the sun&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = VersatileDiffusionPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;shi-labs/versatile-diffusion&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>generator = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">0</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>text_to_image_strength = <span class="hljs-number">0.75</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe.dual_guided(
<span class="hljs-meta">... </span>    prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator
<span class="hljs-meta">... </span>).images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;./car_variation.png&quot;</span>)`}}),{c(){g=n("p"),V=r("Examples:"),_=c(),b(u.$$.fragment)},l(o){g=s(o,"P",{});var h=i(g);V=l(h,"Examples:"),h.forEach(a),_=p(o),y(u.$$.fragment,o)},m(o,h){m(o,g,h),e(g,V),m(o,_,h),w(u,o,h),v=!0},p:Ut,i(o){v||(M(u.$$.fragment,o),v=!0)},o(o){D(u.$$.fragment,o),v=!1},d(o){o&&a(g),o&&a(_),T(u,o)}}}function Qo(k){let g,V,_,u,v;return u=new at({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> VersatileDiffusionPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># let&#x27;s download an initial image</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>response = requests.get(url)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = VersatileDiffusionPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;shi-labs/versatile-diffusion&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>generator = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">0</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe.image_variation(image, generator=generator).images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;./car_variation.png&quot;</span>)`}}),{c(){g=n("p"),V=r("Examples:"),_=c(),b(u.$$.fragment)},l(o){g=s(o,"P",{});var h=i(g);V=l(h,"Examples:"),h.forEach(a),_=p(o),y(u.$$.fragment,o)},m(o,h){m(o,g,h),e(g,V),m(o,_,h),w(u,o,h),v=!0},p:Ut,i(o){v||(M(u.$$.fragment,o),v=!0)},o(o){D(u.$$.fragment,o),v=!1},d(o){o&&a(g),o&&a(_),T(u,o)}}}function Ao(k){let g,V,_,u,v;return u=new at({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> VersatileDiffusionPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = VersatileDiffusionPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;shi-labs/versatile-diffusion&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>generator = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">0</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe.text_to_image(<span class="hljs-string">&quot;an astronaut riding on a horse on mars&quot;</span>, generator=generator).images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;./astronaut.png&quot;</span>)`}}),{c(){g=n("p"),V=r("Examples:"),_=c(),b(u.$$.fragment)},l(o){g=s(o,"P",{});var h=i(g);V=l(h,"Examples:"),h.forEach(a),_=p(o),y(u.$$.fragment,o)},m(o,h){m(o,g,h),e(g,V),m(o,_,h),w(u,o,h),v=!0},p:Ut,i(o){v||(M(u.$$.fragment,o),v=!0)},o(o){D(u.$$.fragment,o),v=!1},d(o){o&&a(g),o&&a(_),T(u,o)}}}function Yo(k){let g,V,_,u,v;return u=new at({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMFZlcnNhdGlsZURpZmZ1c2lvblRleHRUb0ltYWdlUGlwZWxpbmUlMEFpbXBvcnQlMjB0b3JjaCUwQSUwQXBpcGUlMjAlM0QlMjBWZXJzYXRpbGVEaWZmdXNpb25UZXh0VG9JbWFnZVBpcGVsaW5lLmZyb21fcHJldHJhaW5lZCglMEElMjAlMjAlMjAlMjAlMjJzaGktbGFicyUyRnZlcnNhdGlsZS1kaWZmdXNpb24lMjIlMkMlMjB0b3JjaF9kdHlwZSUzRHRvcmNoLmZsb2F0MTYlMEEpJTBBcGlwZS5yZW1vdmVfdW51c2VkX3dlaWdodHMoKSUwQXBpcGUlMjAlM0QlMjBwaXBlLnRvKCUyMmN1ZGElMjIpJTBBJTBBZ2VuZXJhdG9yJTIwJTNEJTIwdG9yY2guR2VuZXJhdG9yKGRldmljZSUzRCUyMmN1ZGElMjIpLm1hbnVhbF9zZWVkKDApJTBBaW1hZ2UlMjAlM0QlMjBwaXBlKCUyMmFuJTIwYXN0cm9uYXV0JTIwcmlkaW5nJTIwb24lMjBhJTIwaG9yc2UlMjBvbiUyMG1hcnMlMjIlMkMlMjBnZW5lcmF0b3IlM0RnZW5lcmF0b3IpLmltYWdlcyU1QjAlNUQlMEFpbWFnZS5zYXZlKCUyMi4lMkZhc3Ryb25hdXQucG5nJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> VersatileDiffusionTextToImagePipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = VersatileDiffusionTextToImagePipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;shi-labs/versatile-diffusion&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.remove_unused_weights()
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>generator = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">0</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe(<span class="hljs-string">&quot;an astronaut riding on a horse on mars&quot;</span>, generator=generator).images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;./astronaut.png&quot;</span>)`}}),{c(){g=n("p"),V=r("Examples:"),_=c(),b(u.$$.fragment)},l(o){g=s(o,"P",{});var h=i(g);V=l(h,"Examples:"),h.forEach(a),_=p(o),y(u.$$.fragment,o)},m(o,h){m(o,g,h),e(g,V),m(o,_,h),w(u,o,h),v=!0},p:Ut,i(o){v||(M(u.$$.fragment,o),v=!0)},o(o){D(u.$$.fragment,o),v=!1},d(o){o&&a(g),o&&a(_),T(u,o)}}}function Oo(k){let g,V,_,u,v;return u=new at({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> VersatileDiffusionImageVariationPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># let&#x27;s download an initial image</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>response = requests.get(url)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = VersatileDiffusionImageVariationPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;shi-labs/versatile-diffusion&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>generator = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">0</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe(image, generator=generator).images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;./car_variation.png&quot;</span>)`}}),{c(){g=n("p"),V=r("Examples:"),_=c(),b(u.$$.fragment)},l(o){g=s(o,"P",{});var h=i(g);V=l(h,"Examples:"),h.forEach(a),_=p(o),y(u.$$.fragment,o)},m(o,h){m(o,g,h),e(g,V),m(o,_,h),w(u,o,h),v=!0},p:Ut,i(o){v||(M(u.$$.fragment,o),v=!0)},o(o){D(u.$$.fragment,o),v=!1},d(o){o&&a(g),o&&a(_),T(u,o)}}}function Ho(k){let g,V,_,u,v;return u=new at({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> VersatileDiffusionDualGuidedPipeline
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> io <span class="hljs-keyword">import</span> BytesIO
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># let&#x27;s download an initial image</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;https://huggingface.co/datasets/diffusers/images/resolve/main/benz.jpg&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>response = requests.get(url)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(BytesIO(response.content)).convert(<span class="hljs-string">&quot;RGB&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>text = <span class="hljs-string">&quot;a red car in the sun&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained(
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;shi-labs/versatile-diffusion&quot;</span>, torch_dtype=torch.float16
<span class="hljs-meta">... </span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe.remove_unused_weights()
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe = pipe.to(<span class="hljs-string">&quot;cuda&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>generator = torch.Generator(device=<span class="hljs-string">&quot;cuda&quot;</span>).manual_seed(<span class="hljs-number">0</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>text_to_image_strength = <span class="hljs-number">0.75</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>image = pipe(
<span class="hljs-meta">... </span>    prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator
<span class="hljs-meta">... </span>).images[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image.save(<span class="hljs-string">&quot;./car_variation.png&quot;</span>)`}}),{c(){g=n("p"),V=r("Examples:"),_=c(),b(u.$$.fragment)},l(o){g=s(o,"P",{});var h=i(g);V=l(h,"Examples:"),h.forEach(a),_=p(o),y(u.$$.fragment,o)},m(o,h){m(o,g,h),e(g,V),m(o,_,h),w(u,o,h),v=!0},p:Ut,i(o){v||(M(u.$$.fragment,o),v=!0)},o(o){D(u.$$.fragment,o),v=!1},d(o){o&&a(g),o&&a(_),T(u,o)}}}function Ko(k){let g,V,_,u,v,o,h,$t,fn,ba,O,un,we,mn,gn,ya,nt,hn,wa,st,Jt,_n,Ma,X,H,Gt,Me,vn,Et,bn,Da,it,De,yn,ot,wn,Mn,Ta,R,K,St,Te,Dn,Wt,Tn,Va,j,Vn,rt,xn,In,lt,Pn,kn,dt,jn,Zn,ct,Un,xa,pt,Nt,$n,Ia,ft,Jn,Pa,E,Ve,Ct,Gn,En,ee,Sn,Bt,Wn,Nn,Cn,xe,Ft,Bn,Fn,te,Xn,Xt,Rn,qn,Ln,Ie,Rt,zn,Qn,ae,An,qt,Yn,On,ka,q,ne,Lt,Pe,Hn,zt,Kn,ja,x,es,ut,ts,as,Qt,ns,ss,mt,is,os,gt,rs,ls,ht,ds,cs,_t,ps,fs,vt,us,ms,At,gs,hs,Yt,_s,vs,bt,bs,ys,Za,ke,Ua,L,se,Ot,je,ws,Ht,Ms,$a,P,Ze,Ds,Kt,Ts,Vs,Ue,xs,yt,Is,Ps,ks,S,$e,js,ea,Zs,Us,ie,$s,W,Je,Js,ta,Gs,Es,oe,Ss,N,Ge,Ws,aa,Ns,Cs,re,Ja,z,le,na,Ee,Bs,sa,Fs,Ga,Z,Se,Xs,We,Rs,wt,qs,Ls,zs,C,Ne,Qs,ia,As,Ys,de,Os,ce,Ce,Hs,Be,Ks,oa,ei,ti,Ea,Q,pe,ra,Fe,ai,la,ni,Sa,U,Xe,si,Re,ii,Mt,oi,ri,li,B,qe,di,da,ci,pi,fe,fi,ue,Le,ui,ze,mi,ca,gi,hi,Wa,A,me,pa,Qe,_i,fa,vi,Na,$,Ae,bi,Ye,yi,Dt,wi,Mi,Di,F,Oe,Ti,ua,Vi,xi,ge,Ii,he,He,Pi,Ke,ki,ma,ji,Zi,Ca;return o=new ye({}),Me=new ye({}),Te=new ye({}),Pe=new ye({}),ke=new at({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> VersatileDiffusionPipeline, EulerDiscreteScheduler

<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = VersatileDiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;shi-labs/versatile-diffusion&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;shi-labs/versatile-diffusion&quot;</span>, subfolder=<span class="hljs-string">&quot;scheduler&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipeline = VersatileDiffusionPipeline.from_pretrained(<span class="hljs-string">&quot;shi-labs/versatile-diffusion&quot;</span>, scheduler=euler_scheduler)`}}),je=new ye({}),Ze=new J({props:{name:"class diffusers.VersatileDiffusionPipeline",anchor:"diffusers.VersatileDiffusionPipeline",parameters:[{name:"tokenizer",val:": CLIPTokenizer"},{name:"image_feature_extractor",val:": CLIPImageProcessor"},{name:"text_encoder",val:": CLIPTextModel"},{name:"image_encoder",val:": CLIPVisionModel"},{name:"image_unet",val:": UNet2DConditionModel"},{name:"text_unet",val:": UNet2DConditionModel"},{name:"vae",val:": AutoencoderKL"},{name:"scheduler",val:": KarrasDiffusionSchedulers"}],parametersDescription:[{anchor:"diffusers.VersatileDiffusionPipeline.vae",description:`<strong>vae</strong> (<a href="/docs/diffusers/v0.18.0/en/api/models/autoencoderkl#diffusers.AutoencoderKL">AutoencoderKL</a>) &#x2014;
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.`,name:"vae"},{anchor:"diffusers.VersatileDiffusionPipeline.text_encoder",description:`<strong>text_encoder</strong> (<code>CLIPTextModel</code>) &#x2014;
Frozen text-encoder. Stable Diffusion uses the text portion of
<a href="https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel" rel="nofollow">CLIP</a>, specifically
the <a href="https://huggingface.co/openai/clip-vit-large-patch14" rel="nofollow">clip-vit-large-patch14</a> variant.`,name:"text_encoder"},{anchor:"diffusers.VersatileDiffusionPipeline.tokenizer",description:`<strong>tokenizer</strong> (<code>CLIPTokenizer</code>) &#x2014;
Tokenizer of class
<a href="https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer" rel="nofollow">CLIPTokenizer</a>.`,name:"tokenizer"},{anchor:"diffusers.VersatileDiffusionPipeline.unet",description:'<strong>unet</strong> (<a href="/docs/diffusers/v0.18.0/en/api/models/unet2d-cond#diffusers.UNet2DConditionModel">UNet2DConditionModel</a>) &#x2014; Conditional U-Net architecture to denoise the encoded image latents.',name:"unet"},{anchor:"diffusers.VersatileDiffusionPipeline.scheduler",description:`<strong>scheduler</strong> (<a href="/docs/diffusers/v0.18.0/en/api/schedulers/overview#diffusers.SchedulerMixin">SchedulerMixin</a>) &#x2014;
A scheduler to be used in combination with <code>unet</code> to denoise the encoded image latents. Can be one of
<a href="/docs/diffusers/v0.18.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">DDIMScheduler</a>, <a href="/docs/diffusers/v0.18.0/en/api/schedulers/lms_discrete#diffusers.LMSDiscreteScheduler">LMSDiscreteScheduler</a>, or <a href="/docs/diffusers/v0.18.0/en/api/schedulers/pndm#diffusers.PNDMScheduler">PNDMScheduler</a>.`,name:"scheduler"},{anchor:"diffusers.VersatileDiffusionPipeline.safety_checker",description:`<strong>safety_checker</strong> (<code>StableDiffusionMegaSafetyChecker</code>) &#x2014;
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the <a href="https://huggingface.co/runwayml/stable-diffusion-v1-5" rel="nofollow">model card</a> for details.`,name:"safety_checker"},{anchor:"diffusers.VersatileDiffusionPipeline.feature_extractor",description:`<strong>feature_extractor</strong> (<code>CLIPImageProcessor</code>) &#x2014;
Model that extracts features from generated images to be used as inputs for the <code>safety_checker</code>.`,name:"feature_extractor"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion.py#L20"}}),$e=new J({props:{name:"dual_guided",anchor:"diffusers.VersatileDiffusionPipeline.dual_guided",parameters:[{name:"prompt",val:": typing.Union[PIL.Image.Image, typing.List[PIL.Image.Image]]"},{name:"image",val:": typing.Union[str, typing.List[str]]"},{name:"text_to_image_strength",val:": float = 0.5"},{name:"height",val:": typing.Optional[int] = None"},{name:"width",val:": typing.Optional[int] = None"},{name:"num_inference_steps",val:": int = 50"},{name:"guidance_scale",val:": float = 7.5"},{name:"num_images_per_prompt",val:": typing.Optional[int] = 1"},{name:"eta",val:": float = 0.0"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"callback",val:": typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None"},{name:"callback_steps",val:": int = 1"}],parametersDescription:[{anchor:"diffusers.VersatileDiffusionPipeline.dual_guided.prompt",description:`<strong>prompt</strong> (<code>str</code> or <code>List[str]</code>) &#x2014;
The prompt or prompts to guide the image generation.`,name:"prompt"},{anchor:"diffusers.VersatileDiffusionPipeline.dual_guided.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to self.image_unet.config.sample_size * self.vae_scale_factor) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.VersatileDiffusionPipeline.dual_guided.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to self.image_unet.config.sample_size * self.vae_scale_factor) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"diffusers.VersatileDiffusionPipeline.dual_guided.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 50) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.`,name:"num_inference_steps"},{anchor:"diffusers.VersatileDiffusionPipeline.dual_guided.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 7.5) &#x2014;
Guidance scale as defined in <a href="https://arxiv.org/abs/2207.12598" rel="nofollow">Classifier-Free Diffusion Guidance</a>.
<code>guidance_scale</code> is defined as <code>w</code> of equation 2. of <a href="https://arxiv.org/pdf/2205.11487.pdf" rel="nofollow">Imagen
Paper</a>. Guidance scale is enabled by setting <code>guidance_scale &gt; 1</code>. Higher guidance scale encourages to generate images that are closely linked to the text <code>prompt</code>,
usually at the expense of lower image quality.`,name:"guidance_scale"},{anchor:"diffusers.VersatileDiffusionPipeline.dual_guided.negative_prompt",description:`<strong>negative_prompt</strong> (<code>str</code> or <code>List[str]</code>, <em>optional</em>) &#x2014;
The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
if <code>guidance_scale</code> is less than <code>1</code>).`,name:"negative_prompt"},{anchor:"diffusers.VersatileDiffusionPipeline.dual_guided.num_images_per_prompt",description:`<strong>num_images_per_prompt</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The number of images to generate per prompt.`,name:"num_images_per_prompt"},{anchor:"diffusers.VersatileDiffusionPipeline.dual_guided.eta",description:`<strong>eta</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
Corresponds to parameter eta (&#x3B7;) in the DDIM paper: <a href="https://arxiv.org/abs/2010.02502" rel="nofollow">https://arxiv.org/abs/2010.02502</a>. Only applies to
<a href="/docs/diffusers/v0.18.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">schedulers.DDIMScheduler</a>, will be ignored for others.`,name:"eta"},{anchor:"diffusers.VersatileDiffusionPipeline.dual_guided.generator",description:`<strong>generator</strong> (<code>torch.Generator</code>, <em>optional</em>) &#x2014;
One or a list of <a href="https://pytorch.org/docs/stable/generated/torch.Generator.html" rel="nofollow">torch generator(s)</a>
to make generation deterministic.`,name:"generator"},{anchor:"diffusers.VersatileDiffusionPipeline.dual_guided.latents",description:`<strong>latents</strong> (<code>torch.FloatTensor</code>, <em>optional</em>) &#x2014;
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random <code>generator</code>.`,name:"latents"},{anchor:"diffusers.VersatileDiffusionPipeline.dual_guided.output_type",description:`<strong>output_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;pil&quot;</code>) &#x2014;
The output format of the generate image. Choose between
<a href="https://pillow.readthedocs.io/en/stable/" rel="nofollow">PIL</a>: <code>PIL.Image.Image</code> or <code>np.array</code>.`,name:"output_type"},{anchor:"diffusers.VersatileDiffusionPipeline.dual_guided.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to return a <a href="/docs/diffusers/v0.18.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput">StableDiffusionPipelineOutput</a> instead of a
plain tuple.`,name:"return_dict"},{anchor:"diffusers.VersatileDiffusionPipeline.dual_guided.callback",description:`<strong>callback</strong> (<code>Callable</code>, <em>optional</em>) &#x2014;
A function that will be called every <code>callback_steps</code> steps during inference. The function will be
called with the following arguments: <code>callback(step: int, timestep: int, latents: torch.FloatTensor)</code>.`,name:"callback"},{anchor:"diffusers.VersatileDiffusionPipeline.dual_guided.callback_steps",description:`<strong>callback_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
The frequency at which the <code>callback</code> function will be called. If not specified, the callback will be
called at every step.`,name:"callback_steps"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion.py#L310",returnDescription:`
<p><code>~pipelines.stable_diffusion.ImagePipelineOutput</code> if <code>return_dict</code> is True, otherwise a \`tuple. When
returning a tuple, the first element is a list with the generated images.</p>
`,returnType:`
<p><code>~pipelines.stable_diffusion.ImagePipelineOutput</code> or <code>tuple</code></p>
`}}),ie=new Zt({props:{anchor:"diffusers.VersatileDiffusionPipeline.dual_guided.example",$$slots:{default:[zo]},$$scope:{ctx:k}}}),Je=new J({props:{name:"image_variation",anchor:"diffusers.VersatileDiffusionPipeline.image_variation",parameters:[{name:"image",val:": typing.Union[torch.FloatTensor, PIL.Image.Image]"},{name:"height",val:": typing.Optional[int] = None"},{name:"width",val:": typing.Optional[int] = None"},{name:"num_inference_steps",val:": int = 50"},{name:"guidance_scale",val:": float = 7.5"},{name:"negative_prompt",val:": typing.Union[str, typing.List[str], NoneType] = None"},{name:"num_images_per_prompt",val:": typing.Optional[int] = 1"},{name:"eta",val:": float = 0.0"},{name:"generator",val:": typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None"},{name:"latents",val:": typing.Optional[torch.FloatTensor] = None"},{name:"output_type",val:": typing.Optional[str] = 'pil'"},{name:"return_dict",val:": bool = True"},{name:"callback",val:": typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None"},{name:"callback_steps",val:": int = 1"}],parametersDescription:[{anchor:"diffusers.VersatileDiffusionPipeline.image_variation.image",description:`<strong>image</strong> (<code>PIL.Image.Image</code>, <code>List[PIL.Image.Image]</code> or <code>torch.Tensor</code>) &#x2014;
The image prompt or prompts to guide the image generation.`,name:"image"},{anchor:"diffusers.VersatileDiffusionPipeline.image_variation.height",description:`<strong>height</strong> (<code>int</code>, <em>optional</em>, defaults to self.image_unet.config.sample_size * self.vae_scale_factor) &#x2014;
The height in pixels of the generated image.`,name:"height"},{anchor:"diffusers.VersatileDiffusionPipeline.image_variation.width",description:`<strong>width</strong> (<code>int</code>, <em>optional</em>, defaults to self.image_unet.config.sample_size * self.vae_scale_factor) &#x2014;
The width in pixels of the generated image.`,name:"width"},{anchor:"diffusers.VersatileDiffusionPipeline.image_variation.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 50) &#x2014;
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.`,name:"num_inference_steps"},{anchor:"diffusers.VersatileDiffusionPipeline.image_variation.guidance_scale",description:`<strong>guidance_scale</strong> (<code>float</code>, <em>optional</em>, defaults to 7.5) &#x2014;
Guidance scale as defined in <a href="https://arxiv.org/abs/2207.12598" rel="nofollow">Classifier-Free Diffusion Guidance</a>.
<code>guidance_scale</code> is defined as <code>w</code> of equation 2. of <a href="https://arxiv.org/pdf/2205.11487.pdf" rel="nofollow">Imagen
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`,returnType:`
<p><a
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<p><a
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`,returnType:`
<p><a
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The output format of the generate image. Choose between
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`,returnType:`
<p><a
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The width in pixels of the generated image.`,name:"width"},{anchor:"diffusers.VersatileDiffusionImageVariationPipeline.__call__.num_inference_steps",description:`<strong>num_inference_steps</strong> (<code>int</code>, <em>optional</em>, defaults to 50) &#x2014;
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Guidance scale as defined in <a href="https://arxiv.org/abs/2207.12598" rel="nofollow">Classifier-Free Diffusion Guidance</a>.
<code>guidance_scale</code> is defined as <code>w</code> of equation 2. of <a href="https://arxiv.org/pdf/2205.11487.pdf" rel="nofollow">Imagen
Paper</a>. Guidance scale is enabled by setting <code>guidance_scale &gt; 1</code>. Higher guidance scale encourages to generate images that are closely linked to the text <code>prompt</code>,
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The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
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<a href="/docs/diffusers/v0.18.0/en/api/schedulers/ddim#diffusers.DDIMScheduler">schedulers.DDIMScheduler</a>, will be ignored for others.`,name:"eta"},{anchor:"diffusers.VersatileDiffusionImageVariationPipeline.__call__.generator",description:`<strong>generator</strong> (<code>torch.Generator</code>, <em>optional</em>) &#x2014;
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Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
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The output format of the generate image. Choose between
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Whether or not to return a <a href="/docs/diffusers/v0.18.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput">StableDiffusionPipelineOutput</a> instead of a
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A function that will be called every <code>callback_steps</code> steps during inference. The function will be
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The frequency at which the <code>callback</code> function will be called. If not specified, the callback will be
called at every step.`,name:"callback_steps"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py#L266",returnDescription:`
<p><a
  href="/docs/diffusers/v0.18.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</a> if <code>return_dict</code> is True, otherwise a <code>tuple. When returning a tuple, the first element is a list with the generated images, and the second element is a list of </code>bool<code>s denoting whether the corresponding generated image likely represents "not-safe-for-work" (nsfw) content, according to the </code>safety_checker\`.</p>
`,returnType:`
<p><a
  href="/docs/diffusers/v0.18.0/en/api/pipelines/stable_diffusion/overview#diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput"
>StableDiffusionPipelineOutput</a> or <code>tuple</code></p>
`}}),fe=new Zt({props:{anchor:"diffusers.VersatileDiffusionImageVariationPipeline.__call__.example",$$slots:{default:[Oo]},$$scope:{ctx:k}}}),Le=new J({props:{name:"enable_sequential_cpu_offload",anchor:"diffusers.VersatileDiffusionImageVariationPipeline.enable_sequential_cpu_offload",parameters:[{name:"gpu_id",val:" = 0"}],source:"https://github.com/huggingface/diffusers/blob/v0.18.0/src/diffusers/pipelines/versatile_diffusion/pipeline_versatile_diffusion_image_variation.py#L78"}}),Qe=new ye({}),Ae=new J({props:{name:"class diffusers.VersatileDiffusionDualGuidedPipeline",anchor:"diffusers.VersatileDiffusionDualGuidedPipeline",parameters:[{name:"tokenizer",val:": CLIPTokenizer"},{name:"image_feature_extractor",val:": CLIPImageProcessor"},{name:"text_encoder",val:": CLIPTextModelWithProjection"},{name:"image_encoder",val:": CLIPVisionModelWithProjection"},{name:"image_unet",val:": UNet2DConditionModel"},{name:"text_unet",val:": UNetFlatConditionModel"},{name:"vae",val:": AutoencoderKL"},{name:"scheduler",val:": KarrasDiffusionSchedulers"}],parametersDescription:[{anchor:"diffusers.VersatileDiffusionDualGuidedPipeline.vqvae",description:`<strong>vqvae</strong> (<a href="/docs/diffusers/v0.18.0/en/api/models/vq#diffusers.VQModel">VQModel</a>) &#x2014;
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Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
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